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: . -mat_view [viewertype]:... - the viewer and its options
1019: Level: intermediate
1021: Note:
1022: .vb
1023: If no value is provided ascii:stdout is used
1024: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1025: for example ascii::ascii_info prints just the information about the object not all details
1026: unless :append is given filename opens in write mode, overwriting what was already there
1027: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
1028: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
1029: socket[:port] defaults to the standard output port
1030: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
1031: .ve
1033: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1034: @*/
1035: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1036: {
1037: PetscFunctionBegin;
1039: #if !defined(PETSC_HAVE_THREADSAFETY)
1040: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1041: #endif
1042: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1043: PetscFunctionReturn(PETSC_SUCCESS);
1044: }
1046: /*@
1047: MatView - display information about a matrix in a variety ways
1049: Collective on viewer
1051: Input Parameters:
1052: + mat - the matrix
1053: - viewer - visualization context
1055: Options Database Keys:
1056: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1057: . -mat_view ::ascii_info_detail - Prints more detailed info
1058: . -mat_view - Prints matrix in ASCII format
1059: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1060: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1061: . -display name - Sets display name (default is host)
1062: . -draw_pause sec - Sets number of seconds to pause after display
1063: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1064: . -viewer_socket_machine machine - -
1065: . -viewer_socket_port port - -
1066: . -mat_view binary - save matrix to file in binary format
1067: - -viewer_binary_filename name - -
1069: Level: beginner
1071: Notes:
1072: The available visualization contexts include
1073: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1074: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1075: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1076: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1078: The user can open alternative visualization contexts with
1079: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1080: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1081: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1082: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1084: The user can call `PetscViewerPushFormat()` to specify the output
1085: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1086: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1087: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1088: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1089: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1090: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1091: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1092: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1093: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1095: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1096: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1098: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1100: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1101: viewer is used.
1103: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1104: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1106: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1107: and then use the following mouse functions.
1108: .vb
1109: left mouse: zoom in
1110: middle mouse: zoom out
1111: right mouse: continue with the simulation
1112: .ve
1114: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1115: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1116: @*/
1117: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1118: {
1119: PetscInt rows, cols, rbs, cbs;
1120: PetscBool isascii, isstring, issaws;
1121: PetscViewerFormat format;
1122: PetscMPIInt size;
1124: PetscFunctionBegin;
1127: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1130: PetscCall(PetscViewerGetFormat(viewer, &format));
1131: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1132: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1134: #if !defined(PETSC_HAVE_THREADSAFETY)
1135: insidematview++;
1136: #endif
1137: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1138: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1139: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1140: 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");
1142: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1143: if (isascii) {
1144: if (!mat->preallocated) {
1145: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1146: #if !defined(PETSC_HAVE_THREADSAFETY)
1147: insidematview--;
1148: #endif
1149: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1150: PetscFunctionReturn(PETSC_SUCCESS);
1151: }
1152: if (!mat->assembled) {
1153: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1154: #if !defined(PETSC_HAVE_THREADSAFETY)
1155: insidematview--;
1156: #endif
1157: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1158: PetscFunctionReturn(PETSC_SUCCESS);
1159: }
1160: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1161: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1162: MatNullSpace nullsp, transnullsp;
1164: PetscCall(PetscViewerASCIIPushTab(viewer));
1165: PetscCall(MatGetSize(mat, &rows, &cols));
1166: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1167: if (rbs != 1 || cbs != 1) {
1168: 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" : ""));
1169: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1170: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1171: if (mat->factortype) {
1172: MatSolverType solver;
1173: PetscCall(MatFactorGetSolverType(mat, &solver));
1174: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1175: }
1176: if (mat->ops->getinfo) {
1177: PetscBool is_constant_or_diagonal;
1179: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1180: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1181: if (!is_constant_or_diagonal) {
1182: MatInfo info;
1184: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1185: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1186: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1187: }
1188: }
1189: PetscCall(MatGetNullSpace(mat, &nullsp));
1190: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1191: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1192: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1193: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1194: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1195: PetscCall(PetscViewerASCIIPushTab(viewer));
1196: PetscCall(MatProductView(mat, viewer));
1197: PetscCall(PetscViewerASCIIPopTab(viewer));
1198: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1199: IS tmp;
1201: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1202: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1203: PetscCall(PetscViewerASCIIPushTab(viewer));
1204: PetscCall(ISView(tmp, viewer));
1205: PetscCall(PetscViewerASCIIPopTab(viewer));
1206: PetscCall(ISDestroy(&tmp));
1207: }
1208: }
1209: } else if (issaws) {
1210: #if defined(PETSC_HAVE_SAWS)
1211: PetscMPIInt rank;
1213: PetscCall(PetscObjectName((PetscObject)mat));
1214: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1215: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1216: #endif
1217: } else if (isstring) {
1218: const char *type;
1219: PetscCall(MatGetType(mat, &type));
1220: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1221: PetscTryTypeMethod(mat, view, viewer);
1222: }
1223: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1224: PetscCall(PetscViewerASCIIPushTab(viewer));
1225: PetscUseTypeMethod(mat, viewnative, viewer);
1226: PetscCall(PetscViewerASCIIPopTab(viewer));
1227: } else if (mat->ops->view) {
1228: PetscCall(PetscViewerASCIIPushTab(viewer));
1229: PetscUseTypeMethod(mat, view, viewer);
1230: PetscCall(PetscViewerASCIIPopTab(viewer));
1231: }
1232: if (isascii) {
1233: PetscCall(PetscViewerGetFormat(viewer, &format));
1234: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1235: }
1236: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1237: #if !defined(PETSC_HAVE_THREADSAFETY)
1238: insidematview--;
1239: #endif
1240: PetscFunctionReturn(PETSC_SUCCESS);
1241: }
1243: #if defined(PETSC_USE_DEBUG)
1244: #include <../src/sys/totalview/tv_data_display.h>
1245: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1246: {
1247: TV_add_row("Local rows", "int", &mat->rmap->n);
1248: TV_add_row("Local columns", "int", &mat->cmap->n);
1249: TV_add_row("Global rows", "int", &mat->rmap->N);
1250: TV_add_row("Global columns", "int", &mat->cmap->N);
1251: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1252: return TV_format_OK;
1253: }
1254: #endif
1256: /*@
1257: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1258: with `MatView()`. The matrix format is determined from the options database.
1259: Generates a parallel MPI matrix if the communicator has more than one
1260: processor. The default matrix type is `MATAIJ`.
1262: Collective
1264: Input Parameters:
1265: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1266: or some related function before a call to `MatLoad()`
1267: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1269: Options Database Key:
1270: . -matload_block_size bs - set block size
1272: Level: beginner
1274: Notes:
1275: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1276: `Mat` before calling this routine if you wish to set it from the options database.
1278: `MatLoad()` automatically loads into the options database any options
1279: given in the file filename.info where filename is the name of the file
1280: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1281: file will be ignored if you use the -viewer_binary_skip_info option.
1283: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1284: sets the default matrix type AIJ and sets the local and global sizes.
1285: If type and/or size is already set, then the same are used.
1287: In parallel, each processor can load a subset of rows (or the
1288: entire matrix). This routine is especially useful when a large
1289: matrix is stored on disk and only part of it is desired on each
1290: processor. For example, a parallel solver may access only some of
1291: the rows from each processor. The algorithm used here reads
1292: relatively small blocks of data rather than reading the entire
1293: matrix and then subsetting it.
1295: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1296: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1297: or the sequence like
1298: .vb
1299: `PetscViewer` v;
1300: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1301: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1302: `PetscViewerSetFromOptions`(v);
1303: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1304: `PetscViewerFileSetName`(v,"datafile");
1305: .ve
1306: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1307: .vb
1308: -viewer_type {binary, hdf5}
1309: .ve
1311: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1312: and src/mat/tutorials/ex10.c with the second approach.
1314: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1315: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1316: Multiple objects, both matrices and vectors, can be stored within the same file.
1317: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1319: Most users should not need to know the details of the binary storage
1320: format, since `MatLoad()` and `MatView()` completely hide these details.
1321: But for anyone who is interested, the standard binary matrix storage
1322: format is
1324: .vb
1325: PetscInt MAT_FILE_CLASSID
1326: PetscInt number of rows
1327: PetscInt number of columns
1328: PetscInt total number of nonzeros
1329: PetscInt *number nonzeros in each row
1330: PetscInt *column indices of all nonzeros (starting index is zero)
1331: PetscScalar *values of all nonzeros
1332: .ve
1333: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1334: 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
1335: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1337: PETSc automatically does the byte swapping for
1338: machines that store the bytes reversed. Thus if you write your own binary
1339: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1340: and `PetscBinaryWrite()` to see how this may be done.
1342: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1343: Each processor's chunk is loaded independently by its owning MPI process.
1344: Multiple objects, both matrices and vectors, can be stored within the same file.
1345: They are looked up by their PetscObject name.
1347: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1348: by default the same structure and naming of the AIJ arrays and column count
1349: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1350: .vb
1351: save example.mat A b -v7.3
1352: .ve
1353: can be directly read by this routine (see Reference 1 for details).
1355: Depending on your MATLAB version, this format might be a default,
1356: otherwise you can set it as default in Preferences.
1358: Unless -nocompression flag is used to save the file in MATLAB,
1359: PETSc must be configured with ZLIB package.
1361: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1363: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1365: Corresponding `MatView()` is not yet implemented.
1367: The loaded matrix is actually a transpose of the original one in MATLAB,
1368: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1369: With this format, matrix is automatically transposed by PETSc,
1370: unless the matrix is marked as SPD or symmetric
1371: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1373: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1375: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1376: @*/
1377: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1378: {
1379: PetscBool flg;
1381: PetscFunctionBegin;
1385: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1387: flg = PETSC_FALSE;
1388: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1389: if (flg) {
1390: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1391: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1392: }
1393: flg = PETSC_FALSE;
1394: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1395: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1397: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1398: PetscUseTypeMethod(mat, load, viewer);
1399: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1400: PetscFunctionReturn(PETSC_SUCCESS);
1401: }
1403: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1404: {
1405: Mat_Redundant *redund = *redundant;
1407: PetscFunctionBegin;
1408: if (redund) {
1409: if (redund->matseq) { /* via MatCreateSubMatrices() */
1410: PetscCall(ISDestroy(&redund->isrow));
1411: PetscCall(ISDestroy(&redund->iscol));
1412: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1413: } else {
1414: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1415: PetscCall(PetscFree(redund->sbuf_j));
1416: PetscCall(PetscFree(redund->sbuf_a));
1417: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1418: PetscCall(PetscFree(redund->rbuf_j[i]));
1419: PetscCall(PetscFree(redund->rbuf_a[i]));
1420: }
1421: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1422: }
1424: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1425: PetscCall(PetscFree(redund));
1426: }
1427: PetscFunctionReturn(PETSC_SUCCESS);
1428: }
1430: /*@
1431: MatDestroy - Frees space taken by a matrix.
1433: Collective
1435: Input Parameter:
1436: . A - the matrix
1438: Level: beginner
1440: Developer Note:
1441: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1442: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1443: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1444: if changes are needed here.
1446: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1447: @*/
1448: PetscErrorCode MatDestroy(Mat *A)
1449: {
1450: PetscFunctionBegin;
1451: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1453: if (--((PetscObject)*A)->refct > 0) {
1454: *A = NULL;
1455: PetscFunctionReturn(PETSC_SUCCESS);
1456: }
1458: /* if memory was published with SAWs then destroy it */
1459: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1460: PetscTryTypeMethod(*A, destroy);
1462: PetscCall(PetscFree((*A)->factorprefix));
1463: PetscCall(PetscFree((*A)->defaultvectype));
1464: PetscCall(PetscFree((*A)->defaultrandtype));
1465: PetscCall(PetscFree((*A)->bsizes));
1466: PetscCall(PetscFree((*A)->solvertype));
1467: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1468: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1469: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1470: PetscCall(MatProductClear(*A));
1471: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1472: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1473: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1474: PetscCall(MatDestroy(&(*A)->schur));
1475: PetscCall(VecDestroy(&(*A)->dot_vec));
1476: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1477: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1478: PetscCall(PetscHeaderDestroy(A));
1479: PetscFunctionReturn(PETSC_SUCCESS);
1480: }
1482: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1483: /*@
1484: MatSetValues - Inserts or adds a block of values into a matrix.
1485: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1486: MUST be called after all calls to `MatSetValues()` have been completed.
1488: Not Collective
1490: Input Parameters:
1491: + mat - the matrix
1492: . m - the number of rows
1493: . idxm - the global indices of the rows
1494: . n - the number of columns
1495: . idxn - the global indices of the columns
1496: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1497: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1498: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1500: Level: beginner
1502: Notes:
1503: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1504: options cannot be mixed without intervening calls to the assembly
1505: routines.
1507: `MatSetValues()` uses 0-based row and column numbers in Fortran
1508: as well as in C.
1510: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1511: simply ignored. This allows easily inserting element stiffness matrices
1512: with homogeneous Dirichlet boundary conditions that you don't want represented
1513: in the matrix.
1515: Efficiency Alert:
1516: The routine `MatSetValuesBlocked()` may offer much better efficiency
1517: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1519: Fortran Notes:
1520: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1521: .vb
1522: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1523: .ve
1525: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1527: Developer Note:
1528: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1529: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1531: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1532: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1533: @*/
1534: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1535: {
1536: PetscFunctionBeginHot;
1539: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1540: PetscAssertPointer(idxm, 3);
1541: PetscAssertPointer(idxn, 5);
1542: MatCheckPreallocated(mat, 1);
1544: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1545: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1547: if (PetscDefined(USE_DEBUG)) {
1548: PetscInt i, j;
1550: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1551: if (v) {
1552: for (i = 0; i < m; i++) {
1553: for (j = 0; j < n; j++) {
1554: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1555: #if defined(PETSC_USE_COMPLEX)
1556: 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]);
1557: #else
1558: 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]);
1559: #endif
1560: }
1561: }
1562: }
1563: 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);
1564: 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);
1565: }
1567: if (mat->assembled) {
1568: mat->was_assembled = PETSC_TRUE;
1569: mat->assembled = PETSC_FALSE;
1570: }
1571: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1572: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1573: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1574: PetscFunctionReturn(PETSC_SUCCESS);
1575: }
1577: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1578: /*@
1579: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1580: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1581: MUST be called after all calls to `MatSetValues()` have been completed.
1583: Not Collective
1585: Input Parameters:
1586: + mat - the matrix
1587: . ism - the rows to provide
1588: . isn - the columns to provide
1589: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1590: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1591: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1593: Level: beginner
1595: Notes:
1596: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1598: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1599: options cannot be mixed without intervening calls to the assembly
1600: routines.
1602: `MatSetValues()` uses 0-based row and column numbers in Fortran
1603: as well as in C.
1605: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1606: simply ignored. This allows easily inserting element stiffness matrices
1607: with homogeneous Dirichlet boundary conditions that you don't want represented
1608: in the matrix.
1610: Fortran Note:
1611: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1613: Efficiency Alert:
1614: The routine `MatSetValuesBlocked()` may offer much better efficiency
1615: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1617: This is currently not optimized for any particular `ISType`
1619: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1620: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1621: @*/
1622: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1623: {
1624: PetscInt m, n;
1625: const PetscInt *rows, *cols;
1627: PetscFunctionBeginHot;
1629: PetscCall(ISGetIndices(ism, &rows));
1630: PetscCall(ISGetIndices(isn, &cols));
1631: PetscCall(ISGetLocalSize(ism, &m));
1632: PetscCall(ISGetLocalSize(isn, &n));
1633: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1634: PetscCall(ISRestoreIndices(ism, &rows));
1635: PetscCall(ISRestoreIndices(isn, &cols));
1636: PetscFunctionReturn(PETSC_SUCCESS);
1637: }
1639: /*@
1640: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1641: values into a matrix
1643: Not Collective
1645: Input Parameters:
1646: + mat - the matrix
1647: . row - the (block) row to set
1648: - 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.
1649: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1651: Level: intermediate
1653: Notes:
1654: The values, `v`, are column-oriented (for the block version) and sorted
1656: All the nonzero values in `row` must be provided
1658: The matrix must have previously had its column indices set, likely by having been assembled.
1660: `row` must belong to this MPI process
1662: Fortran Note:
1663: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1665: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1666: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1667: @*/
1668: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1669: {
1670: PetscInt globalrow;
1672: PetscFunctionBegin;
1675: PetscAssertPointer(v, 3);
1676: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1677: PetscCall(MatSetValuesRow(mat, globalrow, v));
1678: PetscFunctionReturn(PETSC_SUCCESS);
1679: }
1681: /*@
1682: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1683: values into a matrix
1685: Not Collective
1687: Input Parameters:
1688: + mat - the matrix
1689: . row - the (block) row to set
1690: - 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
1692: Level: advanced
1694: Notes:
1695: The values, `v`, are column-oriented for the block version.
1697: All the nonzeros in `row` must be provided
1699: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1701: `row` must belong to this process
1703: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1704: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1705: @*/
1706: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1707: {
1708: PetscFunctionBeginHot;
1711: MatCheckPreallocated(mat, 1);
1712: PetscAssertPointer(v, 3);
1713: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1714: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1715: mat->insertmode = INSERT_VALUES;
1717: if (mat->assembled) {
1718: mat->was_assembled = PETSC_TRUE;
1719: mat->assembled = PETSC_FALSE;
1720: }
1721: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1722: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1723: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1724: PetscFunctionReturn(PETSC_SUCCESS);
1725: }
1727: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1728: /*@
1729: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1730: Using structured grid indexing
1732: Not Collective
1734: Input Parameters:
1735: + mat - the matrix
1736: . m - number of rows being entered
1737: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1738: . n - number of columns being entered
1739: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1740: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1741: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1742: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1744: Level: beginner
1746: Notes:
1747: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1749: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1750: options cannot be mixed without intervening calls to the assembly
1751: routines.
1753: The grid coordinates are across the entire grid, not just the local portion
1755: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1756: as well as in C.
1758: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1760: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1761: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1763: The columns and rows in the stencil passed in MUST be contained within the
1764: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1765: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1766: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1767: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1769: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1770: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1771: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1772: `DM_BOUNDARY_PERIODIC` boundary type.
1774: 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
1775: a single value per point) you can skip filling those indices.
1777: Inspired by the structured grid interface to the HYPRE package
1778: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1780: Fortran Note:
1781: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1783: Efficiency Alert:
1784: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1785: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1787: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1788: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1789: @*/
1790: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1791: {
1792: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1793: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1794: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1796: PetscFunctionBegin;
1797: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1800: PetscAssertPointer(idxm, 3);
1801: PetscAssertPointer(idxn, 5);
1803: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1804: jdxm = buf;
1805: jdxn = buf + m;
1806: } else {
1807: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1808: jdxm = bufm;
1809: jdxn = bufn;
1810: }
1811: for (i = 0; i < m; i++) {
1812: for (j = 0; j < 3 - sdim; j++) dxm++;
1813: tmp = *dxm++ - starts[0];
1814: for (j = 0; j < dim - 1; j++) {
1815: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1816: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1817: }
1818: if (mat->stencil.noc) dxm++;
1819: jdxm[i] = tmp;
1820: }
1821: for (i = 0; i < n; i++) {
1822: for (j = 0; j < 3 - sdim; j++) dxn++;
1823: tmp = *dxn++ - starts[0];
1824: for (j = 0; j < dim - 1; j++) {
1825: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1826: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1827: }
1828: if (mat->stencil.noc) dxn++;
1829: jdxn[i] = tmp;
1830: }
1831: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1832: PetscCall(PetscFree2(bufm, bufn));
1833: PetscFunctionReturn(PETSC_SUCCESS);
1834: }
1836: /*@
1837: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1838: Using structured grid indexing
1840: Not Collective
1842: Input Parameters:
1843: + mat - the matrix
1844: . m - number of rows being entered
1845: . idxm - grid coordinates for matrix rows being entered
1846: . n - number of columns being entered
1847: . idxn - grid coordinates for matrix columns being entered
1848: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1849: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1850: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1852: Level: beginner
1854: Notes:
1855: By default the values, `v`, are row-oriented and unsorted.
1856: See `MatSetOption()` for other options.
1858: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1859: options cannot be mixed without intervening calls to the assembly
1860: routines.
1862: The grid coordinates are across the entire grid, not just the local portion
1864: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1865: as well as in C.
1867: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1869: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1870: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1872: The columns and rows in the stencil passed in MUST be contained within the
1873: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1874: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1875: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1876: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1878: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1879: simply ignored. This allows easily inserting element stiffness matrices
1880: with homogeneous Dirichlet boundary conditions that you don't want represented
1881: in the matrix.
1883: Inspired by the structured grid interface to the HYPRE package
1884: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1886: Fortran Notes:
1887: `idxm` and `idxn` should be declared as
1888: .vb
1889: MatStencil idxm(4,m),idxn(4,n)
1890: .ve
1891: and the values inserted using
1892: .vb
1893: idxm(MatStencil_i,1) = i
1894: idxm(MatStencil_j,1) = j
1895: idxm(MatStencil_k,1) = k
1896: etc
1897: .ve
1899: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1901: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1902: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1903: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1904: @*/
1905: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1906: {
1907: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1908: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1909: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1911: PetscFunctionBegin;
1912: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1915: PetscAssertPointer(idxm, 3);
1916: PetscAssertPointer(idxn, 5);
1917: PetscAssertPointer(v, 6);
1919: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1920: jdxm = buf;
1921: jdxn = buf + m;
1922: } else {
1923: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1924: jdxm = bufm;
1925: jdxn = bufn;
1926: }
1927: for (i = 0; i < m; i++) {
1928: for (j = 0; j < 3 - sdim; j++) dxm++;
1929: tmp = *dxm++ - starts[0];
1930: for (j = 0; j < sdim - 1; j++) {
1931: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1932: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1933: }
1934: dxm++;
1935: jdxm[i] = tmp;
1936: }
1937: for (i = 0; i < n; i++) {
1938: for (j = 0; j < 3 - sdim; j++) dxn++;
1939: tmp = *dxn++ - starts[0];
1940: for (j = 0; j < sdim - 1; j++) {
1941: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1942: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1943: }
1944: dxn++;
1945: jdxn[i] = tmp;
1946: }
1947: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1948: PetscCall(PetscFree2(bufm, bufn));
1949: PetscFunctionReturn(PETSC_SUCCESS);
1950: }
1952: /*@
1953: MatSetStencil - Sets the grid information for setting values into a matrix via
1954: `MatSetValuesStencil()`
1956: Not Collective
1958: Input Parameters:
1959: + mat - the matrix
1960: . dim - dimension of the grid 1, 2, or 3
1961: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1962: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1963: - dof - number of degrees of freedom per node
1965: Level: beginner
1967: Notes:
1968: Inspired by the structured grid interface to the HYPRE package
1969: (www.llnl.gov/CASC/hyper)
1971: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1972: user.
1974: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1975: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1976: @*/
1977: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1978: {
1979: PetscFunctionBegin;
1981: PetscAssertPointer(dims, 3);
1982: PetscAssertPointer(starts, 4);
1984: mat->stencil.dim = dim + (dof > 1);
1985: for (PetscInt i = 0; i < dim; i++) {
1986: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1987: mat->stencil.starts[i] = starts[dim - i - 1];
1988: }
1989: mat->stencil.dims[dim] = dof;
1990: mat->stencil.starts[dim] = 0;
1991: mat->stencil.noc = (PetscBool)(dof == 1);
1992: PetscFunctionReturn(PETSC_SUCCESS);
1993: }
1995: /*@
1996: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
1998: Not Collective
2000: Input Parameters:
2001: + mat - the matrix
2002: . m - the number of block rows
2003: . idxm - the global block indices
2004: . n - the number of block columns
2005: . idxn - the global block indices
2006: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2007: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2008: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2010: Level: intermediate
2012: Notes:
2013: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2014: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2016: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2017: NOT the total number of rows/columns; for example, if the block size is 2 and
2018: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2019: The values in `idxm` would be 1 2; that is the first index for each block divided by
2020: the block size.
2022: You must call `MatSetBlockSize()` when constructing this matrix (before
2023: preallocating it).
2025: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2027: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2028: options cannot be mixed without intervening calls to the assembly
2029: routines.
2031: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2032: as well as in C.
2034: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2035: simply ignored. This allows easily inserting element stiffness matrices
2036: with homogeneous Dirichlet boundary conditions that you don't want represented
2037: in the matrix.
2039: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2040: internal searching must be done to determine where to place the
2041: data in the matrix storage space. By instead inserting blocks of
2042: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2043: reduced.
2045: Example:
2046: .vb
2047: Suppose m=n=2 and block size(bs) = 2 The array is
2049: 1 2 | 3 4
2050: 5 6 | 7 8
2051: - - - | - - -
2052: 9 10 | 11 12
2053: 13 14 | 15 16
2055: v[] should be passed in like
2056: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2058: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2059: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2060: .ve
2062: Fortran Notes:
2063: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2064: .vb
2065: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2066: .ve
2068: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2070: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2071: @*/
2072: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2073: {
2074: PetscFunctionBeginHot;
2077: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2078: PetscAssertPointer(idxm, 3);
2079: PetscAssertPointer(idxn, 5);
2080: MatCheckPreallocated(mat, 1);
2081: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2082: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2083: if (PetscDefined(USE_DEBUG)) {
2084: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2085: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2086: }
2087: if (PetscDefined(USE_DEBUG)) {
2088: PetscInt rbs, cbs, M, N, i;
2089: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2090: PetscCall(MatGetSize(mat, &M, &N));
2091: 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);
2092: for (i = 0; i < n; i++)
2093: 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);
2094: }
2095: if (mat->assembled) {
2096: mat->was_assembled = PETSC_TRUE;
2097: mat->assembled = PETSC_FALSE;
2098: }
2099: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2100: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2101: else {
2102: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2103: PetscInt i, j, bs, cbs;
2105: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2106: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2107: iidxm = buf;
2108: iidxn = buf + m * bs;
2109: } else {
2110: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2111: iidxm = bufr;
2112: iidxn = bufc;
2113: }
2114: for (i = 0; i < m; i++) {
2115: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2116: }
2117: if (m != n || bs != cbs || idxm != idxn) {
2118: for (i = 0; i < n; i++) {
2119: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2120: }
2121: } else iidxn = iidxm;
2122: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2123: PetscCall(PetscFree2(bufr, bufc));
2124: }
2125: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2126: PetscFunctionReturn(PETSC_SUCCESS);
2127: }
2129: /*@
2130: MatGetValues - Gets a block of local values from a matrix.
2132: Not Collective; can only return values that are owned by the give process
2134: Input Parameters:
2135: + mat - the matrix
2136: . v - a logically two-dimensional array for storing the values
2137: . m - the number of rows
2138: . idxm - the global indices of the rows
2139: . n - the number of columns
2140: - idxn - the global indices of the columns
2142: Level: advanced
2144: Notes:
2145: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2146: The values, `v`, are then returned in a row-oriented format,
2147: analogous to that used by default in `MatSetValues()`.
2149: `MatGetValues()` uses 0-based row and column numbers in
2150: Fortran as well as in C.
2152: `MatGetValues()` requires that the matrix has been assembled
2153: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2154: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2155: without intermediate matrix assembly.
2157: Negative row or column indices will be ignored and those locations in `v` will be
2158: left unchanged.
2160: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2161: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2162: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2164: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2165: @*/
2166: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2167: {
2168: PetscFunctionBegin;
2171: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2172: PetscAssertPointer(idxm, 3);
2173: PetscAssertPointer(idxn, 5);
2174: PetscAssertPointer(v, 6);
2175: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2176: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2177: MatCheckPreallocated(mat, 1);
2179: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2180: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2181: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2182: PetscFunctionReturn(PETSC_SUCCESS);
2183: }
2185: /*@
2186: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2187: defined previously by `MatSetLocalToGlobalMapping()`
2189: Not Collective
2191: Input Parameters:
2192: + mat - the matrix
2193: . nrow - number of rows
2194: . irow - the row local indices
2195: . ncol - number of columns
2196: - icol - the column local indices
2198: Output Parameter:
2199: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2200: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2202: Level: advanced
2204: Notes:
2205: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2207: 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,
2208: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2209: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2210: with `MatSetLocalToGlobalMapping()`.
2212: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2213: `MatSetValuesLocal()`, `MatGetValues()`
2214: @*/
2215: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2216: {
2217: PetscFunctionBeginHot;
2220: MatCheckPreallocated(mat, 1);
2221: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2222: PetscAssertPointer(irow, 3);
2223: PetscAssertPointer(icol, 5);
2224: if (PetscDefined(USE_DEBUG)) {
2225: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2226: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2227: }
2228: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2229: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2230: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2231: else {
2232: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2233: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2234: irowm = buf;
2235: icolm = buf + nrow;
2236: } else {
2237: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2238: irowm = bufr;
2239: icolm = bufc;
2240: }
2241: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2242: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2243: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2244: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2245: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2246: PetscCall(PetscFree2(bufr, bufc));
2247: }
2248: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2249: PetscFunctionReturn(PETSC_SUCCESS);
2250: }
2252: /*@
2253: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2254: the same size. Currently, this can only be called once and creates the given matrix.
