Actual source code: matrix.c
1: /*
2: This is where the abstract matrix operations are defined
3: Portions of this code are under:
4: Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
5: */
7: #include <petsc/private/matimpl.h>
8: #include <petsc/private/isimpl.h>
9: #include <petsc/private/vecimpl.h>
11: /* Logging support */
12: PetscClassId MAT_CLASSID;
13: PetscClassId MAT_COLORING_CLASSID;
14: PetscClassId MAT_FDCOLORING_CLASSID;
15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;
17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
18: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
19: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
20: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
21: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
22: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
23: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
24: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
25: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
26: PetscLogEvent MAT_TransposeColoringCreate;
27: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
28: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
29: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
30: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
31: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
32: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
33: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
34: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
35: PetscLogEvent MAT_GetMultiProcBlock;
36: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
37: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
38: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
39: PetscLogEvent MAT_SetValuesBatch;
40: PetscLogEvent MAT_ViennaCLCopyToGPU;
41: PetscLogEvent MAT_CUDACopyToGPU;
42: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
43: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
44: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
45: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
46: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
48: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
50: /*@
51: MatSetRandom - Sets all components of a matrix to random numbers.
53: Logically Collective
55: Input Parameters:
56: + x - the matrix
57: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
58: it will create one internally.
60: Example:
61: .vb
62: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
63: MatSetRandom(x,rctx);
64: PetscRandomDestroy(rctx);
65: .ve
67: Level: intermediate
69: Notes:
70: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
72: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
74: It generates an error if used on unassembled sparse matrices that have not been preallocated.
76: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
77: @*/
78: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
79: {
80: PetscRandom randObj = NULL;
82: PetscFunctionBegin;
86: MatCheckPreallocated(x, 1);
88: if (!rctx) {
89: MPI_Comm comm;
90: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
91: PetscCall(PetscRandomCreate(comm, &randObj));
92: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
93: PetscCall(PetscRandomSetFromOptions(randObj));
94: rctx = randObj;
95: }
96: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
97: PetscUseTypeMethod(x, setrandom, rctx);
98: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
100: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
101: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
102: PetscCall(PetscRandomDestroy(&randObj));
103: PetscFunctionReturn(PETSC_SUCCESS);
104: }
106: /*@
107: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
109: Logically Collective
111: Input Parameter:
112: . mat - the factored matrix
114: Output Parameters:
115: + pivot - the pivot value computed
116: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
117: the share the matrix
119: Level: advanced
121: Notes:
122: This routine does not work for factorizations done with external packages.
124: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
126: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
128: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
129: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
130: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
131: @*/
132: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
133: {
134: PetscFunctionBegin;
136: PetscAssertPointer(pivot, 2);
137: PetscAssertPointer(row, 3);
138: *pivot = mat->factorerror_zeropivot_value;
139: *row = mat->factorerror_zeropivot_row;
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: /*@
144: MatFactorGetError - gets the error code from a factorization
146: Logically Collective
148: Input Parameter:
149: . mat - the factored matrix
151: Output Parameter:
152: . err - the error code
154: Level: advanced
156: Note:
157: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
159: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
160: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
161: @*/
162: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
163: {
164: PetscFunctionBegin;
166: PetscAssertPointer(err, 2);
167: *err = mat->factorerrortype;