2256: Not Collective
2258: Input Parameters:
2259: + mat - the matrix
2260: . nb - the number of blocks
2261: . bs - the number of rows (and columns) in each block
2262: . rows - a concatenation of the rows for each block
2263: - v - a concatenation of logically two-dimensional arrays of values
2265: Level: advanced
2267: Notes:
2268: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2270: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2272: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2273: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2274: @*/
2275: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2276: {
2277: PetscFunctionBegin;
2280: PetscAssertPointer(rows, 4);
2281: PetscAssertPointer(v, 5);
2282: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2284: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2285: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2286: else {
2287: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2288: }
2289: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2290: PetscFunctionReturn(PETSC_SUCCESS);
2291: }
2293: /*@
2294: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2295: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2296: using a local (per-processor) numbering.
2298: Not Collective
2300: Input Parameters:
2301: + x - the matrix
2302: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2303: - cmapping - column mapping
2305: Level: intermediate
2307: Note:
2308: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2310: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2311: @*/
2312: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2313: {
2314: PetscFunctionBegin;
2319: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2320: else {
2321: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2322: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2323: }
2324: PetscFunctionReturn(PETSC_SUCCESS);
2325: }
2327: /*@
2328: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2330: Not Collective
2332: Input Parameter:
2333: . A - the matrix
2335: Output Parameters:
2336: + rmapping - row mapping
2337: - cmapping - column mapping
2339: Level: advanced
2341: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2342: @*/
2343: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2344: {
2345: PetscFunctionBegin;
2348: if (rmapping) {
2349: PetscAssertPointer(rmapping, 2);
2350: *rmapping = A->rmap->mapping;
2351: }
2352: if (cmapping) {
2353: PetscAssertPointer(cmapping, 3);
2354: *cmapping = A->cmap->mapping;
2355: }
2356: PetscFunctionReturn(PETSC_SUCCESS);
2357: }
2359: /*@
2360: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2362: Logically Collective
2364: Input Parameters:
2365: + A - the matrix
2366: . rmap - row layout
2367: - cmap - column layout
2369: Level: advanced
2371: Note:
2372: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2374: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2375: @*/
2376: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2377: {
2378: PetscFunctionBegin;
2380: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2381: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2382: PetscFunctionReturn(PETSC_SUCCESS);
2383: }
2385: /*@
2386: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2388: Not Collective
2390: Input Parameter:
2391: . A - the matrix
2393: Output Parameters:
2394: + rmap - row layout
2395: - cmap - column layout
2397: Level: advanced
2399: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2400: @*/
2401: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2402: {
2403: PetscFunctionBegin;
2406: if (rmap) {
2407: PetscAssertPointer(rmap, 2);
2408: *rmap = A->rmap;
2409: }
2410: if (cmap) {
2411: PetscAssertPointer(cmap, 3);
2412: *cmap = A->cmap;
2413: }
2414: PetscFunctionReturn(PETSC_SUCCESS);
2415: }
2417: /*@
2418: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2419: using a local numbering of the rows and columns.
2421: Not Collective
2423: Input Parameters:
2424: + mat - the matrix
2425: . nrow - number of rows
2426: . irow - the row local indices
2427: . ncol - number of columns
2428: . icol - the column local indices
2429: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2430: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2431: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2433: Level: intermediate
2435: Notes:
2436: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2438: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2439: options cannot be mixed without intervening calls to the assembly
2440: routines.
2442: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2443: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2445: Fortran Notes:
2446: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2447: .vb
2448: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2449: .ve
2451: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2453: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2454: `MatGetValuesLocal()`
2455: @*/
2456: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2457: {
2458: PetscFunctionBeginHot;
2461: MatCheckPreallocated(mat, 1);
2462: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2463: PetscAssertPointer(irow, 3);
2464: PetscAssertPointer(icol, 5);
2465: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2466: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2467: if (PetscDefined(USE_DEBUG)) {
2468: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2469: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2470: }
2472: if (mat->assembled) {
2473: mat->was_assembled = PETSC_TRUE;
2474: mat->assembled = PETSC_FALSE;
2475: }
2476: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2477: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2478: else {
2479: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2480: const PetscInt *irowm, *icolm;
2482: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2483: bufr = buf;
2484: bufc = buf + nrow;
2485: irowm = bufr;
2486: icolm = bufc;
2487: } else {
2488: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2489: irowm = bufr;
2490: icolm = bufc;
2491: }
2492: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2493: else irowm = irow;
2494: if (mat->cmap->mapping) {
2495: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2496: else icolm = irowm;
2497: } else icolm = icol;
2498: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2499: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2500: }
2501: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2502: PetscFunctionReturn(PETSC_SUCCESS);
2503: }
2505: /*@
2506: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2507: using a local ordering of the nodes a block at a time.
2509: Not Collective
2511: Input Parameters:
2512: + mat - the matrix
2513: . nrow - number of rows
2514: . irow - the row local indices
2515: . ncol - number of columns
2516: . icol - the column local indices
2517: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2518: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2519: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2521: Level: intermediate
2523: Notes:
2524: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2525: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2527: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2528: options cannot be mixed without intervening calls to the assembly
2529: routines.
2531: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2532: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2534: Fortran Notes:
2535: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2536: .vb
2537: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2538: .ve
2540: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2542: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2543: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2544: @*/
2545: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2546: {
2547: PetscFunctionBeginHot;
2550: MatCheckPreallocated(mat, 1);
2551: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2552: PetscAssertPointer(irow, 3);
2553: PetscAssertPointer(icol, 5);
2554: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2555: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2556: if (PetscDefined(USE_DEBUG)) {
2557: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2558: 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);
2559: }
2561: if (mat->assembled) {
2562: mat->was_assembled = PETSC_TRUE;
2563: mat->assembled = PETSC_FALSE;
2564: }
2565: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2566: PetscInt irbs, rbs;
2567: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2568: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2569: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2570: }
2571: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2572: PetscInt icbs, cbs;
2573: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2574: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2575: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2576: }
2577: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2578: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2579: else {
2580: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2581: const PetscInt *irowm, *icolm;
2583: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2584: bufr = buf;
2585: bufc = buf + nrow;
2586: irowm = bufr;
2587: icolm = bufc;
2588: } else {
2589: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2590: irowm = bufr;
2591: icolm = bufc;
2592: }
2593: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2594: else irowm = irow;
2595: if (mat->cmap->mapping) {
2596: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2597: else icolm = irowm;
2598: } else icolm = icol;
2599: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2600: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2601: }
2602: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2603: PetscFunctionReturn(PETSC_SUCCESS);
2604: }
2606: /*@
2607: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2609: Collective
2611: Input Parameters:
2612: + mat - the matrix
2613: - x - the vector to be multiplied
2615: Output Parameter:
2616: . y - the result
2618: Level: developer
2620: Note:
2621: The vectors `x` and `y` cannot be the same. I.e., one cannot
2622: call `MatMultDiagonalBlock`(A,y,y).
2624: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2625: @*/
2626: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2627: {
2628: PetscFunctionBegin;
2634: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2635: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2636: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2637: MatCheckPreallocated(mat, 1);
2639: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2640: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2641: PetscFunctionReturn(PETSC_SUCCESS);
2642: }
2644: /*@
2645: MatMult - Computes the matrix-vector product, $y = Ax$.
2647: Neighbor-wise Collective
2649: Input Parameters:
2650: + mat - the matrix
2651: - x - the vector to be multiplied
2653: Output Parameter:
2654: . y - the result
2656: Level: beginner
2658: Note:
2659: The vectors `x` and `y` cannot be the same. I.e., one cannot
2660: call `MatMult`(A,y,y).
2662: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2663: @*/
2664: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2665: {
2666: PetscFunctionBegin;
2670: VecCheckAssembled(x);
2672: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2673: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2674: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2675: 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);
2676: 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);
2677: 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);
2678: 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);
2679: PetscCall(VecSetErrorIfLocked(y, 3));
2680: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2681: MatCheckPreallocated(mat, 1);
2683: PetscCall(VecLockReadPush(x));
2684: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2685: PetscUseTypeMethod(mat, mult, x, y);
2686: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2687: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2688: PetscCall(VecLockReadPop(x));
2689: PetscFunctionReturn(PETSC_SUCCESS);
2690: }
2692: /*@
2693: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2695: Neighbor-wise Collective
2697: Input Parameters:
2698: + mat - the matrix
2699: - x - the vector to be multiplied
2701: Output Parameter:
2702: . y - the result
2704: Level: beginner
2706: Notes:
2707: The vectors `x` and `y` cannot be the same. I.e., one cannot
2708: call `MatMultTranspose`(A,y,y).
2710: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2711: use `MatMultHermitianTranspose()`
2713: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2714: @*/
2715: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2716: {
2717: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2719: PetscFunctionBegin;
2723: VecCheckAssembled(x);
2726: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2727: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2728: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2729: 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);
2730: 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);
2731: 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);
2732: 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);
2733: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2734: MatCheckPreallocated(mat, 1);
2736: if (!mat->ops->multtranspose) {
2737: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2738: 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);
2739: } else op = mat->ops->multtranspose;
2740: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2741: PetscCall(VecLockReadPush(x));
2742: PetscCall((*op)(mat, x, y));
2743: PetscCall(VecLockReadPop(x));
2744: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2745: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2746: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2747: PetscFunctionReturn(PETSC_SUCCESS);
2748: }
2750: /*@
2751: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2753: Neighbor-wise Collective
2755: Input Parameters:
2756: + mat - the matrix
2757: - x - the vector to be multiplied
2759: Output Parameter:
2760: . y - the result
2762: Level: beginner
2764: Notes:
2765: The vectors `x` and `y` cannot be the same. I.e., one cannot
2766: call `MatMultHermitianTranspose`(A,y,y).
2768: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2770: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2772: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2773: @*/
2774: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2775: {
2776: PetscFunctionBegin;
2782: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2783: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2784: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2785: 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);
2786: 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);
2787: 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);
2788: 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);
2789: MatCheckPreallocated(mat, 1);
2791: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2792: #if defined(PETSC_USE_COMPLEX)
2793: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2794: PetscCall(VecLockReadPush(x));
2795: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2796: else PetscUseTypeMethod(mat, mult, x, y);
2797: PetscCall(VecLockReadPop(x));
2798: } else {
2799: Vec w;
2800: PetscCall(VecDuplicate(x, &w));
2801: PetscCall(VecCopy(x, w));
2802: PetscCall(VecConjugate(w));
2803: PetscCall(MatMultTranspose(mat, w, y));
2804: PetscCall(VecDestroy(&w));
2805: PetscCall(VecConjugate(y));
2806: }
2807: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2808: #else
2809: PetscCall(MatMultTranspose(mat, x, y));
2810: #endif
2811: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2812: PetscFunctionReturn(PETSC_SUCCESS);
2813: }
2815: /*@
2816: MatMultAdd - Computes $v3 = v2 + A * v1$.
2818: Neighbor-wise Collective
2820: Input Parameters:
2821: + mat - the matrix
2822: . v1 - the vector to be multiplied by `mat`
2823: - v2 - the vector to be added to the result
2825: Output Parameter:
2826: . v3 - the result
2828: Level: beginner
2830: Note:
2831: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2832: call `MatMultAdd`(A,v1,v2,v1).
2834: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2835: @*/
2836: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2837: {
2838: PetscFunctionBegin;
2845: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2846: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2847: 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);
2848: /* 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);
2849: 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); */
2850: 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);
2851: 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);
2852: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2853: MatCheckPreallocated(mat, 1);
2855: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2856: PetscCall(VecLockReadPush(v1));
2857: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2858: PetscCall(VecLockReadPop(v1));
2859: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2860: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2861: PetscFunctionReturn(PETSC_SUCCESS);
2862: }
2864: /*@
2865: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2867: Neighbor-wise Collective
2869: Input Parameters:
2870: + mat - the matrix
2871: . v1 - the vector to be multiplied by the transpose of the matrix
2872: - v2 - the vector to be added to the result
2874: Output Parameter:
2875: . v3 - the result
2877: Level: beginner
2879: Note:
2880: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2881: call `MatMultTransposeAdd`(A,v1,v2,v1).
2883: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2884: @*/
2885: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2886: {
2887: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2889: PetscFunctionBegin;
2896: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2897: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2898: 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);
2899: 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);
2900: 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);
2901: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2902: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2903: MatCheckPreallocated(mat, 1);
2905: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2906: PetscCall(VecLockReadPush(v1));
2907: PetscCall((*op)(mat, v1, v2, v3));
2908: PetscCall(VecLockReadPop(v1));
2909: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2910: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2911: PetscFunctionReturn(PETSC_SUCCESS);
2912: }
2914: /*@
2915: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2917: Neighbor-wise Collective
2919: Input Parameters:
2920: + mat - the matrix
2921: . v1 - the vector to be multiplied by the Hermitian transpose
2922: - v2 - the vector to be added to the result
2924: Output Parameter:
2925: . v3 - the result
2927: Level: beginner
2929: Note:
2930: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2931: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2933: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2934: @*/
2935: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2936: {
2937: PetscFunctionBegin;
2944: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2945: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2946: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2947: 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);
2948: 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);
2949: 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);
2950: MatCheckPreallocated(mat, 1);
2952: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2953: PetscCall(VecLockReadPush(v1));
2954: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2955: else {
2956: Vec w, z;
2957: PetscCall(VecDuplicate(v1, &w));
2958: PetscCall(VecCopy(v1, w));
2959: PetscCall(VecConjugate(w));
2960: PetscCall(VecDuplicate(v3, &z));
2961: PetscCall(MatMultTranspose(mat, w, z));
2962: PetscCall(VecDestroy(&w));
2963: PetscCall(VecConjugate(z));
2964: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
2965: else PetscCall(VecAXPY(v3, 1.0, z));
2966: PetscCall(VecDestroy(&z));
2967: }
2968: PetscCall(VecLockReadPop(v1));
2969: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2970: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2971: PetscFunctionReturn(PETSC_SUCCESS);
2972: }
2974: PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
2975: {
2976: PetscFunctionBegin;
2977: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, &mat->dot_vec, NULL));
2978: PetscCall(MatMult(mat, x, mat->dot_vec));
2979: PetscCall(VecDot(mat->dot_vec, y, val));
2980: PetscFunctionReturn(PETSC_SUCCESS);
2981: }
2983: PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
2984: {
2985: PetscScalar sval;
2987: PetscFunctionBegin;
2988: PetscCall(MatADot_Default(mat, x, x, &sval));
2989: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
2990: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) < 100 * PETSC_MACHINE_EPSILON, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
2991: *val = PetscSqrtReal(PetscRealPart(sval));
2992: PetscFunctionReturn(PETSC_SUCCESS);
2993: }
2995: /*@
2996: 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)
2997: positive definite.
2999: Collective
3001: Input Parameters:
3002: + mat - matrix used to define the inner product
3003: . x - first vector
3004: - y - second vector
3006: Output Parameter:
3007: . val - the dot product with respect to `A`
3009: Level: intermediate
3011: Note:
3012: For complex vectors, `MatADot()` computes
3013: $$
3014: val = (x,y)_A = y^H A x,
3015: $$
3016: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3017: inner product where the SECOND argument gets the complex conjugate.
3019: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3020: @*/
3021: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3022: {
3023: PetscFunctionBegin;
3027: VecCheckAssembled(x);
3029: VecCheckAssembled(y);
3032: PetscAssertPointer(val, 4);
3033: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3034: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3035: 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);
3036: 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);
3037: 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);
3038: 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);
3039: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3040: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3041: MatCheckPreallocated(mat, 1);
3043: PetscCall(VecLockReadPush(x));
3044: PetscCall(VecLockReadPush(y));
3045: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3046: PetscUseTypeMethod(mat, adot, x, y, val);
3047: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3048: PetscCall(VecLockReadPop(y));
3049: PetscCall(VecLockReadPop(x));
3050: PetscFunctionReturn(PETSC_SUCCESS);
3051: }
3053: /*@
3054: 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)
3055: positive definite.
3057: Collective
3059: Input Parameters:
3060: + mat - matrix used to define norm
3061: - x - the vector to compute the norm of
3063: Output Parameter:
3064: . val - the norm with respect to `A`
3066: Level: intermediate
3068: Note:
3069: For complex vectors, `MatANorm()` computes
3070: $$
3071: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3072: $$
3073: where $x^H$ denotes the conjugate transpose of `x`.
3075: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3076: @*/
3077: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3078: {
3079: PetscFunctionBegin;
3083: VecCheckAssembled(x);
3085: PetscAssertPointer(val, 3);
3086: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3087: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3088: 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);
3089: 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);
3090: 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);
3091: 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);
3092: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3093: MatCheckPreallocated(mat, 1);
3095: PetscCall(VecLockReadPush(x));
3096: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3097: PetscUseTypeMethod(mat, anorm, x, val);
3098: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3099: PetscCall(VecLockReadPop(x));
3100: PetscFunctionReturn(PETSC_SUCCESS);
3101: }
3103: /*@
3104: MatGetFactorType - gets the type of factorization a matrix is
3106: Not Collective
3108: Input Parameter:
3109: . mat - the matrix
3111: Output Parameter:
3112: . 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`
3114: Level: intermediate
3116: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3117: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3118: @*/
3119: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3120: {
3121: PetscFunctionBegin;
3124: PetscAssertPointer(t, 2);
3125: *t = mat->factortype;
3126: PetscFunctionReturn(PETSC_SUCCESS);
3127: }
3129: /*@
3130: MatSetFactorType - sets the type of factorization a matrix is
3132: Logically Collective
3134: Input Parameters:
3135: + mat - the matrix
3136: - 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`
3138: Level: intermediate
3140: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3141: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3142: @*/
3143: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3144: {
3145: PetscFunctionBegin;
3148: mat->factortype = t;
3149: PetscFunctionReturn(PETSC_SUCCESS);
3150: }
3152: /*@
3153: MatGetInfo - Returns information about matrix storage (number of
3154: nonzeros, memory, etc.).
3156: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3158: Input Parameters:
3159: + mat - the matrix
3160: - 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)
3162: Output Parameter:
3163: . info - matrix information context
3165: Options Database Key:
3166: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3168: Level: intermediate
3170: Notes:
3171: The `MatInfo` context contains a variety of matrix data, including
3172: number of nonzeros allocated and used, number of mallocs during
3173: matrix assembly, etc. Additional information for factored matrices
3174: is provided (such as the fill ratio, number of mallocs during
3175: factorization, etc.).
3177: Example:
3178: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3179: data within the `MatInfo` context. For example,
3180: .vb
3181: MatInfo info;
3182: Mat A;
3183: double mal, nz_a, nz_u;
3185: MatGetInfo(A, MAT_LOCAL, &info);
3186: mal = info.mallocs;
3187: nz_a = info.nz_allocated;
3188: .ve
3190: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3191: @*/
3192: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3193: {
3194: PetscFunctionBegin;
3197: PetscAssertPointer(info, 3);
3198: MatCheckPreallocated(mat, 1);
3199: PetscUseTypeMethod(mat, getinfo, flag, info);
3200: PetscFunctionReturn(PETSC_SUCCESS);
3201: }
3203: /*
3204: This is used by external packages where it is not easy to get the info from the actual
3205: matrix factorization.
3206: */
3207: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3208: {
3209: PetscFunctionBegin;
3210: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3211: PetscFunctionReturn(PETSC_SUCCESS);
3212: }
3214: /*@
3215: MatLUFactor - Performs in-place LU factorization of matrix.
3217: Collective
3219: Input Parameters:
3220: + mat - the matrix
3221: . row - row permutation
3222: . col - column permutation
3223: - info - options for factorization, includes
3224: .vb
3225: fill - expected fill as ratio of original fill.
3226: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3227: Run with the option -info to determine an optimal value to use
3228: .ve
3230: Level: developer
3232: Notes:
3233: Most users should employ the `KSP` interface for linear solvers
3234: instead of working directly with matrix algebra routines such as this.
3235: See, e.g., `KSPCreate()`.
3237: This changes the state of the matrix to a factored matrix; it cannot be used
3238: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3240: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3241: when not using `KSP`.
3243: Fortran Note:
3244: A valid (non-null) `info` argument must be provided
3246: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3247: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3248: @*/
3249: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3250: {
3251: MatFactorInfo tinfo;
3253: PetscFunctionBegin;
3257: if (info) PetscAssertPointer(info, 4);
3259: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3260: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3261: MatCheckPreallocated(mat, 1);
3262: if (!info) {
3263: PetscCall(MatFactorInfoInitialize(&tinfo));
3264: info = &tinfo;
3265: }
3267: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3268: PetscUseTypeMethod(mat, lufactor, row, col, info);
3269: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3270: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3271: PetscFunctionReturn(PETSC_SUCCESS);
3272: }
3274: /*@
3275: MatILUFactor - Performs in-place ILU factorization of matrix.
3277: Collective
3279: Input Parameters:
3280: + mat - the matrix
3281: . row - row permutation
3282: . col - column permutation
3283: - info - structure containing
3284: .vb
3285: levels - number of levels of fill.
3286: expected fill - as ratio of original fill.
3287: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3288: missing diagonal entries)
3289: .ve
3291: Level: developer
3293: Notes:
3294: Most users should employ the `KSP` interface for linear solvers
3295: instead of working directly with matrix algebra routines such as this.
3296: See, e.g., `KSPCreate()`.
3298: Probably really in-place only when level of fill is zero, otherwise allocates
3299: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3300: when not using `KSP`.
3302: Fortran Note:
3303: A valid (non-null) `info` argument must be provided
3305: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3306: @*/
3307: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3308: {
3309: PetscFunctionBegin;
3313: PetscAssertPointer(info, 4);
3315: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3316: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3317: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3318: MatCheckPreallocated(mat, 1);
3320: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3321: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3322: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3323: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3324: PetscFunctionReturn(PETSC_SUCCESS);
3325: }
3327: /*@
3328: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3329: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3331: Collective
3333: Input Parameters:
3334: + fact - the factor matrix obtained with `MatGetFactor()`
3335: . mat - the matrix
3336: . row - the row permutation
3337: . col - the column permutation
3338: - info - options for factorization, includes
3339: .vb
3340: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3341: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3342: .ve
3344: Level: developer
3346: Notes:
3347: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3349: Most users should employ the simplified `KSP` interface for linear solvers
3350: instead of working directly with matrix algebra routines such as this.
3351: See, e.g., `KSPCreate()`.
3353: Fortran Note:
3354: A valid (non-null) `info` argument must be provided
3356: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3357: @*/
3358: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3359: {
3360: MatFactorInfo tinfo;
3362: PetscFunctionBegin;
3367: if (info) PetscAssertPointer(info, 5);
3370: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3371: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3372: MatCheckPreallocated(mat, 2);
3373: if (!info) {
3374: PetscCall(MatFactorInfoInitialize(&tinfo));
3375: info = &tinfo;
3376: }
3378: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3379: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3380: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3381: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3382: PetscFunctionReturn(PETSC_SUCCESS);
3383: }
3385: /*@
3386: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3387: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3389: Collective
3391: Input Parameters:
3392: + fact - the factor matrix obtained with `MatGetFactor()`
3393: . mat - the matrix
3394: - info - options for factorization
3396: Level: developer
3398: Notes:
3399: See `MatLUFactor()` for in-place factorization. See
3400: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3402: Most users should employ the `KSP` interface for linear solvers
3403: instead of working directly with matrix algebra routines such as this.
3404: See, e.g., `KSPCreate()`.
3406: Fortran Note:
3407: A valid (non-null) `info` argument must be provided
3409: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3410: @*/
3411: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3412: {
3413: MatFactorInfo tinfo;
3415: PetscFunctionBegin;
3420: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3421: 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,
3422: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3424: MatCheckPreallocated(mat, 2);
3425: if (!info) {
3426: PetscCall(MatFactorInfoInitialize(&tinfo));
3427: info = &tinfo;
3428: }
3430: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3431: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3432: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3433: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3434: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3435: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3436: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3437: PetscFunctionReturn(PETSC_SUCCESS);
3438: }
3440: /*@
3441: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3442: symmetric matrix.
3444: Collective
3446: Input Parameters:
3447: + mat - the matrix
3448: . perm - row and column permutations
3449: - info - expected fill as ratio of original fill
3451: Level: developer
3453: Notes:
3454: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3455: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3457: Most users should employ the `KSP` interface for linear solvers
3458: instead of working directly with matrix algebra routines such as this.
3459: See, e.g., `KSPCreate()`.
3461: Fortran Note:
3462: A valid (non-null) `info` argument must be provided
3464: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3465: `MatGetOrdering()`
3466: @*/
3467: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3468: {
3469: MatFactorInfo tinfo;
3471: PetscFunctionBegin;
3474: if (info) PetscAssertPointer(info, 3);
3476: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3477: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3478: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3479: MatCheckPreallocated(mat, 1);
3480: if (!info) {
3481: PetscCall(MatFactorInfoInitialize(&tinfo));
3482: info = &tinfo;
3483: }
3485: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3486: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3487: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3488: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3489: PetscFunctionReturn(PETSC_SUCCESS);
3490: }
3492: /*@
3493: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3494: of a symmetric matrix.
3496: Collective
3498: Input Parameters:
3499: + fact - the factor matrix obtained with `MatGetFactor()`
3500: . mat - the matrix
3501: . perm - row and column permutations
3502: - info - options for factorization, includes
3503: .vb
3504: fill - expected fill as ratio of original fill.
3505: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3506: Run with the option -info to determine an optimal value to use
3507: .ve
3509: Level: developer
3511: Notes:
3512: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3513: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3515: Most users should employ the `KSP` interface for linear solvers
3516: instead of working directly with matrix algebra routines such as this.
3517: See, e.g., `KSPCreate()`.
3519: Fortran Note:
3520: A valid (non-null) `info` argument must be provided
3522: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3523: `MatGetOrdering()`
3524: @*/
3525: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3526: {
3527: MatFactorInfo tinfo;
3529: PetscFunctionBegin;
3533: if (info) PetscAssertPointer(info, 4);
3536: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3537: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3538: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3539: MatCheckPreallocated(mat, 2);
3540: if (!info) {
3541: PetscCall(MatFactorInfoInitialize(&tinfo));
3542: info = &tinfo;
3543: }
3545: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3546: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3547: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3548: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3549: PetscFunctionReturn(PETSC_SUCCESS);
3550: }
3552: /*@
3553: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3554: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3555: `MatCholeskyFactorSymbolic()`.
3557: Collective
3559: Input Parameters:
3560: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3561: . mat - the initial matrix that is to be factored
3562: - info - options for factorization
3564: Level: developer
3566: Note:
3567: Most users should employ the `KSP` interface for linear solvers
3568: instead of working directly with matrix algebra routines such as this.
3569: See, e.g., `KSPCreate()`.
3571: Fortran Note:
3572: A valid (non-null) `info` argument must be provided
3574: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3575: @*/
3576: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3577: {
3578: MatFactorInfo tinfo;
3580: PetscFunctionBegin;
3585: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3586: 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,
3587: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3588: MatCheckPreallocated(mat, 2);
3589: if (!info) {
3590: PetscCall(MatFactorInfoInitialize(&tinfo));
3591: info = &tinfo;
3592: }
3594: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3595: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3596: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3597: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3598: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3599: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3600: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3601: PetscFunctionReturn(PETSC_SUCCESS);
3602: }
3604: /*@
3605: MatQRFactor - Performs in-place QR factorization of matrix.
3607: Collective
3609: Input Parameters:
3610: + mat - the matrix
3611: . col - column permutation
3612: - info - options for factorization, includes
3613: .vb
3614: fill - expected fill as ratio of original fill.
3615: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3616: Run with the option -info to determine an optimal value to use
3617: .ve
3619: Level: developer
3621: Notes:
3622: Most users should employ the `KSP` interface for linear solvers
3623: instead of working directly with matrix algebra routines such as this.
3624: See, e.g., `KSPCreate()`.
3626: This changes the state of the matrix to a factored matrix; it cannot be used
3627: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3629: Fortran Note:
3630: A valid (non-null) `info` argument must be provided
3632: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3633: `MatSetUnfactored()`
3634: @*/
3635: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3636: {
3637: PetscFunctionBegin;
3640: if (info) PetscAssertPointer(info, 3);
3642: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3643: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3644: MatCheckPreallocated(mat, 1);
3645: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3646: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3647: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3648: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3649: PetscFunctionReturn(PETSC_SUCCESS);
3650: }
3652: /*@
3653: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3654: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3656: Collective
3658: Input Parameters:
3659: + fact - the factor matrix obtained with `MatGetFactor()`
3660: . mat - the matrix
3661: . col - column permutation
3662: - info - options for factorization, includes
3663: .vb
3664: fill - expected fill as ratio of original fill.
3665: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3666: Run with the option -info to determine an optimal value to use
3667: .ve
3669: Level: developer
3671: Note:
3672: Most users should employ the `KSP` interface for linear solvers
3673: instead of working directly with matrix algebra routines such as this.
3674: See, e.g., `KSPCreate()`.
3676: Fortran Note:
3677: A valid (non-null) `info` argument must be provided
3679: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3680: @*/
3681: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3682: {
3683: MatFactorInfo tinfo;
3685: PetscFunctionBegin;
3689: if (info) PetscAssertPointer(info, 4);
3692: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3693: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3694: MatCheckPreallocated(mat, 2);
3695: if (!info) {
3696: PetscCall(MatFactorInfoInitialize(&tinfo));
3697: info = &tinfo;
3698: }
3700: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3701: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3702: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3703: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3704: PetscFunctionReturn(PETSC_SUCCESS);
3705: }
3707: /*@
3708: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3709: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3711: Collective
3713: Input Parameters:
3714: + fact - the factor matrix obtained with `MatGetFactor()`
3715: . mat - the matrix
3716: - info - options for factorization
3718: Level: developer
3720: Notes:
3721: See `MatQRFactor()` for in-place factorization.
3723: Most users should employ the `KSP` interface for linear solvers
3724: instead of working directly with matrix algebra routines such as this.
3725: See, e.g., `KSPCreate()`.
3727: Fortran Note:
3728: A valid (non-null) `info` argument must be provided
3730: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3731: @*/
3732: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3733: {
3734: MatFactorInfo tinfo;
3736: PetscFunctionBegin;
3741: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3742: 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,
3743: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3745: MatCheckPreallocated(mat, 2);
3746: if (!info) {
3747: PetscCall(MatFactorInfoInitialize(&tinfo));
3748: info = &tinfo;
3749: }
3751: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3752: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3753: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3754: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3755: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3756: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3757: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3758: PetscFunctionReturn(PETSC_SUCCESS);
3759: }
3761: /*@
3762: MatSolve - Solves $A x = b$, given a factored matrix.
3764: Neighbor-wise Collective
3766: Input Parameters:
3767: + mat - the factored matrix
3768: - b - the right-hand-side vector
3770: Output Parameter:
3771: . x - the result vector
3773: Level: developer
3775: Notes:
3776: The vectors `b` and `x` cannot be the same. I.e., one cannot
3777: call `MatSolve`(A,x,x).
3779: Most users should employ the `KSP` interface for linear solvers
3780: instead of working directly with matrix algebra routines such as this.
3781: See, e.g., `KSPCreate()`.
3783: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3784: @*/
3785: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3786: {
3787: PetscFunctionBegin;
3792: PetscCheckSameComm(mat, 1, b, 2);
3793: PetscCheckSameComm(mat, 1, x, 3);
3794: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3795: 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);
3796: 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);
3797: 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);
3798: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3799: MatCheckPreallocated(mat, 1);
3801: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3802: PetscCall(VecFlag(x, mat->factorerrortype));
3803: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3804: else PetscUseTypeMethod(mat, solve, b, x);
3805: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3806: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3807: PetscFunctionReturn(PETSC_SUCCESS);
3808: }
3810: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3811: {
3812: Vec b, x;
3813: PetscInt N, i;
3814: PetscErrorCode (*f)(Mat, Vec, Vec);
3815: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3817: PetscFunctionBegin;
3818: if (A->factorerrortype) {
3819: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3820: PetscCall(MatSetInf(X));
3821: PetscFunctionReturn(PETSC_SUCCESS);
3822: }
3823: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3824: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3825: PetscCall(MatBoundToCPU(A, &Abound));
3826: if (!Abound) {
3827: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3828: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3829: }
3830: #if PetscDefined(HAVE_CUDA)
3831: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3832: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3833: #elif PetscDefined(HAVE_HIP)
3834: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3835: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3836: #endif
3837: PetscCall(MatGetSize(B, NULL, &N));
3838: for (i = 0; i < N; i++) {
3839: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3840: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3841: PetscCall((*f)(A, b, x));
3842: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3843: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3844: }
3845: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3846: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3847: PetscFunctionReturn(PETSC_SUCCESS);
3848: }
3850: /*@
3851: MatMatSolve - Solves $A X = B$, given a factored matrix.
3853: Neighbor-wise Collective
3855: Input Parameters:
3856: + A - the factored matrix
3857: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3859: Output Parameter:
3860: . X - the result matrix (dense matrix)
3862: Level: developer
3864: Note:
3865: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3866: otherwise, `B` and `X` cannot be the same.
3868: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3869: @*/
3870: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3871: {
3872: PetscFunctionBegin;
3877: PetscCheckSameComm(A, 1, B, 2);
3878: PetscCheckSameComm(A, 1, X, 3);
3879: 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);
3880: 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);
3881: 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");
3882: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3883: MatCheckPreallocated(A, 1);
3885: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3886: if (!A->ops->matsolve) {
3887: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3888: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3889: } else PetscUseTypeMethod(A, matsolve, B, X);
3890: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3891: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3892: PetscFunctionReturn(PETSC_SUCCESS);
3893: }
3895: /*@
3896: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3898: Neighbor-wise Collective
3900: Input Parameters:
3901: + A - the factored matrix
3902: - B - the right-hand-side matrix (`MATDENSE` matrix)
3904: Output Parameter:
3905: . X - the result matrix (dense matrix)
3907: Level: developer
3909: Note:
3910: The matrices `B` and `X` cannot be the same. I.e., one cannot
3911: call `MatMatSolveTranspose`(A,X,X).
3913: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3914: @*/
3915: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3916: {
3917: PetscFunctionBegin;
3922: PetscCheckSameComm(A, 1, B, 2);
3923: PetscCheckSameComm(A, 1, X, 3);
3924: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3925: 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);
3926: 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);
3927: 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);
3928: 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");
3929: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3930: MatCheckPreallocated(A, 1);
3932: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3933: if (!A->ops->matsolvetranspose) {
3934: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3935: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3936: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3937: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3938: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3939: PetscFunctionReturn(PETSC_SUCCESS);
3940: }
3942: /*@
3943: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3945: Neighbor-wise Collective
3947: Input Parameters:
3948: + A - the factored matrix
3949: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3951: Output Parameter:
3952: . X - the result matrix (dense matrix)
3954: Level: developer
3956: Note:
3957: 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
3958: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3960: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3961: @*/
3962: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3963: {
3964: PetscFunctionBegin;
3969: PetscCheckSameComm(A, 1, Bt, 2);
3970: PetscCheckSameComm(A, 1, X, 3);
3972: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3973: 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);
3974: 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);
3975: 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");
3976: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3977: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3978: MatCheckPreallocated(A, 1);
3980: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3981: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3982: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3983: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3984: PetscFunctionReturn(PETSC_SUCCESS);
3985: }
3987: /*@
3988: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3989: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3991: Neighbor-wise Collective
3993: Input Parameters:
3994: + mat - the factored matrix
3995: - b - the right-hand-side vector
3997: Output Parameter:
3998: . x - the result vector
4000: Level: developer
4002: Notes:
4003: `MatSolve()` should be used for most applications, as it performs
4004: a forward solve followed by a backward solve.
4006: The vectors `b` and `x` cannot be the same, i.e., one cannot
4007: call `MatForwardSolve`(A,x,x).
4009: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4010: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4011: `MatForwardSolve()` solves $U^T*D y = b$, and
4012: `MatBackwardSolve()` solves $U x = y$.
4013: Thus they do not provide a symmetric preconditioner.
4015: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4016: @*/
4017: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4018: {
4019: PetscFunctionBegin;
4024: PetscCheckSameComm(mat, 1, b, 2);
4025: PetscCheckSameComm(mat, 1, x, 3);
4026: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4027: 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);
4028: 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);
4029: 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);
4030: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4031: MatCheckPreallocated(mat, 1);
4033: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4034: PetscUseTypeMethod(mat, forwardsolve, b, x);
4035: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4036: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4037: PetscFunctionReturn(PETSC_SUCCESS);
4038: }
4040: /*@
4041: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4042: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4044: Neighbor-wise Collective
4046: Input Parameters:
4047: + mat - the factored matrix
4048: - b - the right-hand-side vector
4050: Output Parameter:
4051: . x - the result vector
4053: Level: developer
4055: Notes:
4056: `MatSolve()` should be used for most applications, as it performs
4057: a forward solve followed by a backward solve.
4059: The vectors `b` and `x` cannot be the same. I.e., one cannot
4060: call `MatBackwardSolve`(A,x,x).
4062: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4063: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4064: `MatForwardSolve()` solves $U^T*D y = b$, and
4065: `MatBackwardSolve()` solves $U x = y$.
4066: Thus they do not provide a symmetric preconditioner.
4068: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4069: @*/
4070: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4071: {
4072: PetscFunctionBegin;
4077: PetscCheckSameComm(mat, 1, b, 2);
4078: PetscCheckSameComm(mat, 1, x, 3);
4079: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4080: 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);
4081: 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);
4082: 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);
4083: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4084: MatCheckPreallocated(mat, 1);
4086: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4087: PetscUseTypeMethod(mat, backwardsolve, b, x);
4088: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4089: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4090: PetscFunctionReturn(PETSC_SUCCESS);
4091: }
4093: /*@
4094: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4096: Neighbor-wise Collective
4098: Input Parameters:
4099: + mat - the factored matrix
4100: . b - the right-hand-side vector
4101: - y - the vector to be added to
4103: Output Parameter:
4104: . x - the result vector
4106: Level: developer
4108: Note:
4109: The vectors `b` and `x` cannot be the same. I.e., one cannot
4110: call `MatSolveAdd`(A,x,y,x).
4112: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4113: @*/
4114: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4115: {
4116: PetscScalar one = 1.0;
4117: Vec tmp;
4119: PetscFunctionBegin;
4125: PetscCheckSameComm(mat, 1, b, 2);
4126: PetscCheckSameComm(mat, 1, y, 3);
4127: PetscCheckSameComm(mat, 1, x, 4);
4128: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4129: 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);
4130: 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);
4131: 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);
4132: 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);
4133: 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);
4134: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4135: MatCheckPreallocated(mat, 1);
4137: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4138: PetscCall(VecFlag(x, mat->factorerrortype));
4139: if (mat->factorerrortype) {
4140: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4141: } else if (mat->ops->solveadd) {
4142: PetscUseTypeMethod(mat, solveadd, b, y, x);
4143: } else {
4144: /* do the solve then the add manually */
4145: if (x != y) {
4146: PetscCall(MatSolve(mat, b, x));
4147: PetscCall(VecAXPY(x, one, y));
4148: } else {
4149: PetscCall(VecDuplicate(x, &tmp));
4150: PetscCall(VecCopy(x, tmp));
4151: PetscCall(MatSolve(mat, b, x));
4152: PetscCall(VecAXPY(x, one, tmp));
4153: PetscCall(VecDestroy(&tmp));
4154: }
4155: }
4156: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4157: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4158: PetscFunctionReturn(PETSC_SUCCESS);
4159: }
4161: /*@
4162: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4164: Neighbor-wise Collective
4166: Input Parameters:
4167: + mat - the factored matrix
4168: - b - the right-hand-side vector
4170: Output Parameter:
4171: . x - the result vector
4173: Level: developer
4175: Notes:
4176: The vectors `b` and `x` cannot be the same. I.e., one cannot
4177: call `MatSolveTranspose`(A,x,x).
4179: Most users should employ the `KSP` interface for linear solvers
4180: instead of working directly with matrix algebra routines such as this.
4181: See, e.g., `KSPCreate()`.
4183: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4184: @*/
4185: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4186: {
4187: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4189: PetscFunctionBegin;
4194: PetscCheckSameComm(mat, 1, b, 2);
4195: PetscCheckSameComm(mat, 1, x, 3);
4196: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4197: 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);
4198: 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);
4199: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4200: MatCheckPreallocated(mat, 1);
4201: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4202: PetscCall(VecFlag(x, mat->factorerrortype));
4203: if (mat->factorerrortype) {
4204: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4205: } else {
4206: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4207: PetscCall((*f)(mat, b, x));
4208: }
4209: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4210: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4211: PetscFunctionReturn(PETSC_SUCCESS);
4212: }
4214: /*@
4215: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4216: factored matrix.
4218: Neighbor-wise Collective
4220: Input Parameters:
4221: + mat - the factored matrix
4222: . b - the right-hand-side vector
4223: - y - the vector to be added to
4225: Output Parameter:
4226: . x - the result vector
4228: Level: developer
4230: Note:
4231: The vectors `b` and `x` cannot be the same. I.e., one cannot
4232: call `MatSolveTransposeAdd`(A,x,y,x).
4234: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4235: @*/
4236: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4237: {
4238: PetscScalar one = 1.0;
4239: Vec tmp;
4240: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4242: PetscFunctionBegin;
4248: PetscCheckSameComm(mat, 1, b, 2);
4249: PetscCheckSameComm(mat, 1, y, 3);
4250: PetscCheckSameComm(mat, 1, x, 4);
4251: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4252: 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);
4253: 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);
4254: 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);
4255: 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);
4256: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4257: MatCheckPreallocated(mat, 1);
4259: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4260: PetscCall(VecFlag(x, mat->factorerrortype));
4261: if (mat->factorerrortype) {
4262: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4263: } else if (f) {
4264: PetscCall((*f)(mat, b, y, x));
4265: } else {
4266: /* do the solve then the add manually */
4267: if (x != y) {
4268: PetscCall(MatSolveTranspose(mat, b, x));
4269: PetscCall(VecAXPY(x, one, y));
4270: } else {
4271: PetscCall(VecDuplicate(x, &tmp));
4272: PetscCall(VecCopy(x, tmp));
4273: PetscCall(MatSolveTranspose(mat, b, x));
4274: PetscCall(VecAXPY(x, one, tmp));
4275: PetscCall(VecDestroy(&tmp));
4276: }
4277: }
4278: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4279: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4280: PetscFunctionReturn(PETSC_SUCCESS);
4281: }
4283: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4284: /*@
4285: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4287: Neighbor-wise Collective
4289: Input Parameters:
4290: + mat - the matrix
4291: . b - the right-hand side
4292: . omega - the relaxation factor
4293: . flag - flag indicating the type of SOR (see below)
4294: . shift - diagonal shift
4295: . its - the number of iterations
4296: - lits - the number of local iterations
4298: Output Parameter:
4299: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4301: SOR Flags:
4302: + `SOR_FORWARD_SWEEP` - forward SOR
4303: . `SOR_BACKWARD_SWEEP` - backward SOR
4304: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4305: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4306: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4307: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4308: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4309: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4310: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4312: Level: developer
4314: Notes:
4315: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4316: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4317: on each processor.
4319: Application programmers will not generally use `MatSOR()` directly,
4320: but instead will employ `PCSOR` or `PCEISENSTAT`
4322: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4323: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4325: Vectors `x` and `b` CANNOT be the same
4327: The flags are implemented as bitwise inclusive or operations.
4328: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4329: to specify a zero initial guess for SSOR.
4331: Developer Note:
4332: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4334: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4335: @*/
4336: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4337: {
4338: PetscFunctionBegin;
4343: PetscCheckSameComm(mat, 1, b, 2);
4344: PetscCheckSameComm(mat, 1, x, 8);
4345: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4346: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4347: 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);
4348: 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);
4349: 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);
4350: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4351: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4352: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4354: MatCheckPreallocated(mat, 1);
4355: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4356: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4357: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4358: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4359: PetscFunctionReturn(PETSC_SUCCESS);
4360: }
4362: /*
4363: Default matrix copy routine.
4364: */
4365: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4366: {
4367: PetscInt i, rstart = 0, rend = 0, nz;
4368: const PetscInt *cwork;
4369: const PetscScalar *vwork;
4371: PetscFunctionBegin;
4372: if (B->assembled) PetscCall(MatZeroEntries(B));
4373: if (str == SAME_NONZERO_PATTERN) {
4374: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4375: for (i = rstart; i < rend; i++) {
4376: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4377: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4378: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4379: }
4380: } else {
4381: PetscCall(MatAYPX(B, 0.0, A, str));
4382: }
4383: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4384: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4385: PetscFunctionReturn(PETSC_SUCCESS);
4386: }
4388: /*@
4389: MatCopy - Copies a matrix to another matrix.
4391: Collective
4393: Input Parameters:
4394: + A - the matrix
4395: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4397: Output Parameter:
4398: . B - where the copy is put
4400: Level: intermediate
4402: Notes:
4403: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4405: `MatCopy()` copies the matrix entries of a matrix to another existing
4406: matrix (after first zeroing the second matrix). A related routine is
4407: `MatConvert()`, which first creates a new matrix and then copies the data.
4409: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4410: @*/
4411: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4412: {
4413: PetscInt i;
4415: PetscFunctionBegin;
4420: PetscCheckSameComm(A, 1, B, 2);
4421: MatCheckPreallocated(B, 2);
4422: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4423: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4424: 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,
4425: A->cmap->N, B->cmap->N);
4426: MatCheckPreallocated(A, 1);
4427: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4429: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4430: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4431: else PetscCall(MatCopy_Basic(A, B, str));
4433: B->stencil.dim = A->stencil.dim;
4434: B->stencil.noc = A->stencil.noc;
4435: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4436: B->stencil.dims[i] = A->stencil.dims[i];
4437: B->stencil.starts[i] = A->stencil.starts[i];
4438: }
4440: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4441: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4442: PetscFunctionReturn(PETSC_SUCCESS);
4443: }
4445: /*@
4446: MatConvert - Converts a matrix to another matrix, either of the same
4447: or different type.
4449: Collective
4451: Input Parameters:
4452: + mat - the matrix
4453: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4454: same type as the original matrix.
4455: - reuse - denotes if the destination matrix is to be created or reused.
4456: 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
4457: `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).
4459: Output Parameter:
4460: . M - pointer to place new matrix
4462: Level: intermediate
4464: Notes:
4465: `MatConvert()` first creates a new matrix and then copies the data from
4466: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4467: entries of one matrix to another already existing matrix context.
4469: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4470: the MPI communicator of the generated matrix is always the same as the communicator
4471: of the input matrix.
4473: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4474: @*/
4475: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4476: {
4477: PetscBool sametype, issame, flg;
4478: PetscBool3 issymmetric, ishermitian, isspd;
4479: char convname[256], mtype[256];
4480: Mat B;
4482: PetscFunctionBegin;
4485: PetscAssertPointer(M, 4);
4486: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4487: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4488: MatCheckPreallocated(mat, 1);
4490: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4491: if (flg) newtype = mtype;
4493: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4494: PetscCall(PetscStrcmp(newtype, "same", &issame));
4495: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4496: if (reuse == MAT_REUSE_MATRIX) {
4498: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4499: }
4501: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4502: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4503: PetscFunctionReturn(PETSC_SUCCESS);
4504: }
4506: /* Cache Mat options because some converters use MatHeaderReplace() */
4507: issymmetric = mat->symmetric;
4508: ishermitian = mat->hermitian;
4509: isspd = mat->spd;
4511: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4512: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4513: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4514: } else {
4515: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4516: const char *prefix[3] = {"seq", "mpi", ""};
4517: PetscInt i;
4518: /*
4519: Order of precedence:
4520: 0) See if newtype is a superclass of the current matrix.
4521: 1) See if a specialized converter is known to the current matrix.
4522: 2) See if a specialized converter is known to the desired matrix class.
4523: 3) See if a good general converter is registered for the desired class
4524: (as of 6/27/03 only MATMPIADJ falls into this category).
4525: 4) See if a good general converter is known for the current matrix.
4526: 5) Use a really basic converter.
4527: */
4529: /* 0) See if newtype is a superclass of the current matrix.
4530: i.e mat is mpiaij and newtype is aij */
4531: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4532: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4533: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4534: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4535: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4536: if (flg) {
4537: if (reuse == MAT_INPLACE_MATRIX) {
4538: PetscCall(PetscInfo(mat, "Early return\n"));
4539: PetscFunctionReturn(PETSC_SUCCESS);
4540: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4541: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4542: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4543: PetscFunctionReturn(PETSC_SUCCESS);
4544: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4545: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4546: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4547: PetscFunctionReturn(PETSC_SUCCESS);
4548: }
4549: }
4550: }
4551: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4552: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4553: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4554: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4555: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4556: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4557: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4558: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4559: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4560: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4561: if (conv) goto foundconv;
4562: }
4564: /* 2) See if a specialized converter is known to the desired matrix class. */
4565: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4566: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4567: PetscCall(MatSetType(B, newtype));
4568: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4569: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4570: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4571: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4572: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4573: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4574: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4575: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4576: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4577: if (conv) {
4578: PetscCall(MatDestroy(&B));
4579: goto foundconv;
4580: }
4581: }
4583: /* 3) See if a good general converter is registered for the desired class */
4584: conv = B->ops->convertfrom;
4585: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4586: PetscCall(MatDestroy(&B));
4587: if (conv) goto foundconv;
4589: /* 4) See if a good general converter is known for the current matrix */
4590: if (mat->ops->convert) conv = mat->ops->convert;
4591: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4592: if (conv) goto foundconv;
4594: /* 5) Use a really basic converter. */
4595: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4596: conv = MatConvert_Basic;
4598: foundconv:
4599: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4600: PetscCall((*conv)(mat, newtype, reuse, M));
4601: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4602: /* the block sizes must be same if the mappings are copied over */
4603: (*M)->rmap->bs = mat->rmap->bs;
4604: (*M)->cmap->bs = mat->cmap->bs;
4605: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4606: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4607: (*M)->rmap->mapping = mat->rmap->mapping;
4608: (*M)->cmap->mapping = mat->cmap->mapping;
4609: }
4610: (*M)->stencil.dim = mat->stencil.dim;
4611: (*M)->stencil.noc = mat->stencil.noc;
4612: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4613: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4614: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4615: }
4616: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4617: }
4618: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4620: /* Reset Mat options */
4621: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4622: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4623: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4624: PetscFunctionReturn(PETSC_SUCCESS);
4625: }
4627: /*@
4628: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4630: Not Collective
4632: Input Parameter:
4633: . mat - the matrix, must be a factored matrix
4635: Output Parameter:
4636: . type - the string name of the package (do not free this string)
4638: Level: intermediate
4640: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4641: @*/
4642: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4643: {
4644: PetscErrorCode (*conv)(Mat, MatSolverType *);
4646: PetscFunctionBegin;
4649: PetscAssertPointer(type, 2);
4650: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4651: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4652: if (conv) PetscCall((*conv)(mat, type));
4653: else *type = MATSOLVERPETSC;
4654: PetscFunctionReturn(PETSC_SUCCESS);
4655: }
4657: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4658: struct _MatSolverTypeForSpecifcType {
4659: MatType mtype;
4660: /* no entry for MAT_FACTOR_NONE */
4661: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4662: MatSolverTypeForSpecifcType next;
4663: };
4665: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4666: struct _MatSolverTypeHolder {
4667: char *name;
4668: MatSolverTypeForSpecifcType handlers;
4669: MatSolverTypeHolder next;
4670: };
4672: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4674: /*@C
4675: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4677: Logically Collective, No Fortran Support
4679: Input Parameters:
4680: + package - name of the package, for example `petsc` or `superlu`
4681: . mtype - the matrix type that works with this package
4682: . ftype - the type of factorization supported by the package
4683: - createfactor - routine that will create the factored matrix ready to be used
4685: Level: developer
4687: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4688: `MatGetFactor()`
4689: @*/
4690: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4691: {
4692: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4693: PetscBool flg;
4694: MatSolverTypeForSpecifcType inext, iprev = NULL;
4696: PetscFunctionBegin;
4697: PetscCall(MatInitializePackage());
4698: if (!next) {
4699: PetscCall(PetscNew(&MatSolverTypeHolders));
4700: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4701: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4702: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4703: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4704: PetscFunctionReturn(PETSC_SUCCESS);
4705: }
4706: while (next) {
4707: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4708: if (flg) {
4709: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4710: inext = next->handlers;
4711: while (inext) {
4712: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4713: if (flg) {
4714: inext->createfactor[(int)ftype - 1] = createfactor;
4715: PetscFunctionReturn(PETSC_SUCCESS);
4716: }
4717: iprev = inext;
4718: inext = inext->next;
4719: }
4720: PetscCall(PetscNew(&iprev->next));
4721: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4722: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4723: PetscFunctionReturn(PETSC_SUCCESS);
4724: }
4725: prev = next;
4726: next = next->next;
4727: }
4728: PetscCall(PetscNew(&prev->next));
4729: PetscCall(PetscStrallocpy(package, &prev->next->name));
4730: PetscCall(PetscNew(&prev->next->handlers));
4731: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4732: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4733: PetscFunctionReturn(PETSC_SUCCESS);
4734: }
4736: /*@C
4737: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4739: Input Parameters:
4740: + 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
4741: . ftype - the type of factorization supported by the type
4742: - mtype - the matrix type that works with this type
4744: Output Parameters:
4745: + foundtype - `PETSC_TRUE` if the type was registered
4746: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4747: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4749: Calling sequence of `createfactor`:
4750: + A - the matrix providing the factor matrix
4751: . ftype - the `MatFactorType` of the factor requested
4752: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4754: Level: developer
4756: Note:
4757: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4758: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4759: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4761: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4762: `MatInitializePackage()`
4763: @*/
4764: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4765: {
4766: MatSolverTypeHolder next = MatSolverTypeHolders;
4767: PetscBool flg;
4768: MatSolverTypeForSpecifcType inext;
4770: PetscFunctionBegin;
4771: if (foundtype) *foundtype = PETSC_FALSE;
4772: if (foundmtype) *foundmtype = PETSC_FALSE;
4773: if (createfactor) *createfactor = NULL;
4775: if (type) {
4776: while (next) {
4777: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4778: if (flg) {
4779: if (foundtype) *foundtype = PETSC_TRUE;
4780: inext = next->handlers;
4781: while (inext) {
4782: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4783: if (flg) {
4784: if (foundmtype) *foundmtype = PETSC_TRUE;
4785: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4786: PetscFunctionReturn(PETSC_SUCCESS);
4787: }
4788: inext = inext->next;
4789: }
4790: }
4791: next = next->next;
4792: }
4793: } else {
4794: while (next) {
4795: inext = next->handlers;
4796: while (inext) {
4797: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4798: if (flg && inext->createfactor[(int)ftype - 1]) {
4799: if (foundtype) *foundtype = PETSC_TRUE;
4800: if (foundmtype) *foundmtype = PETSC_TRUE;
4801: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4802: PetscFunctionReturn(PETSC_SUCCESS);
4803: }
4804: inext = inext->next;
4805: }
4806: next = next->next;
4807: }
4808: /* try with base classes inext->mtype */
4809: next = MatSolverTypeHolders;
4810: while (next) {
4811: inext = next->handlers;
4812: while (inext) {
4813: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4814: if (flg && inext->createfactor[(int)ftype - 1]) {
4815: if (foundtype) *foundtype = PETSC_TRUE;
4816: if (foundmtype) *foundmtype = PETSC_TRUE;
4817: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4818: PetscFunctionReturn(PETSC_SUCCESS);
4819: }
4820: inext = inext->next;
4821: }
4822: next = next->next;
4823: }
4824: }
4825: PetscFunctionReturn(PETSC_SUCCESS);
4826: }
4828: PetscErrorCode MatSolverTypeDestroy(void)
4829: {
4830: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4831: MatSolverTypeForSpecifcType inext, iprev;
4833: PetscFunctionBegin;
4834: while (next) {
4835: PetscCall(PetscFree(next->name));
4836: inext = next->handlers;
4837: while (inext) {
4838: PetscCall(PetscFree(inext->mtype));
4839: iprev = inext;
4840: inext = inext->next;
4841: PetscCall(PetscFree(iprev));
4842: }
4843: prev = next;
4844: next = next->next;
4845: PetscCall(PetscFree(prev));
4846: }
4847: MatSolverTypeHolders = NULL;
4848: PetscFunctionReturn(PETSC_SUCCESS);
4849: }
4851: /*@
4852: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4854: Logically Collective
4856: Input Parameter:
4857: . mat - the matrix
4859: Output Parameter:
4860: . flg - `PETSC_TRUE` if uses the ordering
4862: Level: developer
4864: Note:
4865: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4866: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4868: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4869: @*/
4870: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4871: {
4872: PetscFunctionBegin;
4873: *flg = mat->canuseordering;
4874: PetscFunctionReturn(PETSC_SUCCESS);
4875: }
4877: /*@
4878: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4880: Logically Collective
4882: Input Parameters:
4883: + mat - the matrix obtained with `MatGetFactor()`
4884: - ftype - the factorization type to be used
4886: Output Parameter:
4887: . otype - the preferred ordering type
4889: Level: developer
4891: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4892: @*/
4893: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4894: {
4895: PetscFunctionBegin;
4896: *otype = mat->preferredordering[ftype];
4897: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4898: PetscFunctionReturn(PETSC_SUCCESS);
4899: }
4901: /*@
4902: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4903: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`, `MatILUFactorNumeric()`, and
4904: `MatICCFactorNumeric()`
4906: Collective
4908: Input Parameters:
4909: + mat - the matrix
4910: . 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
4911: the other criteria is returned
4912: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4914: Output Parameter:
4915: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4917: Options Database Keys:
4918: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
4919: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
4920: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
4922: Level: intermediate
4924: Notes:
4925: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
4926: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
4927: without using a `PC`, one can set the prefix by
4928: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4930: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
4931: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
4932: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
4934: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4935: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4936: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4938: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4939: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4941: Developer Note:
4942: This should actually be called `MatCreateFactor()` since it creates a new factor object
4944: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
4945: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
4947: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4948: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
4949: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
4950: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4951: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`, `MatILUFactorNumeric()`,
4952: `MatICCFactorNumeric()`
4953: @*/
4954: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4955: {
4956: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4957: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4959: PetscFunctionBegin;
4963: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4964: MatCheckPreallocated(mat, 1);
4966: PetscCall(MatIsShell(mat, &shell));
4967: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4968: if (hasop) {
4969: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4970: PetscFunctionReturn(PETSC_SUCCESS);
4971: }
4973: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4974: if (!foundtype) {
4975: if (type) {
4976: 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],
4977: ((PetscObject)mat)->type_name, type);
4978: } else {
4979: 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);
4980: }
4981: }
4982: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4983: 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);
4985: PetscCall((*conv)(mat, ftype, f));
4986: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4987: PetscFunctionReturn(PETSC_SUCCESS);
4988: }
4990: /*@
4991: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4993: Not Collective
4995: Input Parameters:
4996: + mat - the matrix
4997: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4998: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5000: Output Parameter:
5001: . flg - PETSC_TRUE if the factorization is available
5003: Level: intermediate
5005: Notes:
5006: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
5007: such as pastix, superlu, mumps etc.
5009: PETSc must have been ./configure to use the external solver, using the option --download-package
5011: Developer Note:
5012: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
5014: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
5015: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
5016: @*/
5017: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5018: {
5019: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5021: PetscFunctionBegin;
5023: PetscAssertPointer(flg, 4);
5025: *flg = PETSC_FALSE;
5026: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5028: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5029: MatCheckPreallocated(mat, 1);
5031: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5032: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5033: PetscFunctionReturn(PETSC_SUCCESS);
5034: }
5036: /*@
5037: MatDuplicate - Duplicates a matrix including the non-zero structure.
5039: Collective
5041: Input Parameters:
5042: + mat - the matrix
5043: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5044: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5046: Output Parameter:
5047: . M - pointer to place new matrix
5049: Level: intermediate
5051: Notes:
5052: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5054: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5056: 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.
5058: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5059: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5060: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5062: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5063: @*/
5064: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5065: {
5066: Mat B;
5067: VecType vtype;
5068: PetscInt i;
5069: PetscObject dm, container_h, container_d;
5070: PetscErrorCodeFn *viewf;
5072: PetscFunctionBegin;
5075: PetscAssertPointer(M, 3);
5076: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5077: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5078: MatCheckPreallocated(mat, 1);
5080: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5081: PetscUseTypeMethod(mat, duplicate, op, M);
5082: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5083: B = *M;
5085: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5086: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5087: PetscCall(MatGetVecType(mat, &vtype));
5088: PetscCall(MatSetVecType(B, vtype));
5090: B->stencil.dim = mat->stencil.dim;
5091: B->stencil.noc = mat->stencil.noc;
5092: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5093: B->stencil.dims[i] = mat->stencil.dims[i];
5094: B->stencil.starts[i] = mat->stencil.starts[i];
5095: }
5097: B->nooffproczerorows = mat->nooffproczerorows;
5098: B->nooffprocentries = mat->nooffprocentries;
5100: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5101: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5102: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5103: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5104: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5105: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5106: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5107: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5108: PetscFunctionReturn(PETSC_SUCCESS);
5109: }
5111: /*@
5112: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5114: Logically Collective
5116: Input Parameter:
5117: . mat - the matrix
5119: Output Parameter:
5120: . v - the diagonal of the matrix
5122: Level: intermediate
5124: Note:
5125: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5126: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5127: is larger than `ndiag`, the values of the remaining entries are unspecified.
5129: Currently only correct in parallel for square matrices.
5131: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5132: @*/
5133: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5134: {
5135: PetscFunctionBegin;
5139: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5140: MatCheckPreallocated(mat, 1);
5141: if (PetscDefined(USE_DEBUG)) {
5142: PetscInt nv, row, col, ndiag;
5144: PetscCall(VecGetLocalSize(v, &nv));
5145: PetscCall(MatGetLocalSize(mat, &row, &col));
5146: ndiag = PetscMin(row, col);
5147: 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);
5148: }
5150: PetscUseTypeMethod(mat, getdiagonal, v);
5151: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5152: PetscFunctionReturn(PETSC_SUCCESS);
5153: }
5155: /*@
5156: MatGetRowMin - Gets the minimum value (of the real part) of each
5157: row of the matrix
5159: Logically Collective
5161: Input Parameter:
5162: . mat - the matrix
5164: Output Parameters:
5165: + v - the vector for storing the maximums
5166: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5168: Level: intermediate
5170: Note:
5171: The result of this call are the same as if one converted the matrix to dense format
5172: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5174: This code is only implemented for a couple of matrix formats.
5176: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5177: `MatGetRowMax()`
5178: @*/
5179: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5180: {
5181: PetscFunctionBegin;
5185: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5187: if (!mat->cmap->N) {
5188: PetscCall(VecSet(v, PETSC_MAX_REAL));
5189: if (idx) {
5190: PetscInt i, m = mat->rmap->n;
5191: for (i = 0; i < m; i++) idx[i] = -1;
5192: }
5193: } else {
5194: MatCheckPreallocated(mat, 1);
5195: }
5196: PetscUseTypeMethod(mat, getrowmin, v, idx);
5197: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5198: PetscFunctionReturn(PETSC_SUCCESS);
5199: }
5201: /*@
5202: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5203: row of the matrix
5205: Logically Collective
5207: Input Parameter:
5208: . mat - the matrix
5210: Output Parameters:
5211: + v - the vector for storing the minimums
5212: - idx - the indices of the column found for each row (or `NULL` if not needed)
5214: Level: intermediate
5216: Notes:
5217: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5218: row is 0 (the first column).
5220: This code is only implemented for a couple of matrix formats.
5222: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5223: @*/
5224: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5225: {
5226: PetscFunctionBegin;
5230: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5231: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5233: if (!mat->cmap->N) {
5234: PetscCall(VecSet(v, 0.0));
5235: if (idx) {
5236: PetscInt i, m = mat->rmap->n;
5237: for (i = 0; i < m; i++) idx[i] = -1;
5238: }
5239: } else {
5240: MatCheckPreallocated(mat, 1);
5241: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5242: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5243: }
5244: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5245: PetscFunctionReturn(PETSC_SUCCESS);
5246: }
5248: /*@
5249: MatGetRowMax - Gets the maximum value (of the real part) of each
5250: row of the matrix
5252: Logically Collective
5254: Input Parameter:
5255: . mat - the matrix
5257: Output Parameters:
5258: + v - the vector for storing the maximums
5259: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5261: Level: intermediate
5263: Notes:
5264: The result of this call are the same as if one converted the matrix to dense format
5265: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5267: This code is only implemented for a couple of matrix formats.
5269: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5270: @*/
5271: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5272: {
5273: PetscFunctionBegin;
5277: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5279: if (!mat->cmap->N) {
5280: PetscCall(VecSet(v, PETSC_MIN_REAL));
5281: if (idx) {
5282: PetscInt i, m = mat->rmap->n;
5283: for (i = 0; i < m; i++) idx[i] = -1;
5284: }
5285: } else {
5286: MatCheckPreallocated(mat, 1);
5287: PetscUseTypeMethod(mat, getrowmax, v, idx);
5288: }
5289: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5290: PetscFunctionReturn(PETSC_SUCCESS);
5291: }
5293: /*@
5294: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5295: row of the matrix
5297: Logically Collective
5299: Input Parameter:
5300: . mat - the matrix
5302: Output Parameters:
5303: + v - the vector for storing the maximums
5304: - idx - the indices of the column found for each row (or `NULL` if not needed)
5306: Level: intermediate
5308: Notes:
5309: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5310: row is 0 (the first column).
5312: This code is only implemented for a couple of matrix formats.
5314: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5315: @*/
5316: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5317: {
5318: PetscFunctionBegin;
5322: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5324: if (!mat->cmap->N) {
5325: PetscCall(VecSet(v, 0.0));
5326: if (idx) {
5327: PetscInt i, m = mat->rmap->n;
5328: for (i = 0; i < m; i++) idx[i] = -1;
5329: }
5330: } else {
5331: MatCheckPreallocated(mat, 1);
5332: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5333: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5334: }
5335: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5336: PetscFunctionReturn(PETSC_SUCCESS);
5337: }
5339: /*@
5340: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5342: Logically Collective
5344: Input Parameter:
5345: . mat - the matrix
5347: Output Parameter:
5348: . v - the vector for storing the sum
5350: Level: intermediate
5352: This code is only implemented for a couple of matrix formats.
5354: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5355: @*/
5356: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5357: {
5358: PetscFunctionBegin;
5362: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5364: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5365: else {
5366: MatCheckPreallocated(mat, 1);
5367: PetscUseTypeMethod(mat, getrowsumabs, v);
5368: }
5369: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5370: PetscFunctionReturn(PETSC_SUCCESS);
5371: }
5373: /*@
5374: MatGetRowSum - Gets the sum of each row of the matrix
5376: Logically or Neighborhood Collective
5378: Input Parameter:
5379: . mat - the matrix
5381: Output Parameter:
5382: . v - the vector for storing the sum of rows
5384: Level: intermediate
5386: Note:
5387: This code is slow since it is not currently specialized for different formats
5389: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5390: @*/
5391: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5392: {
5393: Vec ones;
5395: PetscFunctionBegin;
5399: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5400: MatCheckPreallocated(mat, 1);
5401: PetscCall(MatCreateVecs(mat, &ones, NULL));
5402: PetscCall(VecSet(ones, 1.));
5403: PetscCall(MatMult(mat, ones, v));
5404: PetscCall(VecDestroy(&ones));
5405: PetscFunctionReturn(PETSC_SUCCESS);
5406: }
5408: /*@
5409: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5410: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5412: Collective
5414: Input Parameter:
5415: . mat - the matrix to provide the transpose
5417: Output Parameter:
5418: . 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
5420: Level: advanced
5422: Note:
5423: 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
5424: routine allows bypassing that call.
5426: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5427: @*/
5428: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5429: {
5430: MatParentState *rb = NULL;
5432: PetscFunctionBegin;
5433: PetscCall(PetscNew(&rb));
5434: rb->id = ((PetscObject)mat)->id;
5435: rb->state = 0;
5436: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5437: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5438: PetscFunctionReturn(PETSC_SUCCESS);
5439: }
5441: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5442: {
5443: PetscContainer rB = NULL;
5444: MatParentState *rb = NULL;
5445: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5447: PetscFunctionBegin;
5450: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5451: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5452: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5453: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5454: MatCheckPreallocated(mat, 1);
5455: if (reuse == MAT_REUSE_MATRIX) {
5456: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5457: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5458: PetscCall(PetscContainerGetPointer(rB, &rb));
5459: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5460: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5461: }
5463: if (conjugate) {
5464: f = mat->ops->hermitiantranspose;
5465: if (f) PetscCall((*f)(mat, reuse, B));
5466: }
5467: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5468: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5469: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5470: PetscUseTypeMethod(mat, transpose, reuse, B);
5471: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5472: }
5473: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5474: if (conjugate) PetscCall(MatConjugate(*B));
5475: }
5477: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5478: if (reuse != MAT_INPLACE_MATRIX) {
5479: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5480: PetscCall(PetscContainerGetPointer(rB, &rb));
5481: rb->state = ((PetscObject)mat)->state;
5482: rb->nonzerostate = mat->nonzerostate;
5483: }
5484: PetscFunctionReturn(PETSC_SUCCESS);
5485: }
5487: /*@
5488: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5490: Collective
5492: Input Parameters:
5493: + mat - the matrix to transpose
5494: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5496: Output Parameter:
5497: . B - the transpose of the matrix
5499: Level: intermediate
5501: Notes:
5502: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5504: `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
5505: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5507: 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.
5509: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5510: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5512: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5514: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5516: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5517: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5518: @*/
5519: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5520: {
5521: PetscFunctionBegin;
5522: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5523: PetscFunctionReturn(PETSC_SUCCESS);
5524: }
5526: /*@
5527: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5529: Collective
5531: Input Parameter:
5532: . A - the matrix to transpose
5534: Output Parameter:
5535: . 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
5536: numerical portion.
5538: Level: intermediate
5540: Note:
5541: This is not supported for many matrix types, use `MatTranspose()` in those cases
5543: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5544: @*/
5545: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5546: {
5547: PetscFunctionBegin;
5550: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5551: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5552: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5553: PetscUseTypeMethod(A, transposesymbolic, B);
5554: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5556: PetscCall(MatTransposeSetPrecursor(A, *B));
5557: PetscFunctionReturn(PETSC_SUCCESS);
5558: }
5560: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5561: {
5562: PetscContainer rB;
5563: MatParentState *rb;
5565: PetscFunctionBegin;
5568: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5569: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5570: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5571: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5572: PetscCall(PetscContainerGetPointer(rB, &rb));
5573: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5574: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5575: PetscFunctionReturn(PETSC_SUCCESS);
5576: }
5578: /*@
5579: MatIsTranspose - Test whether a matrix is another one's transpose,
5580: or its own, in which case it tests symmetry.
5582: Collective
5584: Input Parameters:
5585: + A - the matrix to test
5586: . B - the matrix to test against, this can equal the first parameter
5587: - tol - tolerance, differences between entries smaller than this are counted as zero
5589: Output Parameter:
5590: . flg - the result
5592: Level: intermediate
5594: Notes:
5595: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5596: test involves parallel copies of the block off-diagonal parts of the matrix.
5598: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5599: @*/
5600: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5601: {
5602: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5604: PetscFunctionBegin;
5607: PetscAssertPointer(flg, 4);
5608: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5609: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5610: *flg = PETSC_FALSE;
5611: if (f && g) {
5612: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5613: PetscCall((*f)(A, B, tol, flg));
5614: } else {
5615: MatType mattype;
5617: PetscCall(MatGetType(f ? B : A, &mattype));
5618: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5619: }
5620: PetscFunctionReturn(PETSC_SUCCESS);
5621: }
5623: /*@
5624: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5626: Collective
5628: Input Parameters:
5629: + mat - the matrix to transpose and complex conjugate
5630: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5632: Output Parameter:
5633: . B - the Hermitian transpose
5635: Level: intermediate
5637: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5638: @*/
5639: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5640: {
5641: PetscFunctionBegin;
5642: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_TRUE));
5643: PetscFunctionReturn(PETSC_SUCCESS);
5644: }
5646: /*@
5647: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5649: Collective
5651: Input Parameters:
5652: + A - the matrix to test
5653: . B - the matrix to test against, this can equal the first parameter
5654: - tol - tolerance, differences between entries smaller than this are counted as zero
5656: Output Parameter:
5657: . flg - the result
5659: Level: intermediate
5661: Notes:
5662: Only available for `MATAIJ` matrices.
5664: The sequential algorithm
5665: has a running time of the order of the number of nonzeros; the parallel
5666: test involves parallel copies of the block off-diagonal parts of the matrix.
5668: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5669: @*/
5670: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5671: {
5672: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5674: PetscFunctionBegin;
5677: PetscAssertPointer(flg, 4);
5678: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5679: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5680: if (f && g) {
5681: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5682: PetscCall((*f)(A, B, tol, flg));
5683: } else {
5684: MatType mattype;
5686: PetscCall(MatGetType(f ? B : A, &mattype));
5687: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5688: }
5689: PetscFunctionReturn(PETSC_SUCCESS);
5690: }
5692: /*@
5693: MatPermute - Creates a new matrix with rows and columns permuted from the
5694: original.
5696: Collective
5698: Input Parameters:
5699: + mat - the matrix to permute
5700: . row - row permutation, each processor supplies only the permutation for its rows
5701: - col - column permutation, each processor supplies only the permutation for its columns
5703: Output Parameter:
5704: . B - the permuted matrix
5706: Level: advanced
5708: Note:
5709: The index sets map from row/col of permuted matrix to row/col of original matrix.
5710: The index sets should be on the same communicator as mat and have the same local sizes.
5712: Developer Note:
5713: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5714: exploit the fact that row and col are permutations, consider implementing the
5715: more general `MatCreateSubMatrix()` instead.
5717: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5718: @*/
5719: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5720: {
5721: PetscFunctionBegin;
5726: PetscAssertPointer(B, 4);
5727: PetscCheckSameComm(mat, 1, row, 2);
5728: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5729: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5730: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5731: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5732: MatCheckPreallocated(mat, 1);
5734: if (mat->ops->permute) {
5735: PetscUseTypeMethod(mat, permute, row, col, B);
5736: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5737: } else {
5738: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5739: }
5740: PetscFunctionReturn(PETSC_SUCCESS);
5741: }
5743: /*@
5744: MatEqual - Compares two matrices.
5746: Collective
5748: Input Parameters:
5749: + A - the first matrix
5750: - B - the second matrix
5752: Output Parameter:
5753: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5755: Level: intermediate
5757: Note:
5758: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5759: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5761: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5762: @*/
5763: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5764: {
5765: PetscFunctionBegin;
5770: PetscAssertPointer(flg, 3);
5771: PetscCheckSameComm(A, 1, B, 2);
5772: MatCheckPreallocated(A, 1);
5773: MatCheckPreallocated(B, 2);
5774: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5775: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5776: 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,
5777: B->cmap->N);
5778: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5779: else PetscCall(MatMultEqual(A, B, 10, flg));
5780: PetscFunctionReturn(PETSC_SUCCESS);
5781: }
5783: /*@
5784: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5785: matrices that are stored as vectors. Either of the two scaling
5786: matrices can be `NULL`.
5788: Collective
5790: Input Parameters:
5791: + mat - the matrix to be scaled
5792: . l - the left scaling vector (or `NULL`)
5793: - r - the right scaling vector (or `NULL`)
5795: Level: intermediate
5797: Note:
5798: `MatDiagonalScale()` computes $A = LAR$, where
5799: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5800: The L scales the rows of the matrix, the R scales the columns of the matrix.
5802: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5803: @*/
5804: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5805: {
5806: PetscBool flg = PETSC_FALSE;
5808: PetscFunctionBegin;
5811: if (l) {
5813: PetscCheckSameComm(mat, 1, l, 2);
5814: }
5815: if (r) {
5817: PetscCheckSameComm(mat, 1, r, 3);
5818: }
5819: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5820: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5821: MatCheckPreallocated(mat, 1);
5822: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5824: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5825: PetscUseTypeMethod(mat, diagonalscale, l, r);
5826: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5827: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5828: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5829: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5830: if (l && r) PetscCall(VecEqual(l, r, &flg));
5831: if (!flg) {
5832: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5833: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format, left and right scaling vectors must be the same");
5834: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5835: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5836: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5837: }
5838: }
5839: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5840: flg = PETSC_FALSE;
5841: if (l && r) {
5842: Vec conjugate;
5844: PetscCall(VecDuplicate(l, &conjugate));
5845: PetscCall(VecCopy(l, conjugate));
5846: PetscCall(VecConjugate(conjugate));
5847: PetscCall(VecEqual(conjugate, r, &flg));
5848: PetscCall(VecDestroy(&conjugate));
5849: }
5850: if (!flg) {
5851: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5852: 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");
5853: mat->hermitian = PETSC_BOOL3_FALSE;
5854: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5855: }
5856: }
5857: }
5858: PetscFunctionReturn(PETSC_SUCCESS);
5859: }
5861: /*@
5862: MatScale - Scales all elements of a matrix by a given number.
5864: Logically Collective
5866: Input Parameters:
5867: + mat - the matrix to be scaled
5868: - a - the scaling value
5870: Level: intermediate
5872: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5873: @*/
5874: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5875: {
5876: PetscFunctionBegin;
5879: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5880: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5882: MatCheckPreallocated(mat, 1);
5884: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5885: if (a != (PetscScalar)1.0) {
5886: PetscUseTypeMethod(mat, scale, a);
5887: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5888: }
5889: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5890: PetscFunctionReturn(PETSC_SUCCESS);
5891: }
5893: /*@
5894: MatNorm - Calculates various norms of a matrix.
5896: Collective
5898: Input Parameters:
5899: + mat - the matrix
5900: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5902: Output Parameter:
5903: . nrm - the resulting norm
5905: Level: intermediate
5907: .seealso: [](ch_matrices), `Mat`
5908: @*/
5909: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5910: {
5911: PetscFunctionBegin;
5914: PetscAssertPointer(nrm, 3);
5916: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5917: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5918: MatCheckPreallocated(mat, 1);
5920: PetscUseTypeMethod(mat, norm, type, nrm);
5921: PetscFunctionReturn(PETSC_SUCCESS);
5922: }
5924: /*
5925: This variable is used to prevent counting of MatAssemblyBegin() that
5926: are called from within a MatAssemblyEnd().
5927: */
5928: static PetscInt MatAssemblyEnd_InUse = 0;
5929: /*@
5930: MatAssemblyBegin - Begins assembling the matrix. This routine should
5931: be called after completing all calls to `MatSetValues()`.
5933: Collective
5935: Input Parameters:
5936: + mat - the matrix
5937: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5939: Level: beginner
5941: Notes:
5942: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5943: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5945: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5946: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5947: using the matrix.
5949: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5950: 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
5951: a global collective operation requiring all processes that share the matrix.
5953: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5954: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5955: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5957: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5958: @*/
5959: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5960: {
5961: PetscFunctionBegin;
5964: MatCheckPreallocated(mat, 1);
5965: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5966: if (mat->assembled) {
5967: mat->was_assembled = PETSC_TRUE;
5968: mat->assembled = PETSC_FALSE;
5969: }
5971: if (!MatAssemblyEnd_InUse) {
5972: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5973: PetscTryTypeMethod(mat, assemblybegin, type);
5974: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5975: } else PetscTryTypeMethod(mat, assemblybegin, type);
5976: PetscFunctionReturn(PETSC_SUCCESS);
5977: }
5979: /*@
5980: MatAssembled - Indicates if a matrix has been assembled and is ready for
5981: use; for example, in matrix-vector product.
5983: Not Collective
5985: Input Parameter:
5986: . mat - the matrix
5988: Output Parameter:
5989: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5991: Level: advanced
5993: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5994: @*/
5995: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5996: {
5997: PetscFunctionBegin;
5999: PetscAssertPointer(assembled, 2);
6000: *assembled = mat->assembled;
6001: PetscFunctionReturn(PETSC_SUCCESS);
6002: }
6004: /*@
6005: MatAssemblyEnd - Completes assembling the matrix. This routine should
6006: be called after `MatAssemblyBegin()`.
6008: Collective
6010: Input Parameters:
6011: + mat - the matrix
6012: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6014: Options Database Keys:
6015: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
6016: . -mat_view ::ascii_info_detail - Prints more detailed info
6017: . -mat_view - Prints matrix in ASCII format
6018: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
6019: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
6020: . -display name - Sets display name (default is host)
6021: . -draw_pause sec - Sets number of seconds to pause after display
6022: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
6023: . -viewer_socket_machine machine - Machine to use for socket
6024: . -viewer_socket_port port - Port number to use for socket
6025: - -mat_view binary:filename[:append] - Save matrix to file in binary format
6027: Level: beginner
6029: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
6030: @*/
6031: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6032: {
6033: static PetscInt inassm = 0;
6034: PetscBool flg = PETSC_FALSE;
6036: PetscFunctionBegin;
6040: inassm++;
6041: MatAssemblyEnd_InUse++;
6042: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6043: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6044: PetscTryTypeMethod(mat, assemblyend, type);
6045: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6046: } else PetscTryTypeMethod(mat, assemblyend, type);
6048: /* Flush assembly is not a true assembly */
6049: if (type != MAT_FLUSH_ASSEMBLY) {
6050: if (mat->num_ass) {
6051: if (!mat->symmetry_eternal) {
6052: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6053: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6054: }
6055: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6056: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6057: }
6058: mat->num_ass++;
6059: mat->assembled = PETSC_TRUE;
6060: mat->ass_nonzerostate = mat->nonzerostate;
6061: }
6063: mat->insertmode = NOT_SET_VALUES;
6064: MatAssemblyEnd_InUse--;
6065: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6066: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6067: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6069: if (mat->checksymmetryonassembly) {
6070: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6071: if (flg) {
6072: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6073: } else {
6074: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6075: }
6076: }
6077: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6078: }
6079: inassm--;
6080: PetscFunctionReturn(PETSC_SUCCESS);
6081: }
6083: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6084: /*@
6085: MatSetOption - Sets a parameter option for a matrix. Some options
6086: may be specific to certain storage formats. Some options
6087: determine how values will be inserted (or added). Sorted,
6088: row-oriented input will generally assemble the fastest. The default
6089: is row-oriented.
6091: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6093: Input Parameters:
6094: + mat - the matrix
6095: . op - the option, one of those listed below (and possibly others),
6096: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6098: Options Describing Matrix Structure:
6099: + `MAT_SPD` - symmetric positive definite
6100: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6101: . `MAT_HERMITIAN` - transpose is the complex conjugation
6102: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6103: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6104: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6105: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6107: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6108: do not need to be computed (usually at a high cost)
6110: Options For Use with `MatSetValues()`:
6111: Insert a logically dense subblock, which can be
6112: . `MAT_ROW_ORIENTED` - row-oriented (default)
6114: These options reflect the data you pass in with `MatSetValues()`; it has
6115: nothing to do with how the data is stored internally in the matrix
6116: data structure.
6118: When (re)assembling a matrix, we can restrict the input for
6119: efficiency/debugging purposes. These options include
6120: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6121: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6122: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
6123: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6124: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6125: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6126: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6127: performance for very large process counts.
6128: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6129: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6130: functions, instead sending only neighbor messages.
6132: Level: intermediate
6134: Notes:
6135: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6137: Some options are relevant only for particular matrix types and
6138: are thus ignored by others. Other options are not supported by
6139: certain matrix types and will generate an error message if set.
6141: If using Fortran to compute a matrix, one may need to
6142: use the column-oriented option (or convert to the row-oriented
6143: format).
6145: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6146: that would generate a new entry in the nonzero structure is instead
6147: ignored. Thus, if memory has not already been allocated for this particular
6148: data, then the insertion is ignored. For dense matrices, in which
6149: the entire array is allocated, no entries are ever ignored.
6150: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6152: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6153: that would generate a new entry in the nonzero structure instead produces
6154: 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
6156: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6157: that would generate a new entry that has not been preallocated will
6158: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6159: only.) This is a useful flag when debugging matrix memory preallocation.
6160: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6162: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6163: other processors should be dropped, rather than stashed.
6164: This is useful if you know that the "owning" processor is also
6165: always generating the correct matrix entries, so that PETSc need
6166: not transfer duplicate entries generated on another processor.
6168: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6169: searches during matrix assembly. When this flag is set, the hash table
6170: is created during the first matrix assembly. This hash table is
6171: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6172: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6173: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6174: supported by `MATMPIBAIJ` format only.
6176: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6177: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6179: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6180: a zero location in the matrix
6182: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6184: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6185: zero row routines and thus improves performance for very large process counts.
6187: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6188: part of the matrix (since they should match the upper triangular part).
6190: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6191: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6192: with finite difference schemes with non-periodic boundary conditions.
6194: Developer Note:
6195: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6196: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6197: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6198: not changed.
6200: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6201: @*/
6202: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6203: {
6204: PetscFunctionBegin;
6206: if (op > 0) {
6209: }
6211: 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);
6213: switch (op) {
6214: case MAT_FORCE_DIAGONAL_ENTRIES:
6215: mat->force_diagonals = flg;
6216: PetscFunctionReturn(PETSC_SUCCESS);
6217: case MAT_NO_OFF_PROC_ENTRIES:
6218: mat->nooffprocentries = flg;
6219: PetscFunctionReturn(PETSC_SUCCESS);
6220: case MAT_SUBSET_OFF_PROC_ENTRIES:
6221: mat->assembly_subset = flg;
6222: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6223: #if !defined(PETSC_HAVE_MPIUNI)
6224: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6225: #endif
6226: mat->stash.first_assembly_done = PETSC_FALSE;
6227: }
6228: PetscFunctionReturn(PETSC_SUCCESS);
6229: case MAT_NO_OFF_PROC_ZERO_ROWS:
6230: mat->nooffproczerorows = flg;
6231: PetscFunctionReturn(PETSC_SUCCESS);
6232: case MAT_SPD:
6233: if (flg) {
6234: mat->spd = PETSC_BOOL3_TRUE;
6235: mat->symmetric = PETSC_BOOL3_TRUE;
6236: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6237: #if !defined(PETSC_USE_COMPLEX)
6238: mat->hermitian = PETSC_BOOL3_TRUE;
6239: #endif
6240: } else {
6241: mat->spd = PETSC_BOOL3_FALSE;
6242: }
6243: break;
6244: case MAT_SYMMETRIC:
6245: mat->symmetric = PetscBoolToBool3(flg);
6246: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6247: #if !defined(PETSC_USE_COMPLEX)
6248: mat->hermitian = PetscBoolToBool3(flg);
6249: #endif
6250: break;
6251: case MAT_HERMITIAN:
6252: mat->hermitian = PetscBoolToBool3(flg);
6253: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6254: #if !defined(PETSC_USE_COMPLEX)
6255: mat->symmetric = PetscBoolToBool3(flg);
6256: #endif
6257: break;
6258: case MAT_STRUCTURALLY_SYMMETRIC:
6259: mat->structurally_symmetric = PetscBoolToBool3(flg);
6260: break;
6261: case MAT_SYMMETRY_ETERNAL:
6262: 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");
6263: mat->symmetry_eternal = flg;
6264: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6265: break;
6266: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6267: 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");
6268: mat->structural_symmetry_eternal = flg;
6269: break;
6270: case MAT_SPD_ETERNAL:
6271: 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");
6272: mat->spd_eternal = flg;
6273: if (flg) {
6274: mat->structural_symmetry_eternal = PETSC_TRUE;
6275: mat->symmetry_eternal = PETSC_TRUE;
6276: }
6277: break;
6278: case MAT_STRUCTURE_ONLY:
6279: mat->structure_only = flg;
6280: break;
6281: case MAT_SORTED_FULL:
6282: mat->sortedfull = flg;
6283: break;
6284: default:
6285: break;
6286: }
6287: PetscTryTypeMethod(mat, setoption, op, flg);
6288: PetscFunctionReturn(PETSC_SUCCESS);
6289: }
6291: /*@
6292: MatGetOption - Gets a parameter option that has been set for a matrix.
6294: Logically Collective
6296: Input Parameters:
6297: + mat - the matrix
6298: - op - the option, this only responds to certain options, check the code for which ones
6300: Output Parameter:
6301: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6303: Level: intermediate
6305: Notes:
6306: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6308: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6309: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6311: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6312: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6313: @*/
6314: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6315: {
6316: PetscFunctionBegin;
6320: 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);
6321: 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()");
6323: switch (op) {
6324: case MAT_NO_OFF_PROC_ENTRIES:
6325: *flg = mat->nooffprocentries;
6326: break;
6327: case MAT_NO_OFF_PROC_ZERO_ROWS:
6328: *flg = mat->nooffproczerorows;
6329: break;
6330: case MAT_SYMMETRIC:
6331: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6332: break;
6333: case MAT_HERMITIAN:
6334: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6335: break;
6336: case MAT_STRUCTURALLY_SYMMETRIC:
6337: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6338: break;
6339: case MAT_SPD:
6340: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6341: break;
6342: case MAT_SYMMETRY_ETERNAL:
6343: *flg = mat->symmetry_eternal;
6344: break;
6345: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6346: *flg = mat->symmetry_eternal;
6347: break;
6348: default:
6349: break;
6350: }
6351: PetscFunctionReturn(PETSC_SUCCESS);
6352: }
6354: /*@
6355: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6356: this routine retains the old nonzero structure.
6358: Logically Collective
6360: Input Parameter:
6361: . mat - the matrix
6363: Level: intermediate
6365: Note:
6366: 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.
6367: See the Performance chapter of the users manual for information on preallocating matrices.
6369: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6370: @*/
6371: PetscErrorCode MatZeroEntries(Mat mat)
6372: {
6373: PetscFunctionBegin;
6376: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6377: 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");
6378: MatCheckPreallocated(mat, 1);
6380: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6381: PetscUseTypeMethod(mat, zeroentries);
6382: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6383: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6384: PetscFunctionReturn(PETSC_SUCCESS);
6385: }
6387: /*@
6388: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6389: of a set of rows and columns of a matrix.
6391: Collective
6393: Input Parameters:
6394: + mat - the matrix
6395: . numRows - the number of rows/columns to zero
6396: . rows - the global row indices
6397: . diag - value put in the diagonal of the eliminated rows
6398: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6399: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6401: Level: intermediate
6403: Notes:
6404: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6406: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6407: 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
6409: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6410: Krylov method to take advantage of the known solution on the zeroed rows.
6412: For the parallel case, all processes that share the matrix (i.e.,
6413: those in the communicator used for matrix creation) MUST call this
6414: routine, regardless of whether any rows being zeroed are owned by
6415: them.
6417: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6418: 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
6419: missing.
6421: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6422: list only rows local to itself).
6424: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6426: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6427: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6428: @*/
6429: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6430: {
6431: PetscFunctionBegin;
6434: if (numRows) PetscAssertPointer(rows, 3);
6435: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6436: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6437: MatCheckPreallocated(mat, 1);
6439: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6440: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6441: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6442: PetscFunctionReturn(PETSC_SUCCESS);
6443: }
6445: /*@
6446: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6447: of a set of rows and columns of a matrix.
6449: Collective
6451: Input Parameters:
6452: + mat - the matrix
6453: . is - the rows to zero
6454: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6455: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6456: - b - optional vector of right-hand side, that will be adjusted by provided solution
6458: Level: intermediate
6460: Note:
6461: See `MatZeroRowsColumns()` for details on how this routine operates.
6463: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6464: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6465: @*/
6466: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6467: {
6468: PetscInt numRows;
6469: const PetscInt *rows;
6471: PetscFunctionBegin;
6476: PetscCall(ISGetLocalSize(is, &numRows));
6477: PetscCall(ISGetIndices(is, &rows));
6478: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6479: PetscCall(ISRestoreIndices(is, &rows));
6480: PetscFunctionReturn(PETSC_SUCCESS);
6481: }
6483: /*@
6484: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6485: of a set of rows of a matrix.
6487: Collective
6489: Input Parameters:
6490: + mat - the matrix
6491: . numRows - the number of rows to zero
6492: . rows - the global row indices
6493: . diag - value put in the diagonal of the zeroed rows
6494: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6495: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6497: Level: intermediate
6499: Notes:
6500: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6502: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6504: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6505: Krylov method to take advantage of the known solution on the zeroed rows.
6507: 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)
6508: from the matrix.
6510: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6511: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6512: formats this does not alter the nonzero structure.
6514: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6515: of the matrix is not changed the values are
6516: merely zeroed.
6518: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6519: formats can optionally remove the main diagonal entry from the
6520: nonzero structure as well, by passing 0.0 as the final argument).
6522: For the parallel case, all processes that share the matrix (i.e.,
6523: those in the communicator used for matrix creation) MUST call this
6524: routine, regardless of whether any rows being zeroed are owned by
6525: them.
6527: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6528: list only rows local to itself).
6530: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6531: owns that are to be zeroed. This saves a global synchronization in the implementation.
6533: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6534: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6535: @*/
6536: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6537: {
6538: PetscFunctionBegin;
6541: if (numRows) PetscAssertPointer(rows, 3);
6542: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6543: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6544: MatCheckPreallocated(mat, 1);
6546: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6547: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6548: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6549: PetscFunctionReturn(PETSC_SUCCESS);
6550: }
6552: /*@
6553: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6554: of a set of rows of a matrix indicated by an `IS`
6556: Collective
6558: Input Parameters:
6559: + mat - the matrix
6560: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6561: . diag - value put in all diagonals of eliminated rows
6562: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6563: - b - optional vector of right-hand side, that will be adjusted by provided solution
6565: Level: intermediate
6567: Note:
6568: See `MatZeroRows()` for details on how this routine operates.
6570: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6571: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6572: @*/
6573: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6574: {
6575: PetscInt numRows = 0;
6576: const PetscInt *rows = NULL;
6578: PetscFunctionBegin;
6581: if (is) {
6583: PetscCall(ISGetLocalSize(is, &numRows));
6584: PetscCall(ISGetIndices(is, &rows));
6585: }
6586: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6587: if (is) PetscCall(ISRestoreIndices(is, &rows));
6588: PetscFunctionReturn(PETSC_SUCCESS);
6589: }
6591: /*@
6592: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6593: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6595: Collective
6597: Input Parameters:
6598: + mat - the matrix
6599: . numRows - the number of rows to remove
6600: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6601: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6602: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6603: - b - optional vector of right-hand side, that will be adjusted by provided solution
6605: Level: intermediate
6607: Notes:
6608: See `MatZeroRows()` for details on how this routine operates.
6610: The grid coordinates are across the entire grid, not just the local portion
6612: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6613: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6614: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6615: `DM_BOUNDARY_PERIODIC` boundary type.
6617: 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
6618: a single value per point) you can skip filling those indices.
6620: Fortran Note:
6621: `idxm` and `idxn` should be declared as
6622: .vb
6623: MatStencil idxm(4, m)
6624: .ve
6625: and the values inserted using
6626: .vb
6627: idxm(MatStencil_i, 1) = i
6628: idxm(MatStencil_j, 1) = j
6629: idxm(MatStencil_k, 1) = k
6630: idxm(MatStencil_c, 1) = c
6631: etc
6632: .ve
6634: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6635: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6636: @*/
6637: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6638: {
6639: PetscInt dim = mat->stencil.dim;
6640: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6641: PetscInt *dims = mat->stencil.dims + 1;
6642: PetscInt *starts = mat->stencil.starts;
6643: PetscInt *dxm = (PetscInt *)rows;
6644: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6646: PetscFunctionBegin;
6649: if (numRows) PetscAssertPointer(rows, 3);
6651: PetscCall(PetscMalloc1(numRows, &jdxm));
6652: for (i = 0; i < numRows; ++i) {
6653: /* Skip unused dimensions (they are ordered k, j, i, c) */
6654: for (j = 0; j < 3 - sdim; ++j) dxm++;
6655: /* Local index in X dir */
6656: tmp = *dxm++ - starts[0];
6657: /* Loop over remaining dimensions */
6658: for (j = 0; j < dim - 1; ++j) {
6659: /* If nonlocal, set index to be negative */
6660: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6661: /* Update local index */
6662: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6663: }
6664: /* Skip component slot if necessary */
6665: if (mat->stencil.noc) dxm++;
6666: /* Local row number */
6667: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6668: }
6669: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6670: PetscCall(PetscFree(jdxm));
6671: PetscFunctionReturn(PETSC_SUCCESS);
6672: }
6674: /*@
6675: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6676: of a set of rows and columns of a matrix.
6678: Collective
6680: Input Parameters:
6681: + mat - the matrix
6682: . numRows - the number of rows/columns to remove
6683: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6684: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6685: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6686: - b - optional vector of right-hand side, that will be adjusted by provided solution
6688: Level: intermediate
6690: Notes:
6691: See `MatZeroRowsColumns()` for details on how this routine operates.
6693: The grid coordinates are across the entire grid, not just the local portion
6695: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6696: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6697: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6698: `DM_BOUNDARY_PERIODIC` boundary type.
6700: 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
6701: a single value per point) you can skip filling those indices.
6703: Fortran Note:
6704: `idxm` and `idxn` should be declared as
6705: .vb
6706: MatStencil idxm(4, m)
6707: .ve
6708: and the values inserted using
6709: .vb
6710: idxm(MatStencil_i, 1) = i
6711: idxm(MatStencil_j, 1) = j
6712: idxm(MatStencil_k, 1) = k
6713: idxm(MatStencil_c, 1) = c
6714: etc
6715: .ve
6717: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6718: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6719: @*/
6720: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6721: {
6722: PetscInt dim = mat->stencil.dim;
6723: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6724: PetscInt *dims = mat->stencil.dims + 1;
6725: PetscInt *starts = mat->stencil.starts;
6726: PetscInt *dxm = (PetscInt *)rows;
6727: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6729: PetscFunctionBegin;
6732: if (numRows) PetscAssertPointer(rows, 3);
6734: PetscCall(PetscMalloc1(numRows, &jdxm));
6735: for (i = 0; i < numRows; ++i) {
6736: /* Skip unused dimensions (they are ordered k, j, i, c) */
6737: for (j = 0; j < 3 - sdim; ++j) dxm++;
6738: /* Local index in X dir */
6739: tmp = *dxm++ - starts[0];
6740: /* Loop over remaining dimensions */
6741: for (j = 0; j < dim - 1; ++j) {
6742: /* If nonlocal, set index to be negative */
6743: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6744: /* Update local index */
6745: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6746: }
6747: /* Skip component slot if necessary */
6748: if (mat->stencil.noc) dxm++;
6749: /* Local row number */
6750: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6751: }
6752: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6753: PetscCall(PetscFree(jdxm));
6754: PetscFunctionReturn(PETSC_SUCCESS);
6755: }
6757: /*@
6758: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6759: of a set of rows of a matrix; using local numbering of rows.
6761: Collective
6763: Input Parameters:
6764: + mat - the matrix
6765: . numRows - the number of rows to remove
6766: . rows - the local row indices
6767: . diag - value put in all diagonals of eliminated rows
6768: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6769: - b - optional vector of right-hand side, that will be adjusted by provided solution
6771: Level: intermediate
6773: Notes:
6774: Before calling `MatZeroRowsLocal()`, the user must first set the
6775: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6777: See `MatZeroRows()` for details on how this routine operates.
6779: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6780: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6781: @*/
6782: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6783: {
6784: PetscFunctionBegin;
6787: if (numRows) PetscAssertPointer(rows, 3);
6788: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6789: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6790: MatCheckPreallocated(mat, 1);
6792: if (mat->ops->zerorowslocal) {
6793: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6794: } else {
6795: IS is, newis;
6796: PetscInt *newRows, nl = 0;
6798: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6799: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6800: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6801: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6802: for (PetscInt i = 0; i < numRows; i++)
6803: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6804: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6805: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6806: PetscCall(ISDestroy(&newis));
6807: PetscCall(ISDestroy(&is));
6808: }
6809: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6810: PetscFunctionReturn(PETSC_SUCCESS);
6811: }
6813: /*@
6814: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6815: of a set of rows of a matrix; using local numbering of rows.
6817: Collective
6819: Input Parameters:
6820: + mat - the matrix
6821: . is - index set of rows to remove
6822: . diag - value put in all diagonals of eliminated rows
6823: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6824: - b - optional vector of right-hand side, that will be adjusted by provided solution
6826: Level: intermediate
6828: Notes:
6829: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6830: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6832: See `MatZeroRows()` for details on how this routine operates.
6834: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6835: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6836: @*/
6837: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6838: {
6839: PetscInt numRows;
6840: const PetscInt *rows;
6842: PetscFunctionBegin;
6846: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6847: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6848: MatCheckPreallocated(mat, 1);
6850: PetscCall(ISGetLocalSize(is, &numRows));
6851: PetscCall(ISGetIndices(is, &rows));
6852: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6853: PetscCall(ISRestoreIndices(is, &rows));
6854: PetscFunctionReturn(PETSC_SUCCESS);
6855: }
6857: /*@
6858: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6859: of a set of rows and columns of a matrix; using local numbering of rows.
6861: Collective
6863: Input Parameters:
6864: + mat - the matrix
6865: . numRows - the number of rows to remove
6866: . rows - the global row indices
6867: . diag - value put in all diagonals of eliminated rows
6868: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6869: - b - optional vector of right-hand side, that will be adjusted by provided solution
6871: Level: intermediate
6873: Notes:
6874: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6875: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6877: See `MatZeroRowsColumns()` for details on how this routine operates.
6879: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6880: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6881: @*/
6882: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6883: {
6884: PetscFunctionBegin;
6887: if (numRows) PetscAssertPointer(rows, 3);
6888: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6889: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6890: MatCheckPreallocated(mat, 1);
6892: if (mat->ops->zerorowscolumnslocal) {
6893: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6894: } else {
6895: IS is, newis;
6896: PetscInt *newRows, nl = 0;
6898: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6899: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6900: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6901: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6902: for (PetscInt i = 0; i < numRows; i++)
6903: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6904: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6905: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6906: PetscCall(ISDestroy(&newis));
6907: PetscCall(ISDestroy(&is));
6908: }
6909: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6910: PetscFunctionReturn(PETSC_SUCCESS);
6911: }
6913: /*@
6914: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6915: of a set of rows and columns of a matrix; using local numbering of rows.
6917: Collective
6919: Input Parameters:
6920: + mat - the matrix
6921: . is - index set of rows to remove
6922: . diag - value put in all diagonals of eliminated rows
6923: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6924: - b - optional vector of right-hand side, that will be adjusted by provided solution
6926: Level: intermediate
6928: Notes:
6929: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6930: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6932: See `MatZeroRowsColumns()` for details on how this routine operates.
6934: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6935: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6936: @*/
6937: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6938: {
6939: PetscInt numRows;
6940: const PetscInt *rows;
6942: PetscFunctionBegin;
6946: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6947: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6948: MatCheckPreallocated(mat, 1);
6950: PetscCall(ISGetLocalSize(is, &numRows));
6951: PetscCall(ISGetIndices(is, &rows));
6952: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6953: PetscCall(ISRestoreIndices(is, &rows));
6954: PetscFunctionReturn(PETSC_SUCCESS);
6955: }
6957: /*@
6958: MatGetSize - Returns the numbers of rows and columns in a matrix.
6960: Not Collective
6962: Input Parameter:
6963: . mat - the matrix
6965: Output Parameters:
6966: + m - the number of global rows
6967: - n - the number of global columns
6969: Level: beginner
6971: Note:
6972: Both output parameters can be `NULL` on input.
6974: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6975: @*/
6976: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6977: {
6978: PetscFunctionBegin;
6980: if (m) *m = mat->rmap->N;
6981: if (n) *n = mat->cmap->N;
6982: PetscFunctionReturn(PETSC_SUCCESS);
6983: }
6985: /*@
6986: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6987: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6989: Not Collective
6991: Input Parameter:
6992: . mat - the matrix
6994: Output Parameters:
6995: + m - the number of local rows, use `NULL` to not obtain this value
6996: - n - the number of local columns, use `NULL` to not obtain this value
6998: Level: beginner
7000: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
7001: @*/
7002: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
7003: {
7004: PetscFunctionBegin;
7006: if (m) PetscAssertPointer(m, 2);
7007: if (n) PetscAssertPointer(n, 3);
7008: if (m) *m = mat->rmap->n;
7009: if (n) *n = mat->cmap->n;
7010: PetscFunctionReturn(PETSC_SUCCESS);
7011: }
7013: /*@
7014: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
7015: vector one multiplies this matrix by that are owned by this processor.
7017: Not Collective, unless matrix has not been allocated, then collective
7019: Input Parameter:
7020: . mat - the matrix
7022: Output Parameters:
7023: + m - the global index of the first local column, use `NULL` to not obtain this value
7024: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
7026: Level: developer
7028: Notes:
7029: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7031: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7032: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7034: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7035: the local values in the matrix.
7037: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7038: Layouts](sec_matlayout) for details on matrix layouts.
7040: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7041: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7042: @*/
7043: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7044: {
7045: PetscFunctionBegin;
7048: if (m) PetscAssertPointer(m, 2);
7049: if (n) PetscAssertPointer(n, 3);
7050: MatCheckPreallocated(mat, 1);
7051: if (m) *m = mat->cmap->rstart;
7052: if (n) *n = mat->cmap->rend;
7053: PetscFunctionReturn(PETSC_SUCCESS);
7054: }
7056: /*@
7057: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7058: this MPI process.
7060: Not Collective
7062: Input Parameter:
7063: . mat - the matrix
7065: Output Parameters:
7066: + m - the global index of the first local row, use `NULL` to not obtain this value
7067: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7069: Level: beginner
7071: Notes:
7072: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7074: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7075: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7077: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7078: the local values in the matrix.
7080: The high argument is one more than the last element stored locally.
7082: For all matrices it returns the range of matrix rows associated with rows of a vector that
7083: would contain the result of a matrix vector product with this matrix. See [Matrix
7084: Layouts](sec_matlayout) for details on matrix layouts.
7086: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7087: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7088: @*/
7089: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7090: {
7091: PetscFunctionBegin;
7094: if (m) PetscAssertPointer(m, 2);
7095: if (n) PetscAssertPointer(n, 3);
7096: MatCheckPreallocated(mat, 1);
7097: if (m) *m = mat->rmap->rstart;
7098: if (n) *n = mat->rmap->rend;
7099: PetscFunctionReturn(PETSC_SUCCESS);
7100: }
7102: /*@C
7103: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7104: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7106: Not Collective, unless matrix has not been allocated
7108: Input Parameter:
7109: . mat - the matrix
7111: Output Parameter:
7112: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
7113: where `size` is the number of MPI processes used by `mat`
7115: Level: beginner
7117: Notes:
7118: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7120: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7121: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7123: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7124: the local values in the matrix.
7126: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7127: would contain the result of a matrix vector product with this matrix. See [Matrix
7128: Layouts](sec_matlayout) for details on matrix layouts.
7130: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7131: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7132: `DMDAGetGhostCorners()`, `DM`
7133: @*/
7134: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7135: {
7136: PetscFunctionBegin;
7139: MatCheckPreallocated(mat, 1);
7140: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7141: PetscFunctionReturn(PETSC_SUCCESS);
7142: }
7144: /*@C
7145: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7146: vector one multiplies this vector by that are owned by each processor.
7148: Not Collective, unless matrix has not been allocated
7150: Input Parameter:
7151: . mat - the matrix
7153: Output Parameter:
7154: . ranges - start of each processors portion plus one more than the total length at the end
7156: Level: beginner
7158: Notes:
7159: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7161: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7162: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7164: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7165: the local values in the matrix.
7167: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7168: Layouts](sec_matlayout) for details on matrix layouts.
7170: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7171: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7172: `DMDAGetGhostCorners()`, `DM`
7173: @*/
7174: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7175: {
7176: PetscFunctionBegin;
7179: MatCheckPreallocated(mat, 1);
7180: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7181: PetscFunctionReturn(PETSC_SUCCESS);
7182: }
7184: /*@
7185: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7187: Not Collective
7189: Input Parameter:
7190: . A - matrix
7192: Output Parameters:
7193: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7194: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7196: Level: intermediate
7198: Note:
7199: You should call `ISDestroy()` on the returned `IS`
7201: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7202: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7203: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7204: details on matrix layouts.
7206: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7207: @*/
7208: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7209: {
7210: PetscErrorCode (*f)(Mat, IS *, IS *);
7212: PetscFunctionBegin;
7215: MatCheckPreallocated(A, 1);
7216: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7217: if (f) {
7218: PetscCall((*f)(A, rows, cols));
7219: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7220: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7221: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7222: }
7223: PetscFunctionReturn(PETSC_SUCCESS);
7224: }
7226: /*@
7227: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7228: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7229: to complete the factorization.
7231: Collective
7233: Input Parameters:
7234: + fact - the factorized matrix obtained with `MatGetFactor()`
7235: . mat - the matrix
7236: . row - row permutation
7237: . col - column permutation
7238: - info - structure containing
7239: .vb
7240: levels - number of levels of fill.
7241: expected fill - as ratio of original fill.
7242: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7243: missing diagonal entries)
7244: .ve
7246: Level: developer
7248: Notes:
7249: See [Matrix Factorization](sec_matfactor) for additional information.
7251: Most users should employ the `KSP` interface for linear solvers
7252: instead of working directly with matrix algebra routines such as this.
7253: See, e.g., `KSPCreate()`.
7255: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7257: Fortran Note:
7258: A valid (non-null) `info` argument must be provided
7260: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7261: `MatGetOrdering()`, `MatFactorInfo`
7262: @*/
7263: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7264: {
7265: PetscFunctionBegin;
7270: PetscAssertPointer(info, 5);
7271: PetscAssertPointer(fact, 1);
7272: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7273: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7274: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7275: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7276: MatCheckPreallocated(mat, 2);
7278: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7279: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7280: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7281: PetscFunctionReturn(PETSC_SUCCESS);
7282: }
7284: /*@
7285: MatICCFactorSymbolic - Performs symbolic incomplete
7286: Cholesky factorization for a symmetric matrix. Use
7287: `MatCholeskyFactorNumeric()` to complete the factorization.
7289: Collective
7291: Input Parameters:
7292: + fact - the factorized matrix obtained with `MatGetFactor()`
7293: . mat - the matrix to be factored
7294: . perm - row and column permutation
7295: - info - structure containing
7296: .vb
7297: levels - number of levels of fill.
7298: expected fill - as ratio of original fill.
7299: .ve
7301: Level: developer
7303: Notes:
7304: Most users should employ the `KSP` interface for linear solvers
7305: instead of working directly with matrix algebra routines such as this.
7306: See, e.g., `KSPCreate()`.
7308: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7310: Fortran Note:
7311: A valid (non-null) `info` argument must be provided
7313: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7314: @*/
7315: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7316: {
7317: PetscFunctionBegin;
7321: PetscAssertPointer(info, 4);
7322: PetscAssertPointer(fact, 1);
7323: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7324: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7325: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7326: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7327: MatCheckPreallocated(mat, 2);
7329: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7330: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7331: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7332: PetscFunctionReturn(PETSC_SUCCESS);
7333: }
7335: /*@C
7336: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7337: points to an array of valid matrices, they may be reused to store the new
7338: submatrices.
7340: Collective
7342: Input Parameters:
7343: + mat - the matrix
7344: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7345: . irow - index set of rows to extract
7346: . icol - index set of columns to extract
7347: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7349: Output Parameter:
7350: . submat - the array of submatrices
7352: Level: advanced
7354: Notes:
7355: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7356: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7357: to extract a parallel submatrix.
7359: Some matrix types place restrictions on the row and column
7360: indices, such as that they be sorted or that they be equal to each other.
7362: The index sets may not have duplicate entries.
7364: When extracting submatrices from a parallel matrix, each processor can
7365: form a different submatrix by setting the rows and columns of its
7366: individual index sets according to the local submatrix desired.
7368: When finished using the submatrices, the user should destroy
7369: them with `MatDestroySubMatrices()`.
7371: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7372: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7374: This routine creates the matrices in submat; you should NOT create them before
7375: calling it. It also allocates the array of matrix pointers submat.
7377: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7378: request one row/column in a block, they must request all rows/columns that are in
7379: that block. For example, if the block size is 2 you cannot request just row 0 and
7380: column 0.
7382: Fortran Note:
7383: .vb
7384: Mat, pointer :: submat(:)
7385: .ve
7387: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7388: @*/
7389: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7390: {
7391: PetscInt i;
7392: PetscBool eq;
7394: PetscFunctionBegin;
7397: if (n) {
7398: PetscAssertPointer(irow, 3);
7400: PetscAssertPointer(icol, 4);
7402: }
7403: PetscAssertPointer(submat, 6);
7404: if (n && scall == MAT_REUSE_MATRIX) {
7405: PetscAssertPointer(*submat, 6);
7407: }
7408: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7409: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7410: MatCheckPreallocated(mat, 1);
7411: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7412: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7413: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7414: for (i = 0; i < n; i++) {
7415: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7416: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7417: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7418: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7419: if (mat->boundtocpu && mat->bindingpropagates) {
7420: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7421: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7422: }
7423: #endif
7424: }
7425: PetscFunctionReturn(PETSC_SUCCESS);
7426: }
7428: /*@C
7429: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7431: Collective
7433: Input Parameters:
7434: + mat - the matrix
7435: . n - the number of submatrixes to be extracted
7436: . irow - index set of rows to extract
7437: . icol - index set of columns to extract
7438: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7440: Output Parameter:
7441: . submat - the array of submatrices
7443: Level: advanced
7445: Note:
7446: This is used by `PCGASM`
7448: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7449: @*/
7450: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7451: {
7452: PetscInt i;
7453: PetscBool eq;
7455: PetscFunctionBegin;
7458: if (n) {
7459: PetscAssertPointer(irow, 3);
7461: PetscAssertPointer(icol, 4);
7463: }
7464: PetscAssertPointer(submat, 6);
7465: if (n && scall == MAT_REUSE_MATRIX) {
7466: PetscAssertPointer(*submat, 6);
7468: }
7469: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7470: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7471: MatCheckPreallocated(mat, 1);
7473: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7474: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7475: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7476: for (i = 0; i < n; i++) {
7477: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7478: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7479: }
7480: PetscFunctionReturn(PETSC_SUCCESS);
7481: }
7483: /*@C
7484: MatDestroyMatrices - Destroys an array of matrices
7486: Collective
7488: Input Parameters:
7489: + n - the number of local matrices
7490: - mat - the matrices (this is a pointer to the array of matrices)
7492: Level: advanced
7494: Notes:
7495: Frees not only the matrices, but also the array that contains the matrices
7497: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7499: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7500: @*/
7501: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7502: {
7503: PetscInt i;
7505: PetscFunctionBegin;
7506: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7507: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7508: PetscAssertPointer(mat, 2);
7510: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7512: /* memory is allocated even if n = 0 */
7513: PetscCall(PetscFree(*mat));
7514: PetscFunctionReturn(PETSC_SUCCESS);
7515: }
7517: /*@C
7518: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7520: Collective
7522: Input Parameters:
7523: + n - the number of local matrices
7524: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7526: Level: advanced
7528: Note:
7529: Frees not only the matrices, but also the array that contains the matrices
7531: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7532: @*/
7533: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7534: {
7535: Mat mat0;
7537: PetscFunctionBegin;
7538: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7539: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7540: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7541: PetscAssertPointer(mat, 2);
7543: mat0 = (*mat)[0];
7544: if (mat0 && mat0->ops->destroysubmatrices) {
7545: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7546: } else {
7547: PetscCall(MatDestroyMatrices(n, mat));
7548: }
7549: PetscFunctionReturn(PETSC_SUCCESS);
7550: }
7552: /*@
7553: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7555: Collective
7557: Input Parameter:
7558: . mat - the matrix
7560: Output Parameter:
7561: . matstruct - the sequential matrix with the nonzero structure of `mat`
7563: Level: developer
7565: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7566: @*/
7567: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7568: {
7569: PetscFunctionBegin;
7571: PetscAssertPointer(matstruct, 2);
7574: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7575: MatCheckPreallocated(mat, 1);
7577: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7578: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7579: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7580: PetscFunctionReturn(PETSC_SUCCESS);
7581: }
7583: /*@C
7584: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7586: Collective
7588: Input Parameter:
7589: . mat - the matrix
7591: Level: advanced
7593: Note:
7594: This is not needed, one can just call `MatDestroy()`
7596: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7597: @*/
7598: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7599: {
7600: PetscFunctionBegin;
7601: PetscAssertPointer(mat, 1);
7602: PetscCall(MatDestroy(mat));
7603: PetscFunctionReturn(PETSC_SUCCESS);
7604: }
7606: /*@
7607: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7608: replaces the index sets by larger ones that represent submatrices with
7609: additional overlap.
7611: Collective
7613: Input Parameters:
7614: + mat - the matrix
7615: . n - the number of index sets
7616: . is - the array of index sets (these index sets will changed during the call)
7617: - ov - the additional overlap requested
7619: Options Database Key:
7620: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7622: Level: developer
7624: Note:
7625: The computed overlap preserves the matrix block sizes when the blocks are square.
7626: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7627: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7629: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7630: @*/
7631: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7632: {
7633: PetscInt i, bs, cbs;
7635: PetscFunctionBegin;
7639: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7640: if (n) {
7641: PetscAssertPointer(is, 3);
7643: }
7644: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7645: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7646: MatCheckPreallocated(mat, 1);
7648: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7649: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7650: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7651: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7652: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7653: if (bs == cbs) {
7654: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7655: }
7656: PetscFunctionReturn(PETSC_SUCCESS);
7657: }
7659: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7661: /*@
7662: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7663: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7664: additional overlap.
7666: Collective
7668: Input Parameters:
7669: + mat - the matrix
7670: . n - the number of index sets
7671: . is - the array of index sets (these index sets will changed during the call)
7672: - ov - the additional overlap requested
7674: ` Options Database Key:
7675: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7677: Level: developer
7679: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7680: @*/
7681: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7682: {
7683: PetscInt i;
7685: PetscFunctionBegin;
7688: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7689: if (n) {
7690: PetscAssertPointer(is, 3);
7692: }
7693: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7694: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7695: MatCheckPreallocated(mat, 1);
7696: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7697: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7698: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7699: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7700: PetscFunctionReturn(PETSC_SUCCESS);
7701: }
7703: /*@
7704: MatGetBlockSize - Returns the matrix block size.
7706: Not Collective
7708: Input Parameter:
7709: . mat - the matrix
7711: Output Parameter:
7712: . bs - block size
7714: Level: intermediate
7716: Notes:
7717: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7719: If the block size has not been set yet this routine returns 1.
7721: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7722: @*/
7723: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7724: {
7725: PetscFunctionBegin;
7727: PetscAssertPointer(bs, 2);
7728: *bs = mat->rmap->bs;
7729: PetscFunctionReturn(PETSC_SUCCESS);
7730: }
7732: /*@
7733: MatGetBlockSizes - Returns the matrix block row and column sizes.
7735: Not Collective
7737: Input Parameter:
7738: . mat - the matrix
7740: Output Parameters:
7741: + rbs - row block size
7742: - cbs - column block size
7744: Level: intermediate
7746: Notes:
7747: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7748: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7750: If a block size has not been set yet this routine returns 1.
7752: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7753: @*/
7754: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7755: {
7756: PetscFunctionBegin;
7758: if (rbs) PetscAssertPointer(rbs, 2);
7759: if (cbs) PetscAssertPointer(cbs, 3);
7760: if (rbs) *rbs = mat->rmap->bs;
7761: if (cbs) *cbs = mat->cmap->bs;
7762: PetscFunctionReturn(PETSC_SUCCESS);
7763: }
7765: /*@
7766: MatSetBlockSize - Sets the matrix block size.
7768: Logically Collective
7770: Input Parameters:
7771: + mat - the matrix
7772: - bs - block size
7774: Level: intermediate
7776: Notes:
7777: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7778: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7780: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7781: is compatible with the matrix local sizes.
7783: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7784: @*/
7785: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7786: {
7787: PetscFunctionBegin;
7790: PetscCall(MatSetBlockSizes(mat, bs, bs));
7791: PetscFunctionReturn(PETSC_SUCCESS);
7792: }
7794: typedef struct {
7795: PetscInt n;
7796: IS *is;
7797: Mat *mat;
7798: PetscObjectState nonzerostate;
7799: Mat C;
7800: } EnvelopeData;
7802: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
7803: {
7804: EnvelopeData *edata = *(EnvelopeData **)ptr;
7806: PetscFunctionBegin;
7807: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7808: PetscCall(PetscFree(edata->is));
7809: PetscCall(PetscFree(edata));
7810: PetscFunctionReturn(PETSC_SUCCESS);
7811: }
7813: /*@
7814: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7815: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7817: Collective
7819: Input Parameter:
7820: . mat - the matrix
7822: Level: intermediate
7824: Notes:
7825: There can be zeros within the blocks
7827: The blocks can overlap between processes, including laying on more than two processes
7829: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7830: @*/
7831: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7832: {
7833: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7834: PetscInt *diag, *odiag, sc;
7835: VecScatter scatter;
7836: PetscScalar *seqv;
7837: const PetscScalar *parv;
7838: const PetscInt *ia, *ja;
7839: PetscBool set, flag, done;
7840: Mat AA = mat, A;
7841: MPI_Comm comm;
7842: PetscMPIInt rank, size, tag;
7843: MPI_Status status;
7844: PetscContainer container;
7845: EnvelopeData *edata;
7846: Vec seq, par;
7847: IS isglobal;
7849: PetscFunctionBegin;
7851: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7852: if (!set || !flag) {
7853: /* TODO: only needs nonzero structure of transpose */
7854: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7855: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7856: }
7857: PetscCall(MatAIJGetLocalMat(AA, &A));
7858: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7859: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7861: PetscCall(MatGetLocalSize(mat, &n, NULL));
7862: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7863: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7864: PetscCallMPI(MPI_Comm_size(comm, &size));
7865: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7867: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7869: if (rank > 0) {
7870: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7871: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7872: }
7873: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7874: for (i = 0; i < n; i++) {
7875: env = PetscMax(env, ja[ia[i + 1] - 1]);
7876: II = rstart + i;
7877: if (env == II) {
7878: starts[lblocks] = tbs;
7879: sizes[lblocks++] = 1 + II - tbs;
7880: tbs = 1 + II;
7881: }
7882: }
7883: if (rank < size - 1) {
7884: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7885: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7886: }
7888: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7889: if (!set || !flag) PetscCall(MatDestroy(&AA));
7890: PetscCall(MatDestroy(&A));
7892: PetscCall(PetscNew(&edata));
7893: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7894: edata->n = lblocks;
7895: /* create IS needed for extracting blocks from the original matrix */
7896: PetscCall(PetscMalloc1(lblocks, &edata->is));
7897: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7899: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7900: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7901: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7902: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7903: PetscCall(MatSetType(edata->C, MATAIJ));
7905: /* Communicate the start and end of each row, from each block to the correct rank */
7906: /* TODO: Use PetscSF instead of VecScatter */
7907: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7908: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7909: PetscCall(VecGetArrayWrite(seq, &seqv));
7910: for (PetscInt i = 0; i < lblocks; i++) {
7911: for (PetscInt j = 0; j < sizes[i]; j++) {
7912: seqv[cnt] = starts[i];
7913: seqv[cnt + 1] = starts[i] + sizes[i];
7914: cnt += 2;
7915: }
7916: }
7917: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7918: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7919: sc -= cnt;
7920: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7921: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7922: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7923: PetscCall(ISDestroy(&isglobal));
7924: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7925: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7926: PetscCall(VecScatterDestroy(&scatter));
7927: PetscCall(VecDestroy(&seq));
7928: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7929: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7930: PetscCall(VecGetArrayRead(par, &parv));
7931: cnt = 0;
7932: PetscCall(MatGetSize(mat, NULL, &n));
7933: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7934: PetscInt start, end, d = 0, od = 0;
7936: start = (PetscInt)PetscRealPart(parv[cnt]);
7937: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7938: cnt += 2;
7940: if (start < cstart) {
7941: od += cstart - start + n - cend;
7942: d += cend - cstart;
7943: } else if (start < cend) {
7944: od += n - cend;
7945: d += cend - start;
7946: } else od += n - start;
7947: if (end <= cstart) {
7948: od -= cstart - end + n - cend;
7949: d -= cend - cstart;
7950: } else if (end < cend) {
7951: od -= n - cend;
7952: d -= cend - end;
7953: } else od -= n - end;
7955: odiag[i] = od;
7956: diag[i] = d;
7957: }
7958: PetscCall(VecRestoreArrayRead(par, &parv));
7959: PetscCall(VecDestroy(&par));
7960: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7961: PetscCall(PetscFree2(diag, odiag));
7962: PetscCall(PetscFree2(sizes, starts));
7964: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7965: PetscCall(PetscContainerSetPointer(container, edata));
7966: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7967: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7968: PetscCall(PetscObjectDereference((PetscObject)container));
7969: PetscFunctionReturn(PETSC_SUCCESS);
7970: }
7972: /*@
7973: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7975: Collective
7977: Input Parameters:
7978: + A - the matrix
7979: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7981: Output Parameter:
7982: . C - matrix with inverted block diagonal of `A`
7984: Level: advanced
7986: Note:
7987: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7989: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7990: @*/
7991: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7992: {
7993: PetscContainer container;
7994: EnvelopeData *edata;
7995: PetscObjectState nonzerostate;
7997: PetscFunctionBegin;
7998: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7999: if (!container) {
8000: PetscCall(MatComputeVariableBlockEnvelope(A));
8001: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8002: }
8003: PetscCall(PetscContainerGetPointer(container, &edata));
8004: PetscCall(MatGetNonzeroState(A, &nonzerostate));
8005: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
8006: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
8008: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
8009: *C = edata->C;
8011: for (PetscInt i = 0; i < edata->n; i++) {
8012: Mat D;
8013: PetscScalar *dvalues;
8015: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
8016: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
8017: PetscCall(MatSeqDenseInvert(D));
8018: PetscCall(MatDenseGetArray(D, &dvalues));
8019: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
8020: PetscCall(MatDestroy(&D));
8021: }
8022: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
8023: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
8024: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
8025: PetscFunctionReturn(PETSC_SUCCESS);
8026: }
8028: /*@
8029: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8031: Not Collective
8033: Input Parameters:
8034: + mat - the matrix
8035: . nblocks - the number of blocks on this process, each block can only exist on a single process
8036: - bsizes - the block sizes
8038: Level: intermediate
8040: Notes:
8041: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8043: 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.
8045: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8046: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8047: @*/
8048: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8049: {
8050: PetscInt ncnt = 0, nlocal;
8052: PetscFunctionBegin;
8054: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8055: 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);
8056: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8057: 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);
8058: PetscCall(PetscFree(mat->bsizes));
8059: mat->nblocks = nblocks;
8060: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8061: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8062: PetscFunctionReturn(PETSC_SUCCESS);
8063: }
8065: /*@C
8066: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8068: Not Collective; No Fortran Support
8070: Input Parameter:
8071: . mat - the matrix
8073: Output Parameters:
8074: + nblocks - the number of blocks on this process
8075: - bsizes - the block sizes
8077: Level: intermediate
8079: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8080: @*/
8081: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8082: {
8083: PetscFunctionBegin;
8085: if (nblocks) *nblocks = mat->nblocks;
8086: if (bsizes) *bsizes = mat->bsizes;
8087: PetscFunctionReturn(PETSC_SUCCESS);
8088: }
8090: /*@
8091: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8093: Not Collective
8095: Input Parameter:
8096: + subA - the submatrix
8097: . A - the original matrix
8098: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8100: Level: developer
8102: Notes:
8103: If the index set is not sorted or contains off-process entries, this function will do nothing.
8105: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8106: @*/
8107: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8108: {
8109: const PetscInt *rows;
8110: PetscInt n, rStart, rEnd, Nb = 0;
8111: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8113: PetscFunctionBegin;
8114: // The code for block size extraction does not support an unsorted IS
8115: if (flg) PetscCall(ISSorted(isrow, &flg));
8116: // We don't support originally off-diagonal blocks
8117: if (flg) {
8118: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8119: PetscCall(ISGetLocalSize(isrow, &n));
8120: PetscCall(ISGetIndices(isrow, &rows));
8121: for (PetscInt i = 0; i < n && flg; ++i) {
8122: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8123: }
8124: PetscCall(ISRestoreIndices(isrow, &rows));
8125: }
8126: // quiet return if we can't extract block size
8127: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8128: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8130: // extract block sizes
8131: PetscCall(ISGetIndices(isrow, &rows));
8132: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8133: PetscBool occupied = PETSC_FALSE;
8135: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8136: const PetscInt row = gr + br;
8138: if (i == n) break;
8139: if (rows[i] == row) {
8140: occupied = PETSC_TRUE;
8141: ++i;
8142: }
8143: while (i < n && rows[i] < row) ++i;
8144: }
8145: gr += A->bsizes[b];
8146: if (occupied) ++Nb;
8147: }
8148: subA->nblocks = Nb;
8149: PetscCall(PetscFree(subA->bsizes));
8150: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8151: PetscInt sb = 0;
8152: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8153: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8154: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8155: const PetscInt row = gr + br;
8157: if (i == n) break;
8158: if (rows[i] == row) {
8159: ++subA->bsizes[sb];
8160: ++i;
8161: }
8162: while (i < n && rows[i] < row) ++i;
8163: }
8164: gr += A->bsizes[b];
8165: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8166: }
8167: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8168: PetscInt nlocal, ncnt = 0;
8169: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8170: 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);
8171: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8172: 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);
8173: PetscCall(ISRestoreIndices(isrow, &rows));
8174: PetscFunctionReturn(PETSC_SUCCESS);
8175: }
8177: /*@
8178: MatSetBlockSizes - Sets the matrix block row and column sizes.
8180: Logically Collective
8182: Input Parameters:
8183: + mat - the matrix
8184: . rbs - row block size
8185: - cbs - column block size
8187: Level: intermediate
8189: Notes:
8190: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8191: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8192: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8194: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8195: are compatible with the matrix local sizes.
8197: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8199: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8200: @*/
8201: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8202: {
8203: PetscFunctionBegin;
8207: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8208: if (mat->rmap->refcnt) {
8209: ISLocalToGlobalMapping l2g = NULL;
8210: PetscLayout nmap = NULL;
8212: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8213: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8214: PetscCall(PetscLayoutDestroy(&mat->rmap));
8215: mat->rmap = nmap;
8216: mat->rmap->mapping = l2g;
8217: }
8218: if (mat->cmap->refcnt) {
8219: ISLocalToGlobalMapping l2g = NULL;
8220: PetscLayout nmap = NULL;
8222: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8223: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8224: PetscCall(PetscLayoutDestroy(&mat->cmap));
8225: mat->cmap = nmap;
8226: mat->cmap->mapping = l2g;
8227: }
8228: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8229: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8230: PetscFunctionReturn(PETSC_SUCCESS);
8231: }
8233: /*@
8234: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8236: Logically Collective
8238: Input Parameters:
8239: + mat - the matrix
8240: . fromRow - matrix from which to copy row block size
8241: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8243: Level: developer
8245: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8246: @*/
8247: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8248: {
8249: PetscFunctionBegin;
8253: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8254: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8255: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8256: PetscFunctionReturn(PETSC_SUCCESS);
8257: }
8259: /*@
8260: MatResidual - Default routine to calculate the residual r = b - Ax
8262: Collective
8264: Input Parameters:
8265: + mat - the matrix
8266: . b - the right-hand-side
8267: - x - the approximate solution
8269: Output Parameter:
8270: . r - location to store the residual
8272: Level: developer
8274: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8275: @*/
8276: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8277: {
8278: PetscFunctionBegin;
8284: MatCheckPreallocated(mat, 1);
8285: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8286: if (!mat->ops->residual) {
8287: PetscCall(MatMult(mat, x, r));
8288: PetscCall(VecAYPX(r, -1.0, b));
8289: } else {
8290: PetscUseTypeMethod(mat, residual, b, x, r);
8291: }
8292: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8293: PetscFunctionReturn(PETSC_SUCCESS);
8294: }
8296: /*@C
8297: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8299: Collective
8301: Input Parameters:
8302: + mat - the matrix
8303: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8304: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8305: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8306: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8307: always used.
8309: Output Parameters:
8310: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8311: . 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
8312: . ja - the column indices, use `NULL` if not needed
8313: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8314: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8316: Level: developer
8318: Notes:
8319: You CANNOT change any of the ia[] or ja[] values.
8321: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8323: Fortran Notes:
8324: Use
8325: .vb
8326: PetscInt, pointer :: ia(:),ja(:)
8327: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8328: ! Access the ith and jth entries via ia(i) and ja(j)
8329: .ve
8331: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8332: @*/
8333: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8334: {
8335: PetscFunctionBegin;
8338: if (n) PetscAssertPointer(n, 5);
8339: if (ia) PetscAssertPointer(ia, 6);
8340: if (ja) PetscAssertPointer(ja, 7);
8341: if (done) PetscAssertPointer(done, 8);
8342: MatCheckPreallocated(mat, 1);
8343: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8344: else {
8345: if (done) *done = PETSC_TRUE;
8346: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8347: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8348: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8349: }
8350: PetscFunctionReturn(PETSC_SUCCESS);
8351: }
8353: /*@C
8354: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8356: Collective
8358: Input Parameters:
8359: + mat - the matrix
8360: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8361: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8362: symmetrized
8363: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8364: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8365: always used.
8367: Output Parameters:
8368: + n - number of columns in the (possibly compressed) matrix
8369: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8370: . ja - the row indices
8371: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8373: Level: developer
8375: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8376: @*/
8377: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8378: {
8379: PetscFunctionBegin;
8382: PetscAssertPointer(n, 5);
8383: if (ia) PetscAssertPointer(ia, 6);
8384: if (ja) PetscAssertPointer(ja, 7);
8385: PetscAssertPointer(done, 8);
8386: MatCheckPreallocated(mat, 1);
8387: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8388: else {
8389: *done = PETSC_TRUE;
8390: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8391: }
8392: PetscFunctionReturn(PETSC_SUCCESS);
8393: }
8395: /*@C
8396: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8398: Collective
8400: Input Parameters:
8401: + mat - the matrix
8402: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8403: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8404: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8405: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8406: always used.
8407: . n - size of (possibly compressed) matrix
8408: . ia - the row pointers
8409: - ja - the column indices
8411: Output Parameter:
8412: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8414: Level: developer
8416: Note:
8417: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8418: us of the array after it has been restored. If you pass `NULL`, it will
8419: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8421: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8422: @*/
8423: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8424: {
8425: PetscFunctionBegin;
8428: if (ia) PetscAssertPointer(ia, 6);
8429: if (ja) PetscAssertPointer(ja, 7);
8430: if (done) PetscAssertPointer(done, 8);
8431: MatCheckPreallocated(mat, 1);
8433: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8434: else {
8435: if (done) *done = PETSC_TRUE;
8436: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8437: if (n) *n = 0;
8438: if (ia) *ia = NULL;
8439: if (ja) *ja = NULL;
8440: }
8441: PetscFunctionReturn(PETSC_SUCCESS);
8442: }
8444: /*@C
8445: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8447: Collective
8449: Input Parameters:
8450: + mat - the matrix
8451: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8452: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8453: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8454: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8455: always used.
8457: Output Parameters:
8458: + n - size of (possibly compressed) matrix
8459: . ia - the column pointers
8460: . ja - the row indices
8461: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8463: Level: developer
8465: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8466: @*/
8467: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8468: {
8469: PetscFunctionBegin;
8472: if (ia) PetscAssertPointer(ia, 6);
8473: if (ja) PetscAssertPointer(ja, 7);
8474: PetscAssertPointer(done, 8);
8475: MatCheckPreallocated(mat, 1);
8477: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8478: else {
8479: *done = PETSC_TRUE;
8480: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8481: if (n) *n = 0;
8482: if (ia) *ia = NULL;
8483: if (ja) *ja = NULL;
8484: }
8485: PetscFunctionReturn(PETSC_SUCCESS);
8486: }
8488: /*@
8489: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8490: `MatGetColumnIJ()`.
8492: Collective
8494: Input Parameters:
8495: + mat - the matrix
8496: . ncolors - maximum color value
8497: . n - number of entries in colorarray
8498: - colorarray - array indicating color for each column
8500: Output Parameter:
8501: . iscoloring - coloring generated using colorarray information
8503: Level: developer
8505: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8506: @*/
8507: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8508: {
8509: PetscFunctionBegin;
8512: PetscAssertPointer(colorarray, 4);
8513: PetscAssertPointer(iscoloring, 5);
8514: MatCheckPreallocated(mat, 1);
8516: if (!mat->ops->coloringpatch) {
8517: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8518: } else {
8519: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8520: }
8521: PetscFunctionReturn(PETSC_SUCCESS);
8522: }
8524: /*@
8525: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8527: Logically Collective
8529: Input Parameter:
8530: . mat - the factored matrix to be reset
8532: Level: developer
8534: Notes:
8535: This routine should be used only with factored matrices formed by in-place
8536: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8537: format). This option can save memory, for example, when solving nonlinear
8538: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8539: ILU(0) preconditioner.
8541: One can specify in-place ILU(0) factorization by calling
8542: .vb
8543: PCType(pc,PCILU);
8544: PCFactorSeUseInPlace(pc);
8545: .ve
8546: or by using the options -pc_type ilu -pc_factor_in_place
8548: In-place factorization ILU(0) can also be used as a local
8549: solver for the blocks within the block Jacobi or additive Schwarz
8550: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8551: for details on setting local solver options.
8553: Most users should employ the `KSP` interface for linear solvers
8554: instead of working directly with matrix algebra routines such as this.
8555: See, e.g., `KSPCreate()`.
8557: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8558: @*/
8559: PetscErrorCode MatSetUnfactored(Mat mat)
8560: {
8561: PetscFunctionBegin;
8564: MatCheckPreallocated(mat, 1);
8565: mat->factortype = MAT_FACTOR_NONE;
8566: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8567: PetscUseTypeMethod(mat, setunfactored);
8568: PetscFunctionReturn(PETSC_SUCCESS);
8569: }
8571: /*@
8572: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8573: as the original matrix.
8575: Collective
8577: Input Parameters:
8578: + mat - the original matrix
8579: . isrow - parallel `IS` containing the rows this processor should obtain
8580: . 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.
8581: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8583: Output Parameter:
8584: . newmat - the new submatrix, of the same type as the original matrix
8586: Level: advanced
8588: Notes:
8589: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8591: Some matrix types place restrictions on the row and column indices, such
8592: 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;
8593: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8595: The index sets may not have duplicate entries.
8597: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8598: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8599: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8600: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8601: you are finished using it.
8603: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8604: the input matrix.
8606: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8608: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8609: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8611: Example usage:
8612: Consider the following 8x8 matrix with 34 non-zero values, that is
8613: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8614: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8615: as follows
8616: .vb
8617: 1 2 0 | 0 3 0 | 0 4
8618: Proc0 0 5 6 | 7 0 0 | 8 0
8619: 9 0 10 | 11 0 0 | 12 0
8620: -------------------------------------
8621: 13 0 14 | 15 16 17 | 0 0
8622: Proc1 0 18 0 | 19 20 21 | 0 0
8623: 0 0 0 | 22 23 0 | 24 0
8624: -------------------------------------
8625: Proc2 25 26 27 | 0 0 28 | 29 0
8626: 30 0 0 | 31 32 33 | 0 34
8627: .ve
8629: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8631: .vb
8632: 2 0 | 0 3 0 | 0
8633: Proc0 5 6 | 7 0 0 | 8
8634: -------------------------------
8635: Proc1 18 0 | 19 20 21 | 0
8636: -------------------------------
8637: Proc2 26 27 | 0 0 28 | 29
8638: 0 0 | 31 32 33 | 0
8639: .ve
8641: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8642: @*/
8643: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8644: {
8645: PetscMPIInt size;
8646: Mat *local;
8647: IS iscoltmp;
8648: PetscBool flg;
8650: PetscFunctionBegin;
8654: PetscAssertPointer(newmat, 5);
8657: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8658: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8659: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
8661: MatCheckPreallocated(mat, 1);
8662: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8664: if (!iscol || isrow == iscol) {
8665: PetscBool stride;
8666: PetscMPIInt grabentirematrix = 0, grab;
8667: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8668: if (stride) {
8669: PetscInt first, step, n, rstart, rend;
8670: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8671: if (step == 1) {
8672: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8673: if (rstart == first) {
8674: PetscCall(ISGetLocalSize(isrow, &n));
8675: if (n == rend - rstart) grabentirematrix = 1;
8676: }
8677: }
8678: }
8679: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8680: if (grab) {
8681: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8682: if (cll == MAT_INITIAL_MATRIX) {
8683: *newmat = mat;
8684: PetscCall(PetscObjectReference((PetscObject)mat));
8685: }
8686: PetscFunctionReturn(PETSC_SUCCESS);
8687: }
8688: }
8690: if (!iscol) {
8691: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8692: } else {
8693: iscoltmp = iscol;
8694: }
8696: /* if original matrix is on just one processor then use submatrix generated */
8697: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8698: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8699: goto setproperties;
8700: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8701: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8702: *newmat = *local;
8703: PetscCall(PetscFree(local));
8704: goto setproperties;
8705: } else if (!mat->ops->createsubmatrix) {
8706: /* Create a new matrix type that implements the operation using the full matrix */
8707: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8708: switch (cll) {
8709: case MAT_INITIAL_MATRIX:
8710: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8711: break;
8712: case MAT_REUSE_MATRIX:
8713: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8714: break;
8715: default:
8716: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8717: }
8718: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8719: goto setproperties;
8720: }
8722: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8723: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8724: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8726: setproperties:
8727: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8728: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8729: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8730: }
8731: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8732: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8733: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8734: PetscFunctionReturn(PETSC_SUCCESS);
8735: }
8737: /*@
8738: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8740: Not Collective
8742: Input Parameters:
8743: + A - the matrix we wish to propagate options from
8744: - B - the matrix we wish to propagate options to
8746: Level: beginner
8748: Note:
8749: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8751: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8752: @*/
8753: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8754: {
8755: PetscFunctionBegin;
8758: B->symmetry_eternal = A->symmetry_eternal;
8759: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8760: B->symmetric = A->symmetric;
8761: B->structurally_symmetric = A->structurally_symmetric;
8762: B->spd = A->spd;
8763: B->hermitian = A->hermitian;
8764: PetscFunctionReturn(PETSC_SUCCESS);
8765: }
8767: /*@
8768: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8769: used during the assembly process to store values that belong to
8770: other processors.
8772: Not Collective
8774: Input Parameters:
8775: + mat - the matrix
8776: . size - the initial size of the stash.
8777: - bsize - the initial size of the block-stash(if used).
8779: Options Database Keys:
8780: + -matstash_initial_size size or size0,size1,...,sizep-1 - set initial size
8781: - -matstash_block_initial_size bsize or bsize0,bsize1,...,bsizep-1 - set initial block size
8783: Level: intermediate
8785: Notes:
8786: The block-stash is used for values set with `MatSetValuesBlocked()` while
8787: the stash is used for values set with `MatSetValues()`
8789: Run with the option -info and look for output of the form
8790: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8791: to determine the appropriate value, MM, to use for size and
8792: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8793: to determine the value, BMM to use for bsize
8795: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8796: @*/
8797: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8798: {
8799: PetscFunctionBegin;
8802: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8803: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8804: PetscFunctionReturn(PETSC_SUCCESS);
8805: }
8807: /*@
8808: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8809: the matrix
8811: Neighbor-wise Collective
8813: Input Parameters:
8814: + A - the matrix
8815: . x - the vector to be multiplied by the interpolation operator
8816: - y - the vector to be added to the result
8818: Output Parameter:
8819: . w - the resulting vector
8821: Level: intermediate
8823: Notes:
8824: `w` may be the same vector as `y`.
8826: This allows one to use either the restriction or interpolation (its transpose)
8827: matrix to do the interpolation
8829: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8830: @*/
8831: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8832: {
8833: PetscInt M, N, Ny;
8835: PetscFunctionBegin;
8840: PetscCall(MatGetSize(A, &M, &N));
8841: PetscCall(VecGetSize(y, &Ny));
8842: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
8843: else PetscCall(MatMultTransposeAdd(A, x, y, w));
8844: PetscFunctionReturn(PETSC_SUCCESS);
8845: }
8847: /*@
8848: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8849: the matrix
8851: Neighbor-wise Collective
8853: Input Parameters:
8854: + A - the matrix
8855: - x - the vector to be interpolated
8857: Output Parameter:
8858: . y - the resulting vector
8860: Level: intermediate
8862: Note:
8863: This allows one to use either the restriction or interpolation (its transpose)
8864: matrix to do the interpolation
8866: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8867: @*/
8868: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8869: {
8870: PetscInt M, N, Ny;
8872: PetscFunctionBegin;
8876: PetscCall(MatGetSize(A, &M, &N));
8877: PetscCall(VecGetSize(y, &Ny));
8878: if (M == Ny) PetscCall(MatMult(A, x, y));
8879: else PetscCall(MatMultTranspose(A, x, y));
8880: PetscFunctionReturn(PETSC_SUCCESS);
8881: }
8883: /*@
8884: MatRestrict - $y = A*x$ or $A^T*x$
8886: Neighbor-wise Collective
8888: Input Parameters:
8889: + A - the matrix
8890: - x - the vector to be restricted
8892: Output Parameter:
8893: . y - the resulting vector
8895: Level: intermediate
8897: Note:
8898: This allows one to use either the restriction or interpolation (its transpose)
8899: matrix to do the restriction
8901: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8902: @*/
8903: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8904: {
8905: PetscInt M, N, Nx;
8907: PetscFunctionBegin;
8911: PetscCall(MatGetSize(A, &M, &N));
8912: PetscCall(VecGetSize(x, &Nx));
8913: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
8914: else PetscCall(MatMult(A, x, y));
8915: PetscFunctionReturn(PETSC_SUCCESS);
8916: }
8918: /*@
8919: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8921: Neighbor-wise Collective
8923: Input Parameters:
8924: + A - the matrix
8925: . x - the input dense matrix to be multiplied
8926: - w - the input dense matrix to be added to the result
8928: Output Parameter:
8929: . y - the output dense matrix
8931: Level: intermediate
8933: Note:
8934: This allows one to use either the restriction or interpolation (its transpose)
8935: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8936: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8938: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8939: @*/
8940: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8941: {
8942: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8943: PetscBool trans = PETSC_TRUE;
8944: MatReuse reuse = MAT_INITIAL_MATRIX;
8946: PetscFunctionBegin;
8952: PetscCall(MatGetSize(A, &M, &N));
8953: PetscCall(MatGetSize(x, &Mx, &Nx));
8954: if (N == Mx) trans = PETSC_FALSE;
8955: 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);
8956: Mo = trans ? N : M;
8957: if (*y) {
8958: PetscCall(MatGetSize(*y, &My, &Ny));
8959: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
8960: else {
8961: 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);
8962: PetscCall(MatDestroy(y));
8963: }
8964: }
8966: if (w && *y == w) { /* this is to minimize changes in PCMG */
8967: PetscBool flg;
8969: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8970: if (w) {
8971: PetscInt My, Ny, Mw, Nw;
8973: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8974: PetscCall(MatGetSize(*y, &My, &Ny));
8975: PetscCall(MatGetSize(w, &Mw, &Nw));
8976: if (!flg || My != Mw || Ny != Nw) w = NULL;
8977: }
8978: if (!w) {
8979: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8980: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8981: PetscCall(PetscObjectDereference((PetscObject)w));
8982: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8983: }
8984: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8985: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8986: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8987: PetscFunctionReturn(PETSC_SUCCESS);
8988: }
8990: /*@
8991: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8993: Neighbor-wise Collective
8995: Input Parameters:
8996: + A - the matrix
8997: - x - the input dense matrix
8999: Output Parameter:
9000: . y - the output dense matrix
9002: Level: intermediate
9004: Note:
9005: This allows one to use either the restriction or interpolation (its transpose)
9006: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9007: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9009: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
9010: @*/
9011: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
9012: {
9013: PetscFunctionBegin;
9014: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9015: PetscFunctionReturn(PETSC_SUCCESS);
9016: }
9018: /*@
9019: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9021: Neighbor-wise Collective
9023: Input Parameters:
9024: + A - the matrix
9025: - x - the input dense matrix
9027: Output Parameter:
9028: . y - the output dense matrix
9030: Level: intermediate
9032: Note:
9033: This allows one to use either the restriction or interpolation (its transpose)
9034: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9035: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9037: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9038: @*/
9039: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9040: {
9041: PetscFunctionBegin;
9042: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9043: PetscFunctionReturn(PETSC_SUCCESS);
9044: }
9046: /*@
9047: MatGetNullSpace - retrieves the null space of a matrix.
9049: Logically Collective
9051: Input Parameters:
9052: + mat - the matrix
9053: - nullsp - the null space object
9055: Level: developer
9057: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9058: @*/
9059: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9060: {
9061: PetscFunctionBegin;
9063: PetscAssertPointer(nullsp, 2);
9064: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9065: PetscFunctionReturn(PETSC_SUCCESS);
9066: }
9068: /*@C
9069: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
9071: Logically Collective
9073: Input Parameters:
9074: + n - the number of matrices
9075: - mat - the array of matrices
9077: Output Parameters:
9078: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
9080: Level: developer
9082: Note:
9083: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9085: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9086: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
9087: @*/
9088: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9089: {
9090: PetscFunctionBegin;
9091: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9092: PetscAssertPointer(mat, 2);
9093: PetscAssertPointer(nullsp, 3);
9095: PetscCall(PetscCalloc1(3 * n, nullsp));
9096: for (PetscInt i = 0; i < n; i++) {
9098: (*nullsp)[i] = mat[i]->nullsp;
9099: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9100: (*nullsp)[n + i] = mat[i]->nearnullsp;
9101: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9102: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9103: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9104: }
9105: PetscFunctionReturn(PETSC_SUCCESS);
9106: }
9108: /*@C
9109: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9111: Logically Collective
9113: Input Parameters:
9114: + n - the number of matrices
9115: . mat - the array of matrices
9116: - nullsp - an array of null spaces
9118: Level: developer
9120: Note:
9121: Call `MatGetNullSpaces()` to create `nullsp`
9123: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9124: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
9125: @*/
9126: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9127: {
9128: PetscFunctionBegin;
9129: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9130: PetscAssertPointer(mat, 2);
9131: PetscAssertPointer(nullsp, 3);
9132: PetscAssertPointer(*nullsp, 3);
9134: for (PetscInt i = 0; i < n; i++) {
9136: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9137: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9138: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9139: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9140: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9141: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9142: }
9143: PetscCall(PetscFree(*nullsp));
9144: PetscFunctionReturn(PETSC_SUCCESS);
9145: }
9147: /*@
9148: MatSetNullSpace - attaches a null space to a matrix.
9150: Logically Collective
9152: Input Parameters:
9153: + mat - the matrix
9154: - nullsp - the null space object
9156: Level: advanced
9158: Notes:
9159: This null space is used by the `KSP` linear solvers to solve singular systems.
9161: 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`
9163: 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
9164: to zero but the linear system will still be solved in a least squares sense.
9166: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9167: 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 $A^T$, $R(A^T)$.
9168: 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
9169: $n(A^T))$ 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
9170: 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 $n(A^T)$.
9171: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9173: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9174: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9175: routine also automatically calls `MatSetTransposeNullSpace()`.
9177: The user should call `MatNullSpaceDestroy()`.
9179: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9180: `KSPSetPCSide()`
9181: @*/
9182: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9183: {
9184: PetscFunctionBegin;
9187: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9188: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9189: mat->nullsp = nullsp;
9190: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9191: PetscFunctionReturn(PETSC_SUCCESS);
9192: }
9194: /*@
9195: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9197: Logically Collective
9199: Input Parameters:
9200: + mat - the matrix
9201: - nullsp - the null space object
9203: Level: developer
9205: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9206: @*/
9207: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9208: {
9209: PetscFunctionBegin;
9212: PetscAssertPointer(nullsp, 2);
9213: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9214: PetscFunctionReturn(PETSC_SUCCESS);
9215: }
9217: /*@
9218: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9220: Logically Collective
9222: Input Parameters:
9223: + mat - the matrix
9224: - nullsp - the null space object
9226: Level: advanced
9228: Notes:
9229: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9231: See `MatSetNullSpace()`
9233: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9234: @*/
9235: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9236: {
9237: PetscFunctionBegin;
9240: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9241: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9242: mat->transnullsp = nullsp;
9243: PetscFunctionReturn(PETSC_SUCCESS);
9244: }
9246: /*@
9247: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9248: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9250: Logically Collective
9252: Input Parameters:
9253: + mat - the matrix
9254: - nullsp - the null space object
9256: Level: advanced
9258: Notes:
9259: Overwrites any previous near null space that may have been attached
9261: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9263: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9264: @*/
9265: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9266: {
9267: PetscFunctionBegin;
9271: MatCheckPreallocated(mat, 1);
9272: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9273: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9274: mat->nearnullsp = nullsp;
9275: PetscFunctionReturn(PETSC_SUCCESS);
9276: }
9278: /*@
9279: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9281: Not Collective
9283: Input Parameter:
9284: . mat - the matrix
9286: Output Parameter:
9287: . nullsp - the null space object, `NULL` if not set
9289: Level: advanced
9291: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9292: @*/
9293: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9294: {
9295: PetscFunctionBegin;
9298: PetscAssertPointer(nullsp, 2);
9299: MatCheckPreallocated(mat, 1);
9300: *nullsp = mat->nearnullsp;
9301: PetscFunctionReturn(PETSC_SUCCESS);
9302: }
9304: /*@
9305: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9307: Collective
9309: Input Parameters:
9310: + mat - the matrix
9311: . row - row/column permutation
9312: - info - information on desired factorization process
9314: Level: developer
9316: Notes:
9317: Probably really in-place only when level of fill is zero, otherwise allocates
9318: new space to store factored matrix and deletes previous memory.
9320: Most users should employ the `KSP` interface for linear solvers
9321: instead of working directly with matrix algebra routines such as this.
9322: See, e.g., `KSPCreate()`.
9324: Fortran Note:
9325: A valid (non-null) `info` argument must be provided
9327: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9328: @*/
9329: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9330: {
9331: PetscFunctionBegin;
9335: PetscAssertPointer(info, 3);
9336: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9337: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9338: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9339: MatCheckPreallocated(mat, 1);
9340: PetscUseTypeMethod(mat, iccfactor, row, info);
9341: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9342: PetscFunctionReturn(PETSC_SUCCESS);
9343: }
9345: /*@
9346: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9347: ghosted ones.
9349: Not Collective
9351: Input Parameters:
9352: + mat - the matrix
9353: - diag - the diagonal values, including ghost ones
9355: Level: developer
9357: Notes:
9358: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9360: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9362: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9363: @*/
9364: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9365: {
9366: PetscMPIInt size;
9368: PetscFunctionBegin;
9373: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9374: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9375: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9376: if (size == 1) {
9377: PetscInt n, m;
9378: PetscCall(VecGetSize(diag, &n));
9379: PetscCall(MatGetSize(mat, NULL, &m));
9380: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9381: PetscCall(MatDiagonalScale(mat, NULL, diag));
9382: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9383: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9384: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9385: PetscFunctionReturn(PETSC_SUCCESS);
9386: }
9388: /*@
9389: MatGetInertia - Gets the inertia from a factored matrix
9391: Collective
9393: Input Parameter:
9394: . mat - the matrix
9396: Output Parameters:
9397: + nneg - number of negative eigenvalues
9398: . nzero - number of zero eigenvalues
9399: - npos - number of positive eigenvalues
9401: Level: advanced
9403: Note:
9404: Matrix must have been factored by `MatCholeskyFactor()`
9406: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9407: @*/
9408: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9409: {
9410: PetscFunctionBegin;
9413: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9414: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9415: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9416: PetscFunctionReturn(PETSC_SUCCESS);
9417: }
9419: /*@C
9420: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9422: Neighbor-wise Collective
9424: Input Parameters:
9425: + mat - the factored matrix obtained with `MatGetFactor()`
9426: - b - the right-hand-side vectors
9428: Output Parameter:
9429: . x - the result vectors
9431: Level: developer
9433: Note:
9434: The vectors `b` and `x` cannot be the same. I.e., one cannot
9435: call `MatSolves`(A,x,x).
9437: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9438: @*/
9439: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9440: {
9441: PetscFunctionBegin;
9444: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9445: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9446: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9448: MatCheckPreallocated(mat, 1);
9449: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9450: PetscUseTypeMethod(mat, solves, b, x);
9451: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9452: PetscFunctionReturn(PETSC_SUCCESS);
9453: }
9455: /*@
9456: MatIsSymmetric - Test whether a matrix is symmetric
9458: Collective
9460: Input Parameters:
9461: + A - the matrix to test
9462: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9464: Output Parameter:
9465: . flg - the result
9467: Level: intermediate
9469: Notes:
9470: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9472: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9474: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9475: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9477: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9478: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9479: @*/
9480: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9481: {
9482: PetscFunctionBegin;
9484: PetscAssertPointer(flg, 3);
9485: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9486: else {
9487: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9488: else PetscCall(MatIsTranspose(A, A, tol, flg));
9489: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9490: }
9491: PetscFunctionReturn(PETSC_SUCCESS);
9492: }
9494: /*@
9495: MatIsHermitian - Test whether a matrix is Hermitian
9497: Collective
9499: Input Parameters:
9500: + A - the matrix to test
9501: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9503: Output Parameter:
9504: . flg - the result
9506: Level: intermediate
9508: Notes:
9509: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9511: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9513: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9514: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9516: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9517: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9518: @*/
9519: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9520: {
9521: PetscFunctionBegin;
9523: PetscAssertPointer(flg, 3);
9524: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9525: else {
9526: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9527: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9528: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9529: }
9530: PetscFunctionReturn(PETSC_SUCCESS);
9531: }
9533: /*@
9534: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9536: Not Collective
9538: Input Parameter:
9539: . A - the matrix to check
9541: Output Parameters:
9542: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9543: - flg - the result (only valid if set is `PETSC_TRUE`)
9545: Level: advanced
9547: Notes:
9548: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9549: if you want it explicitly checked
9551: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9552: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9554: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9555: @*/
9556: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9557: {
9558: PetscFunctionBegin;
9560: PetscAssertPointer(set, 2);
9561: PetscAssertPointer(flg, 3);
9562: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9563: *set = PETSC_TRUE;
9564: *flg = PetscBool3ToBool(A->symmetric);
9565: } else *set = PETSC_FALSE;
9566: PetscFunctionReturn(PETSC_SUCCESS);
9567: }
9569: /*@
9570: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9572: Not Collective
9574: Input Parameter:
9575: . A - the matrix to check
9577: Output Parameters:
9578: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9579: - flg - the result (only valid if set is `PETSC_TRUE`)
9581: Level: advanced
9583: Notes:
9584: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9586: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9587: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9589: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9590: @*/
9591: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9592: {
9593: PetscFunctionBegin;
9595: PetscAssertPointer(set, 2);
9596: PetscAssertPointer(flg, 3);
9597: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9598: *set = PETSC_TRUE;
9599: *flg = PetscBool3ToBool(A->spd);
9600: } else *set = PETSC_FALSE;
9601: PetscFunctionReturn(PETSC_SUCCESS);
9602: }
9604: /*@
9605: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9607: Not Collective
9609: Input Parameter:
9610: . A - the matrix to check
9612: Output Parameters:
9613: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9614: - flg - the result (only valid if set is `PETSC_TRUE`)
9616: Level: advanced
9618: Notes:
9619: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9620: if you want it explicitly checked
9622: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9623: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9625: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9626: @*/
9627: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9628: {
9629: PetscFunctionBegin;
9631: PetscAssertPointer(set, 2);
9632: PetscAssertPointer(flg, 3);
9633: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9634: *set = PETSC_TRUE;
9635: *flg = PetscBool3ToBool(A->hermitian);
9636: } else *set = PETSC_FALSE;
9637: PetscFunctionReturn(PETSC_SUCCESS);
9638: }
9640: /*@
9641: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9643: Collective
9645: Input Parameter:
9646: . A - the matrix to test
9648: Output Parameter:
9649: . flg - the result
9651: Level: intermediate
9653: Notes:
9654: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9656: 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
9657: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9659: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9660: @*/
9661: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9662: {
9663: PetscFunctionBegin;
9665: PetscAssertPointer(flg, 2);
9666: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
9667: else {
9668: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9669: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9670: }
9671: PetscFunctionReturn(PETSC_SUCCESS);
9672: }
9674: /*@
9675: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9677: Not Collective
9679: Input Parameter:
9680: . A - the matrix to check
9682: Output Parameters:
9683: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9684: - flg - the result (only valid if set is PETSC_TRUE)
9686: Level: advanced
9688: Notes:
9689: 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
9690: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9692: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9694: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9695: @*/
9696: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9697: {
9698: PetscFunctionBegin;
9700: PetscAssertPointer(set, 2);
9701: PetscAssertPointer(flg, 3);
9702: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9703: *set = PETSC_TRUE;
9704: *flg = PetscBool3ToBool(A->structurally_symmetric);
9705: } else *set = PETSC_FALSE;
9706: PetscFunctionReturn(PETSC_SUCCESS);
9707: }
9709: /*@
9710: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9711: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9713: Not Collective
9715: Input Parameter:
9716: . mat - the matrix
9718: Output Parameters:
9719: + nstash - the size of the stash
9720: . reallocs - the number of additional mallocs incurred.
9721: . bnstash - the size of the block stash
9722: - breallocs - the number of additional mallocs incurred.in the block stash
9724: Level: advanced
9726: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9727: @*/
9728: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9729: {
9730: PetscFunctionBegin;
9731: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9732: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9733: PetscFunctionReturn(PETSC_SUCCESS);
9734: }
9736: /*@
9737: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9738: parallel layout, `PetscLayout` for rows and columns
9740: Collective
9742: Input Parameter:
9743: . mat - the matrix
9745: Output Parameters:
9746: + right - (optional) vector that the matrix can be multiplied against
9747: - left - (optional) vector that the matrix vector product can be stored in
9749: Level: advanced
9751: Notes:
9752: 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()`.
9754: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9756: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9757: @*/
9758: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9759: {
9760: PetscFunctionBegin;
9763: if (mat->ops->getvecs) {
9764: PetscUseTypeMethod(mat, getvecs, right, left);
9765: } else {
9766: if (right) {
9767: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9768: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9769: PetscCall(VecSetType(*right, mat->defaultvectype));
9770: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9771: if (mat->boundtocpu && mat->bindingpropagates) {
9772: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9773: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9774: }
9775: #endif
9776: }
9777: if (left) {
9778: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9779: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9780: PetscCall(VecSetType(*left, mat->defaultvectype));
9781: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9782: if (mat->boundtocpu && mat->bindingpropagates) {
9783: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9784: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9785: }
9786: #endif
9787: }
9788: }
9789: PetscFunctionReturn(PETSC_SUCCESS);
9790: }
9792: /*@
9793: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9794: with default values.
9796: Not Collective
9798: Input Parameter:
9799: . info - the `MatFactorInfo` data structure
9801: Level: developer
9803: Notes:
9804: The solvers are generally used through the `KSP` and `PC` objects, for example
9805: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9807: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9809: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9810: @*/
9811: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9812: {
9813: PetscFunctionBegin;
9814: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9815: PetscFunctionReturn(PETSC_SUCCESS);
9816: }
9818: /*@
9819: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9821: Collective
9823: Input Parameters:
9824: + mat - the factored matrix
9825: - is - the index set defining the Schur indices (0-based)
9827: Level: advanced
9829: Notes:
9830: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9832: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9834: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9836: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9837: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9838: @*/
9839: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9840: {
9841: PetscErrorCode (*f)(Mat, IS);
9843: PetscFunctionBegin;
9848: PetscCheckSameComm(mat, 1, is, 2);
9849: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9850: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9851: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9852: PetscCall(MatDestroy(&mat->schur));
9853: PetscCall((*f)(mat, is));
9854: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9855: PetscFunctionReturn(PETSC_SUCCESS);
9856: }
9858: /*@
9859: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9861: Logically Collective
9863: Input Parameters:
9864: + F - the factored matrix obtained by calling `MatGetFactor()`
9865: . S - location where to return the Schur complement, can be `NULL`
9866: - status - the status of the Schur complement matrix, can be `NULL`
9868: Level: advanced
9870: Notes:
9871: You must call `MatFactorSetSchurIS()` before calling this routine.
9873: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9875: The routine provides a copy of the Schur matrix stored within the solver data structures.
9876: The caller must destroy the object when it is no longer needed.
9877: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9879: 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)
9881: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9883: Developer Note:
9884: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9885: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9887: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9888: @*/
9889: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9890: {
9891: PetscFunctionBegin;
9893: if (S) PetscAssertPointer(S, 2);
9894: if (status) PetscAssertPointer(status, 3);
9895: if (S) {
9896: PetscErrorCode (*f)(Mat, Mat *);
9898: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9899: if (f) PetscCall((*f)(F, S));
9900: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9901: }
9902: if (status) *status = F->schur_status;
9903: PetscFunctionReturn(PETSC_SUCCESS);
9904: }
9906: /*@
9907: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9909: Logically Collective
9911: Input Parameters:
9912: + F - the factored matrix obtained by calling `MatGetFactor()`
9913: . S - location where to return the Schur complement, can be `NULL`
9914: - status - the status of the Schur complement matrix, can be `NULL`
9916: Level: advanced
9918: Notes:
9919: You must call `MatFactorSetSchurIS()` before calling this routine.
9921: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9923: The routine returns a the Schur Complement stored within the data structures of the solver.
9925: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9927: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9929: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9931: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9933: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9934: @*/
9935: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9936: {
9937: PetscFunctionBegin;
9939: if (S) {
9940: PetscAssertPointer(S, 2);
9941: *S = F->schur;
9942: }
9943: if (status) {
9944: PetscAssertPointer(status, 3);
9945: *status = F->schur_status;
9946: }
9947: PetscFunctionReturn(PETSC_SUCCESS);
9948: }
9950: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9951: {
9952: Mat S = F->schur;
9954: PetscFunctionBegin;
9955: switch (F->schur_status) {
9956: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9957: case MAT_FACTOR_SCHUR_INVERTED:
9958: if (S) {
9959: S->ops->solve = NULL;
9960: S->ops->matsolve = NULL;
9961: S->ops->solvetranspose = NULL;
9962: S->ops->matsolvetranspose = NULL;
9963: S->ops->solveadd = NULL;
9964: S->ops->solvetransposeadd = NULL;
9965: S->factortype = MAT_FACTOR_NONE;
9966: PetscCall(PetscFree(S->solvertype));
9967: }
9968: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9969: break;
9970: default:
9971: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9972: }
9973: PetscFunctionReturn(PETSC_SUCCESS);
9974: }
9976: /*@
9977: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9979: Logically Collective
9981: Input Parameters:
9982: + F - the factored matrix obtained by calling `MatGetFactor()`
9983: . S - location where the Schur complement is stored
9984: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9986: Level: advanced
9988: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9989: @*/
9990: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9991: {
9992: PetscFunctionBegin;
9994: if (S) {
9996: *S = NULL;
9997: }
9998: F->schur_status = status;
9999: PetscCall(MatFactorUpdateSchurStatus_Private(F));
10000: PetscFunctionReturn(PETSC_SUCCESS);
10001: }
10003: /*@
10004: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
10006: Logically Collective
10008: Input Parameters:
10009: + F - the factored matrix obtained by calling `MatGetFactor()`
10010: . rhs - location where the right-hand side of the Schur complement system is stored
10011: - sol - location where the solution of the Schur complement system has to be returned
10013: Level: advanced
10015: Notes:
10016: The sizes of the vectors should match the size of the Schur complement
10018: Must be called after `MatFactorSetSchurIS()`
10020: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10021: @*/
10022: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10023: {
10024: PetscFunctionBegin;
10031: PetscCheckSameComm(F, 1, rhs, 2);
10032: PetscCheckSameComm(F, 1, sol, 3);
10033: PetscCall(MatFactorFactorizeSchurComplement(F));
10034: switch (F->schur_status) {
10035: case MAT_FACTOR_SCHUR_FACTORED:
10036: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10037: break;
10038: case MAT_FACTOR_SCHUR_INVERTED:
10039: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10040: break;
10041: default:
10042: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10043: }
10044: PetscFunctionReturn(PETSC_SUCCESS);
10045: }
10047: /*@
10048: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10050: Logically Collective
10052: Input Parameters:
10053: + F - the factored matrix obtained by calling `MatGetFactor()`
10054: . rhs - location where the right-hand side of the Schur complement system is stored
10055: - sol - location where the solution of the Schur complement system has to be returned
10057: Level: advanced
10059: Notes:
10060: The sizes of the vectors should match the size of the Schur complement
10062: Must be called after `MatFactorSetSchurIS()`
10064: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10065: @*/
10066: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10067: {
10068: PetscFunctionBegin;
10075: PetscCheckSameComm(F, 1, rhs, 2);
10076: PetscCheckSameComm(F, 1, sol, 3);
10077: PetscCall(MatFactorFactorizeSchurComplement(F));
10078: switch (F->schur_status) {
10079: case MAT_FACTOR_SCHUR_FACTORED:
10080: PetscCall(MatSolve(F->schur, rhs, sol));
10081: break;
10082: case MAT_FACTOR_SCHUR_INVERTED:
10083: PetscCall(MatMult(F->schur, rhs, sol));
10084: break;
10085: default:
10086: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10087: }
10088: PetscFunctionReturn(PETSC_SUCCESS);
10089: }
10091: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10092: #if PetscDefined(HAVE_CUDA)
10093: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10094: #endif
10096: /* Schur status updated in the interface */
10097: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10098: {
10099: Mat S = F->schur;
10101: PetscFunctionBegin;
10102: if (S) {
10103: PetscMPIInt size;
10104: PetscBool isdense, isdensecuda;
10106: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10107: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10108: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10109: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10110: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10111: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10112: if (isdense) {
10113: PetscCall(MatSeqDenseInvertFactors_Private(S));
10114: } else if (isdensecuda) {
10115: #if defined(PETSC_HAVE_CUDA)
10116: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10117: #endif
10118: }
10119: // HIP??????????????
10120: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10121: }
10122: PetscFunctionReturn(PETSC_SUCCESS);
10123: }
10125: /*@
10126: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10128: Logically Collective
10130: Input Parameter:
10131: . F - the factored matrix obtained by calling `MatGetFactor()`
10133: Level: advanced
10135: Notes:
10136: Must be called after `MatFactorSetSchurIS()`.
10138: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10140: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10141: @*/
10142: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10143: {
10144: PetscFunctionBegin;
10147: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10148: PetscCall(MatFactorFactorizeSchurComplement(F));
10149: PetscCall(MatFactorInvertSchurComplement_Private(F));
10150: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10151: PetscFunctionReturn(PETSC_SUCCESS);
10152: }
10154: /*@
10155: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10157: Logically Collective
10159: Input Parameter:
10160: . F - the factored matrix obtained by calling `MatGetFactor()`
10162: Level: advanced
10164: Note:
10165: Must be called after `MatFactorSetSchurIS()`
10167: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10168: @*/
10169: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10170: {
10171: MatFactorInfo info;
10173: PetscFunctionBegin;
10176: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10177: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10178: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10179: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10180: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10181: } else {
10182: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10183: }
10184: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10185: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10186: PetscFunctionReturn(PETSC_SUCCESS);
10187: }
10189: /*@
10190: MatPtAP - Creates the matrix product $C = P^T * A * P$
10192: Neighbor-wise Collective
10194: Input Parameters:
10195: + A - the matrix
10196: . P - the projection matrix
10197: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10198: - 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
10199: if the result is a dense matrix this is irrelevant
10201: Output Parameter:
10202: . C - the product matrix
10204: Level: intermediate
10206: Notes:
10207: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10209: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10210: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10212: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10214: Developer Note:
10215: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10217: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10218: @*/
10219: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10220: {
10221: PetscFunctionBegin;
10222: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10223: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10225: if (scall == MAT_INITIAL_MATRIX) {
10226: PetscCall(MatProductCreate(A, P, NULL, C));
10227: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10228: PetscCall(MatProductSetAlgorithm(*C, "default"));
10229: PetscCall(MatProductSetFill(*C, fill));
10231: (*C)->product->api_user = PETSC_TRUE;
10232: PetscCall(MatProductSetFromOptions(*C));
10233: 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);
10234: PetscCall(MatProductSymbolic(*C));
10235: } else { /* scall == MAT_REUSE_MATRIX */
10236: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10237: }
10239: PetscCall(MatProductNumeric(*C));
10240: if (A->symmetric == PETSC_BOOL3_TRUE) {
10241: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10242: (*C)->spd = A->spd;
10243: }
10244: PetscFunctionReturn(PETSC_SUCCESS);
10245: }
10247: /*@
10248: MatRARt - Creates the matrix product $C = R * A * R^T$
10250: Neighbor-wise Collective
10252: Input Parameters:
10253: + A - the matrix
10254: . R - the projection matrix
10255: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10256: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10257: if the result is a dense matrix this is irrelevant
10259: Output Parameter:
10260: . C - the product matrix
10262: Level: intermediate
10264: Notes:
10265: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10267: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10268: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10270: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10271: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10272: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10273: We recommend using `MatPtAP()` when possible.
10275: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10277: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10278: @*/
10279: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10280: {
10281: PetscFunctionBegin;
10282: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10283: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10285: if (scall == MAT_INITIAL_MATRIX) {
10286: PetscCall(MatProductCreate(A, R, NULL, C));
10287: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10288: PetscCall(MatProductSetAlgorithm(*C, "default"));
10289: PetscCall(MatProductSetFill(*C, fill));
10291: (*C)->product->api_user = PETSC_TRUE;
10292: PetscCall(MatProductSetFromOptions(*C));
10293: 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);
10294: PetscCall(MatProductSymbolic(*C));
10295: } else { /* scall == MAT_REUSE_MATRIX */
10296: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10297: }
10299: PetscCall(MatProductNumeric(*C));
10300: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10301: PetscFunctionReturn(PETSC_SUCCESS);
10302: }
10304: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10305: {
10306: PetscBool flg = PETSC_TRUE;
10308: PetscFunctionBegin;
10309: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10310: if (scall == MAT_INITIAL_MATRIX) {
10311: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10312: PetscCall(MatProductCreate(A, B, NULL, C));
10313: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10314: PetscCall(MatProductSetFill(*C, fill));
10315: } else { /* scall == MAT_REUSE_MATRIX */
10316: Mat_Product *product = (*C)->product;
10318: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10319: if (flg && product && product->type != ptype) {
10320: PetscCall(MatProductClear(*C));
10321: product = NULL;
10322: }
10323: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10324: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10325: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10326: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10327: product = (*C)->product;
10328: product->fill = fill;
10329: product->clear = PETSC_TRUE;
10330: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10331: flg = PETSC_FALSE;
10332: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10333: }
10334: }
10335: if (flg) {
10336: (*C)->product->api_user = PETSC_TRUE;
10337: PetscCall(MatProductSetType(*C, ptype));
10338: PetscCall(MatProductSetFromOptions(*C));
10339: PetscCall(MatProductSymbolic(*C));
10340: }
10341: PetscCall(MatProductNumeric(*C));
10342: PetscFunctionReturn(PETSC_SUCCESS);
10343: }
10345: /*@
10346: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10348: Neighbor-wise Collective
10350: Input Parameters:
10351: + A - the left matrix
10352: . B - the right matrix
10353: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10354: - 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
10355: if the result is a dense matrix this is irrelevant
10357: Output Parameter:
10358: . C - the product matrix
10360: Notes:
10361: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10363: `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
10364: call to this function with `MAT_INITIAL_MATRIX`.
10366: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10368: 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`,
10369: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10371: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10373: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10374: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10376: Example of Usage:
10377: .vb
10378: MatProductCreate(A,B,NULL,&C);
10379: MatProductSetType(C,MATPRODUCT_AB);
10380: MatProductSymbolic(C);
10381: MatProductNumeric(C); // compute C=A * B
10382: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10383: MatProductNumeric(C);
10384: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10385: MatProductNumeric(C);
10386: .ve
10388: Level: intermediate
10390: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10391: @*/
10392: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10393: {
10394: PetscFunctionBegin;
10395: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10396: PetscFunctionReturn(PETSC_SUCCESS);
10397: }
10399: /*@
10400: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10402: Neighbor-wise Collective
10404: Input Parameters:
10405: + A - the left matrix
10406: . B - the right matrix
10407: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10408: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10410: Output Parameter:
10411: . C - the product matrix
10413: Options Database Key:
10414: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10415: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10416: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10418: Level: intermediate
10420: Notes:
10421: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10423: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10425: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10426: actually needed.
10428: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10429: and for pairs of `MATMPIDENSE` matrices.
10431: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10432: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10434: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10436: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10437: @*/
10438: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10439: {
10440: PetscFunctionBegin;
10441: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10442: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10443: PetscFunctionReturn(PETSC_SUCCESS);
10444: }
10446: /*@
10447: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10449: Neighbor-wise Collective
10451: Input Parameters:
10452: + A - the left matrix
10453: . B - the right matrix
10454: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10455: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10457: Output Parameter:
10458: . C - the product matrix
10460: Level: intermediate
10462: Notes:
10463: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10465: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10467: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10468: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10470: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10471: actually needed.
10473: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10474: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10476: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10478: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10479: @*/
10480: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10481: {
10482: PetscFunctionBegin;
10483: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10484: PetscFunctionReturn(PETSC_SUCCESS);
10485: }
10487: /*@
10488: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10490: Neighbor-wise Collective
10492: Input Parameters:
10493: + A - the left matrix
10494: . B - the middle matrix
10495: . C - the right matrix
10496: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10497: - 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
10498: if the result is a dense matrix this is irrelevant
10500: Output Parameter:
10501: . D - the product matrix
10503: Level: intermediate
10505: Notes:
10506: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10508: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10510: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10511: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10513: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10514: actually needed.
10516: If you have many matrices with the same non-zero structure to multiply, you
10517: should use `MAT_REUSE_MATRIX` in all calls but the first
10519: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10521: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10522: @*/
10523: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10524: {
10525: PetscFunctionBegin;
10526: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10527: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10529: if (scall == MAT_INITIAL_MATRIX) {
10530: PetscCall(MatProductCreate(A, B, C, D));
10531: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10532: PetscCall(MatProductSetAlgorithm(*D, "default"));
10533: PetscCall(MatProductSetFill(*D, fill));
10535: (*D)->product->api_user = PETSC_TRUE;
10536: PetscCall(MatProductSetFromOptions(*D));
10537: 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,
10538: ((PetscObject)C)->type_name);
10539: PetscCall(MatProductSymbolic(*D));
10540: } else { /* user may change input matrices when REUSE */
10541: PetscCall(MatProductReplaceMats(A, B, C, *D));
10542: }
10543: PetscCall(MatProductNumeric(*D));
10544: PetscFunctionReturn(PETSC_SUCCESS);
10545: }
10547: /*@
10548: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10550: Collective
10552: Input Parameters:
10553: + mat - the matrix
10554: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10555: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10556: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10558: Output Parameter:
10559: . matredundant - redundant matrix
10561: Level: advanced
10563: Notes:
10564: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10565: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10567: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10568: calling it.
10570: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10572: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10573: @*/
10574: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10575: {
10576: MPI_Comm comm;
10577: PetscMPIInt size;
10578: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10579: Mat_Redundant *redund = NULL;
10580: PetscSubcomm psubcomm = NULL;
10581: MPI_Comm subcomm_in = subcomm;
10582: Mat *matseq;
10583: IS isrow, iscol;
10584: PetscBool newsubcomm = PETSC_FALSE;
10586: PetscFunctionBegin;
10588: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10589: PetscAssertPointer(*matredundant, 5);
10591: }
10593: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10594: if (size == 1 || nsubcomm == 1) {
10595: if (reuse == MAT_INITIAL_MATRIX) {
10596: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10597: } else {
10598: 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");
10599: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10600: }
10601: PetscFunctionReturn(PETSC_SUCCESS);
10602: }
10604: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10605: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10606: MatCheckPreallocated(mat, 1);
10608: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10609: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10610: /* create psubcomm, then get subcomm */
10611: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10612: PetscCallMPI(MPI_Comm_size(comm, &size));
10613: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10615: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10616: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10617: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10618: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10619: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10620: newsubcomm = PETSC_TRUE;
10621: PetscCall(PetscSubcommDestroy(&psubcomm));
10622: }
10624: /* get isrow, iscol and a local sequential matrix matseq[0] */
10625: if (reuse == MAT_INITIAL_MATRIX) {
10626: mloc_sub = PETSC_DECIDE;
10627: nloc_sub = PETSC_DECIDE;
10628: if (bs < 1) {
10629: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10630: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10631: } else {
10632: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10633: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10634: }
10635: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10636: rstart = rend - mloc_sub;
10637: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10638: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10639: PetscCall(ISSetIdentity(iscol));
10640: } else { /* reuse == MAT_REUSE_MATRIX */
10641: 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");
10642: /* retrieve subcomm */
10643: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10644: redund = (*matredundant)->redundant;
10645: isrow = redund->isrow;
10646: iscol = redund->iscol;
10647: matseq = redund->matseq;
10648: }
10649: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10651: /* get matredundant over subcomm */
10652: if (reuse == MAT_INITIAL_MATRIX) {
10653: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10655: /* create a supporting struct and attach it to C for reuse */
10656: PetscCall(PetscNew(&redund));
10657: (*matredundant)->redundant = redund;
10658: redund->isrow = isrow;
10659: redund->iscol = iscol;
10660: redund->matseq = matseq;
10661: if (newsubcomm) {
10662: redund->subcomm = subcomm;
10663: } else {
10664: redund->subcomm = MPI_COMM_NULL;
10665: }
10666: } else {
10667: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10668: }
10669: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10670: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10671: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10672: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10673: }
10674: #endif
10675: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10676: PetscFunctionReturn(PETSC_SUCCESS);
10677: }
10679: /*@C
10680: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10681: a given `Mat`. Each submatrix can span multiple procs.
10683: Collective
10685: Input Parameters:
10686: + mat - the matrix
10687: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10688: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10690: Output Parameter:
10691: . subMat - parallel sub-matrices each spanning a given `subcomm`
10693: Level: advanced
10695: Notes:
10696: The submatrix partition across processors is dictated by `subComm` a
10697: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10698: is not restricted to be grouped with consecutive original MPI processes.
10700: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10701: map directly to the layout of the original matrix [wrt the local
10702: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10703: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10704: the `subMat`. However the offDiagMat looses some columns - and this is
10705: reconstructed with `MatSetValues()`
10707: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10709: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10710: @*/
10711: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10712: {
10713: PetscMPIInt commsize, subCommSize;
10715: PetscFunctionBegin;
10716: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10717: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10718: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10720: 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");
10721: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10722: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10723: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10724: PetscFunctionReturn(PETSC_SUCCESS);
10725: }
10727: /*@
10728: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10730: Not Collective
10732: Input Parameters:
10733: + mat - matrix to extract local submatrix from
10734: . isrow - local row indices for submatrix
10735: - iscol - local column indices for submatrix
10737: Output Parameter:
10738: . submat - the submatrix
10740: Level: intermediate
10742: Notes:
10743: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10745: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10746: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10748: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10749: `MatSetValuesBlockedLocal()` will also be implemented.
10751: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10752: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10754: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10755: @*/
10756: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10757: {
10758: PetscFunctionBegin;
10762: PetscCheckSameComm(isrow, 2, iscol, 3);
10763: PetscAssertPointer(submat, 4);
10764: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10766: if (mat->ops->getlocalsubmatrix) {
10767: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10768: } else {
10769: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10770: }
10771: (*submat)->assembled = mat->assembled;
10772: PetscFunctionReturn(PETSC_SUCCESS);
10773: }
10775: /*@
10776: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10778: Not Collective
10780: Input Parameters:
10781: + mat - matrix to extract local submatrix from
10782: . isrow - local row indices for submatrix
10783: . iscol - local column indices for submatrix
10784: - submat - the submatrix
10786: Level: intermediate
10788: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10789: @*/
10790: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10791: {
10792: PetscFunctionBegin;
10796: PetscCheckSameComm(isrow, 2, iscol, 3);
10797: PetscAssertPointer(submat, 4);
10800: if (mat->ops->restorelocalsubmatrix) {
10801: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10802: } else {
10803: PetscCall(MatDestroy(submat));
10804: }
10805: *submat = NULL;
10806: PetscFunctionReturn(PETSC_SUCCESS);
10807: }
10809: /*@
10810: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10812: Collective
10814: Input Parameter:
10815: . mat - the matrix
10817: Output Parameter:
10818: . is - if any rows have zero diagonals this contains the list of them
10820: Level: developer
10822: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10823: @*/
10824: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10825: {
10826: PetscFunctionBegin;
10829: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10830: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10832: if (!mat->ops->findzerodiagonals) {
10833: Vec diag;
10834: const PetscScalar *a;
10835: PetscInt *rows;
10836: PetscInt rStart, rEnd, r, nrow = 0;
10838: PetscCall(MatCreateVecs(mat, &diag, NULL));
10839: PetscCall(MatGetDiagonal(mat, diag));
10840: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10841: PetscCall(VecGetArrayRead(diag, &a));
10842: for (r = 0; r < rEnd - rStart; ++r)
10843: if (a[r] == 0.0) ++nrow;
10844: PetscCall(PetscMalloc1(nrow, &rows));
10845: nrow = 0;
10846: for (r = 0; r < rEnd - rStart; ++r)
10847: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10848: PetscCall(VecRestoreArrayRead(diag, &a));
10849: PetscCall(VecDestroy(&diag));
10850: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10851: } else {
10852: PetscUseTypeMethod(mat, findzerodiagonals, is);
10853: }
10854: PetscFunctionReturn(PETSC_SUCCESS);
10855: }
10857: /*@
10858: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10860: Collective
10862: Input Parameter:
10863: . mat - the matrix
10865: Output Parameter:
10866: . is - contains the list of rows with off block diagonal entries
10868: Level: developer
10870: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10871: @*/
10872: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10873: {
10874: PetscFunctionBegin;
10877: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10878: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10880: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10881: PetscFunctionReturn(PETSC_SUCCESS);
10882: }
10884: /*@C
10885: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10887: Collective; No Fortran Support
10889: Input Parameter:
10890: . mat - the matrix
10892: Output Parameter:
10893: . values - the block inverses in column major order (FORTRAN-like)
10895: Level: advanced
10897: Notes:
10898: The size of the blocks is determined by the block size of the matrix.
10900: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10902: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10904: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10905: @*/
10906: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10907: {
10908: PetscFunctionBegin;
10910: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10911: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10912: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10913: PetscFunctionReturn(PETSC_SUCCESS);
10914: }
10916: /*@
10917: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10919: Collective; No Fortran Support
10921: Input Parameters:
10922: + mat - the matrix
10923: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10924: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10926: Output Parameter:
10927: . values - the block inverses in column major order (FORTRAN-like)
10929: Level: advanced
10931: Notes:
10932: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10934: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10936: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10937: @*/
10938: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10939: {
10940: PetscFunctionBegin;
10942: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10943: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10944: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10945: PetscFunctionReturn(PETSC_SUCCESS);
10946: }
10948: /*@
10949: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10951: Collective
10953: Input Parameters:
10954: + A - the matrix
10955: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10957: Level: advanced
10959: Note:
10960: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10962: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10963: @*/
10964: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10965: {
10966: const PetscScalar *vals;
10967: PetscInt *dnnz;
10968: PetscInt m, rstart, rend, bs, i, j;
10970: PetscFunctionBegin;
10971: PetscCall(MatInvertBlockDiagonal(A, &vals));
10972: PetscCall(MatGetBlockSize(A, &bs));
10973: PetscCall(MatGetLocalSize(A, &m, NULL));
10974: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10975: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10976: PetscCall(PetscMalloc1(m / bs, &dnnz));
10977: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10978: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10979: PetscCall(PetscFree(dnnz));
10980: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10981: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10982: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10983: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
10984: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10985: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10986: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
10987: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10988: PetscFunctionReturn(PETSC_SUCCESS);
10989: }
10991: /*@
10992: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10993: via `MatTransposeColoringCreate()`.
10995: Collective
10997: Input Parameter:
10998: . c - coloring context
11000: Level: intermediate
11002: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
11003: @*/
11004: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
11005: {
11006: MatTransposeColoring matcolor = *c;
11008: PetscFunctionBegin;
11009: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
11010: if (--((PetscObject)matcolor)->refct > 0) {
11011: matcolor = NULL;
11012: PetscFunctionReturn(PETSC_SUCCESS);
11013: }
11015: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
11016: PetscCall(PetscFree(matcolor->rows));
11017: PetscCall(PetscFree(matcolor->den2sp));
11018: PetscCall(PetscFree(matcolor->colorforcol));
11019: PetscCall(PetscFree(matcolor->columns));
11020: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11021: PetscCall(PetscHeaderDestroy(c));
11022: PetscFunctionReturn(PETSC_SUCCESS);
11023: }
11025: /*@
11026: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11027: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11028: `MatTransposeColoring` to sparse `B`.
11030: Collective
11032: Input Parameters:
11033: + coloring - coloring context created with `MatTransposeColoringCreate()`
11034: - B - sparse matrix
11036: Output Parameter:
11037: . Btdense - dense matrix $B^T$
11039: Level: developer
11041: Note:
11042: These are used internally for some implementations of `MatRARt()`
11044: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11045: @*/
11046: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11047: {
11048: PetscFunctionBegin;
11053: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11054: PetscFunctionReturn(PETSC_SUCCESS);
11055: }
11057: /*@
11058: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11059: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11060: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11061: $C_{sp}$ from $C_{den}$.
11063: Collective
11065: Input Parameters:
11066: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11067: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11069: Output Parameter:
11070: . Csp - sparse matrix
11072: Level: developer
11074: Note:
11075: These are used internally for some implementations of `MatRARt()`
11077: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11078: @*/
11079: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11080: {
11081: PetscFunctionBegin;
11086: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11087: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11088: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11089: PetscFunctionReturn(PETSC_SUCCESS);
11090: }
11092: /*@
11093: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11095: Collective
11097: Input Parameters:
11098: + mat - the matrix product C
11099: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11101: Output Parameter:
11102: . color - the new coloring context
11104: Level: intermediate
11106: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11107: `MatTransColoringApplyDenToSp()`
11108: @*/
11109: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11110: {
11111: MatTransposeColoring c;
11112: MPI_Comm comm;
11114: PetscFunctionBegin;
11115: PetscAssertPointer(color, 3);
11117: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11118: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11119: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11120: c->ctype = iscoloring->ctype;
11121: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11122: *color = c;
11123: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11124: PetscFunctionReturn(PETSC_SUCCESS);
11125: }
11127: /*@
11128: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11129: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11131: Not Collective
11133: Input Parameter:
11134: . mat - the matrix
11136: Output Parameter:
11137: . state - the current state
11139: Level: intermediate
11141: Notes:
11142: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11143: different matrices
11145: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11147: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11149: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11150: @*/
11151: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11152: {
11153: PetscFunctionBegin;
11155: *state = mat->nonzerostate;
11156: PetscFunctionReturn(PETSC_SUCCESS);
11157: }
11159: /*@
11160: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11161: matrices from each processor
11163: Collective
11165: Input Parameters:
11166: + comm - the communicators the parallel matrix will live on
11167: . seqmat - the input sequential matrices
11168: . n - number of local columns (or `PETSC_DECIDE`)
11169: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11171: Output Parameter:
11172: . mpimat - the parallel matrix generated
11174: Level: developer
11176: Note:
11177: The number of columns of the matrix in EACH processor MUST be the same.
11179: .seealso: [](ch_matrices), `Mat`
11180: @*/
11181: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11182: {
11183: PetscMPIInt size;
11185: PetscFunctionBegin;
11186: PetscCallMPI(MPI_Comm_size(comm, &size));
11187: if (size == 1) {
11188: if (reuse == MAT_INITIAL_MATRIX) {
11189: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11190: } else {
11191: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11192: }
11193: PetscFunctionReturn(PETSC_SUCCESS);
11194: }
11196: 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");
11198: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11199: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11200: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11201: PetscFunctionReturn(PETSC_SUCCESS);
11202: }
11204: /*@
11205: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11207: Collective
11209: Input Parameters:
11210: + A - the matrix to create subdomains from
11211: - N - requested number of subdomains
11213: Output Parameters:
11214: + n - number of subdomains resulting on this MPI process
11215: - iss - `IS` list with indices of subdomains on this MPI process
11217: Level: advanced
11219: Note:
11220: The number of subdomains must be smaller than the communicator size
11222: .seealso: [](ch_matrices), `Mat`, `IS`
11223: @*/
11224: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11225: {
11226: MPI_Comm comm, subcomm;
11227: PetscMPIInt size, rank, color;
11228: PetscInt rstart, rend, k;
11230: PetscFunctionBegin;
11231: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11232: PetscCallMPI(MPI_Comm_size(comm, &size));
11233: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11234: 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);
11235: *n = 1;
11236: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11237: color = rank / k;
11238: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11239: PetscCall(PetscMalloc1(1, iss));
11240: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11241: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11242: PetscCallMPI(MPI_Comm_free(&subcomm));
11243: PetscFunctionReturn(PETSC_SUCCESS);
11244: }
11246: /*@
11247: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11249: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11250: If they are not the same, uses `MatMatMatMult()`.
11252: Once the coarse grid problem is constructed, correct for interpolation operators
11253: that are not of full rank, which can legitimately happen in the case of non-nested
11254: geometric multigrid.
11256: Input Parameters:
11257: + restrct - restriction operator
11258: . dA - fine grid matrix
11259: . interpolate - interpolation operator
11260: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11261: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11263: Output Parameter:
11264: . A - the Galerkin coarse matrix
11266: Options Database Key:
11267: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11269: Level: developer
11271: Note:
11272: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11274: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11275: @*/
11276: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11277: {
11278: IS zerorows;
11279: Vec diag;
11281: PetscFunctionBegin;
11282: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11283: /* Construct the coarse grid matrix */
11284: if (interpolate == restrct) {
11285: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11286: } else {
11287: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11288: }
11290: /* If the interpolation matrix is not of full rank, A will have zero rows.
11291: This can legitimately happen in the case of non-nested geometric multigrid.
11292: In that event, we set the rows of the matrix to the rows of the identity,
11293: ignoring the equations (as the RHS will also be zero). */
11295: PetscCall(MatFindZeroRows(*A, &zerorows));
11297: if (zerorows != NULL) { /* if there are any zero rows */
11298: PetscCall(MatCreateVecs(*A, &diag, NULL));
11299: PetscCall(MatGetDiagonal(*A, diag));
11300: PetscCall(VecISSet(diag, zerorows, 1.0));
11301: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11302: PetscCall(VecDestroy(&diag));
11303: PetscCall(ISDestroy(&zerorows));
11304: }
11305: PetscFunctionReturn(PETSC_SUCCESS);
11306: }
11308: /*@C
11309: MatSetOperation - Allows user to set a matrix operation for any matrix type
11311: Logically Collective
11313: Input Parameters:
11314: + mat - the matrix
11315: . op - the name of the operation
11316: - f - the function that provides the operation
11318: Level: developer
11320: Example Usage:
11321: .vb
11322: extern PetscErrorCode usermult(Mat, Vec, Vec);
11324: PetscCall(MatCreateXXX(comm, ..., &A));
11325: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11326: .ve
11328: Notes:
11329: See the file `include/petscmat.h` for a complete list of matrix
11330: operations, which all have the form MATOP_<OPERATION>, where
11331: <OPERATION> is the name (in all capital letters) of the
11332: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11334: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11335: sequence as the usual matrix interface routines, since they
11336: are intended to be accessed via the usual matrix interface
11337: routines, e.g.,
11338: .vb
11339: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11340: .ve
11342: In particular each function MUST return `PETSC_SUCCESS` on success and
11343: nonzero on failure.
11345: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11347: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11348: @*/
11349: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11350: {
11351: PetscFunctionBegin;
11353: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11354: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11355: PetscFunctionReturn(PETSC_SUCCESS);
11356: }
11358: /*@C
11359: MatGetOperation - Gets a matrix operation for any matrix type.
11361: Not Collective
11363: Input Parameters:
11364: + mat - the matrix
11365: - op - the name of the operation
11367: Output Parameter:
11368: . f - the function that provides the operation
11370: Level: developer
11372: Example Usage:
11373: .vb
11374: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11376: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11377: .ve
11379: Notes:
11380: See the file `include/petscmat.h` for a complete list of matrix
11381: operations, which all have the form MATOP_<OPERATION>, where
11382: <OPERATION> is the name (in all capital letters) of the
11383: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11385: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11387: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11388: @*/
11389: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11390: {
11391: PetscFunctionBegin;
11393: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11394: PetscFunctionReturn(PETSC_SUCCESS);
11395: }
11397: /*@
11398: MatHasOperation - Determines whether the given matrix supports the particular operation.
11400: Not Collective
11402: Input Parameters:
11403: + mat - the matrix
11404: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11406: Output Parameter:
11407: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11409: Level: advanced
11411: Note:
11412: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11414: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11415: @*/
11416: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11417: {
11418: PetscFunctionBegin;
11420: PetscAssertPointer(has, 3);
11421: if (mat->ops->hasoperation) {
11422: PetscUseTypeMethod(mat, hasoperation, op, has);
11423: } else {
11424: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11425: else {
11426: *has = PETSC_FALSE;
11427: if (op == MATOP_CREATE_SUBMATRIX) {
11428: PetscMPIInt size;
11430: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11431: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11432: }
11433: }
11434: }
11435: PetscFunctionReturn(PETSC_SUCCESS);
11436: }
11438: /*@
11439: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11441: Collective
11443: Input Parameter:
11444: . mat - the matrix
11446: Output Parameter:
11447: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11449: Level: beginner
11451: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11452: @*/
11453: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11454: {
11455: PetscFunctionBegin;
11458: PetscAssertPointer(cong, 2);
11459: if (!mat->rmap || !mat->cmap) {
11460: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11461: PetscFunctionReturn(PETSC_SUCCESS);
11462: }
11463: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11464: PetscCall(PetscLayoutSetUp(mat->rmap));
11465: PetscCall(PetscLayoutSetUp(mat->cmap));
11466: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11467: if (*cong) mat->congruentlayouts = 1;
11468: else mat->congruentlayouts = 0;
11469: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11470: PetscFunctionReturn(PETSC_SUCCESS);
11471: }
11473: PetscErrorCode MatSetInf(Mat A)
11474: {
11475: PetscFunctionBegin;
11476: PetscUseTypeMethod(A, setinf);
11477: PetscFunctionReturn(PETSC_SUCCESS);
11478: }
11480: /*@
11481: 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
11482: and possibly removes small values from the graph structure.
11484: Collective
11486: Input Parameters:
11487: + A - the matrix
11488: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11489: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11490: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11491: . num_idx - size of 'index' array
11492: - index - array of block indices to use for graph strength of connection weight
11494: Output Parameter:
11495: . graph - the resulting graph
11497: Level: advanced
11499: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11500: @*/
11501: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11502: {
11503: PetscFunctionBegin;
11507: PetscAssertPointer(graph, 7);
11508: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11509: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11510: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11511: PetscFunctionReturn(PETSC_SUCCESS);
11512: }
11514: /*@
11515: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11516: meaning the same memory is used for the matrix, and no new memory is allocated.
11518: Collective
11520: Input Parameters:
11521: + A - the matrix
11522: - 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
11524: Level: intermediate
11526: Developer Note:
11527: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11528: of the arrays in the data structure are unneeded.
11530: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11531: @*/
11532: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11533: {
11534: PetscFunctionBegin;
11536: PetscUseTypeMethod(A, eliminatezeros, keep);
11537: PetscFunctionReturn(PETSC_SUCCESS);
11538: }
11540: /*@C
11541: MatGetCurrentMemType - Get the memory location of the matrix
11543: Not Collective, but the result will be the same on all MPI processes
11545: Input Parameter:
11546: . A - the matrix whose memory type we are checking
11548: Output Parameter:
11549: . m - the memory type
11551: Level: intermediate
11553: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11554: @*/
11555: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11556: {
11557: PetscFunctionBegin;
11559: PetscAssertPointer(m, 2);
11560: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11561: else *m = PETSC_MEMTYPE_HOST;
11562: PetscFunctionReturn(PETSC_SUCCESS);
11563: }