168: PetscFunctionReturn(PETSC_SUCCESS);
169: }
171: /*@
172: MatFactorClearError - clears the error code in a factorization
174: Logically Collective
176: Input Parameter:
177: . mat - the factored matrix
179: Level: developer
181: Note:
182: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
184: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
185: `MatGetErrorCode()`, `MatFactorError`
186: @*/
187: PetscErrorCode MatFactorClearError(Mat mat)
188: {
189: PetscFunctionBegin;
191: mat->factorerrortype = MAT_FACTOR_NOERROR;
192: mat->factorerror_zeropivot_value = 0.0;
193: mat->factorerror_zeropivot_row = 0;
194: PetscFunctionReturn(PETSC_SUCCESS);
195: }
197: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
198: {
199: Vec r, l;
200: const PetscScalar *al;
201: PetscInt i, nz, gnz, N, n, st;
203: PetscFunctionBegin;
204: PetscCall(MatCreateVecs(mat, &r, &l));
205: if (!cols) { /* nonzero rows */
206: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
207: PetscCall(MatGetSize(mat, &N, NULL));
208: PetscCall(MatGetLocalSize(mat, &n, NULL));
209: PetscCall(VecSet(l, 0.0));
210: PetscCall(VecSetRandom(r, NULL));
211: PetscCall(MatMult(mat, r, l));
212: PetscCall(VecGetArrayRead(l, &al));
213: } else { /* nonzero columns */
214: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
215: PetscCall(MatGetSize(mat, NULL, &N));
216: PetscCall(MatGetLocalSize(mat, NULL, &n));
217: PetscCall(VecSet(r, 0.0));
218: PetscCall(VecSetRandom(l, NULL));
219: PetscCall(MatMultTranspose(mat, l, r));
220: PetscCall(VecGetArrayRead(r, &al));
221: }
222: if (tol <= 0.0) {
223: for (i = 0, nz = 0; i < n; i++)
224: if (al[i] != 0.0) nz++;
225: } else {
226: for (i = 0, nz = 0; i < n; i++)
227: if (PetscAbsScalar(al[i]) > tol) nz++;
228: }
229: PetscCall(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
230: if (gnz != N) {
231: PetscInt *nzr;
232: PetscCall(PetscMalloc1(nz, &nzr));
233: if (nz) {
234: if (tol < 0) {
235: for (i = 0, nz = 0; i < n; i++)
236: if (al[i] != 0.0) nzr[nz++] = i + st;
237: } else {
238: for (i = 0, nz = 0; i < n; i++)
239: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
240: }
241: }
242: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
243: } else *nonzero = NULL;
244: if (!cols) { /* nonzero rows */
245: PetscCall(VecRestoreArrayRead(l, &al));
246: } else {
247: PetscCall(VecRestoreArrayRead(r, &al));
248: }
249: PetscCall(VecDestroy(&l));
250: PetscCall(VecDestroy(&r));
251: PetscFunctionReturn(PETSC_SUCCESS);
252: }
254: /*@
255: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
257: Input Parameter:
258: . mat - the matrix
260: Output Parameter:
261: . keptrows - the rows that are not completely zero
263: Level: intermediate
265: Note:
266: `keptrows` is set to `NULL` if all rows are nonzero.
268: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
269: @*/
270: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
271: {
272: PetscFunctionBegin;
275: PetscAssertPointer(keptrows, 2);
276: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
277: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
278: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
279: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
280: PetscFunctionReturn(PETSC_SUCCESS);
281: }
283: /*@
284: MatFindZeroRows - Locate all rows that are completely zero in the matrix
286: Input Parameter:
287: . mat - the matrix
289: Output Parameter:
290: . zerorows - the rows that are completely zero
292: Level: intermediate
294: Note:
295: `zerorows` is set to `NULL` if no rows are zero.
297: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
298: @*/
299: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
300: {
301: IS keptrows;
302: PetscInt m, n;
304: PetscFunctionBegin;
307: PetscAssertPointer(zerorows, 2);
308: PetscCall(MatFindNonzeroRows(mat, &keptrows));
309: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
310: In keeping with this convention, we set zerorows to NULL if there are no zero
311: rows. */
312: if (keptrows == NULL) {
313: *zerorows = NULL;
314: } else {
315: PetscCall(MatGetOwnershipRange(mat, &m, &n));
316: PetscCall(ISComplement(keptrows, m, n, zerorows));
317: PetscCall(ISDestroy(&keptrows));
318: }
319: PetscFunctionReturn(PETSC_SUCCESS);
320: }
322: /*@
323: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
325: Not Collective
327: Input Parameter:
328: . A - the matrix
330: Output Parameter:
331: . a - the diagonal part (which is a SEQUENTIAL matrix)
333: Level: advanced
335: Notes:
336: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
338: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
340: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
341: @*/
342: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
343: {
344: PetscFunctionBegin;
347: PetscAssertPointer(a, 2);
348: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
349: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
350: else {
351: PetscMPIInt size;
353: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
354: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
355: *a = A;
356: }
357: PetscFunctionReturn(PETSC_SUCCESS);
358: }
360: /*@
361: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
363: Collective
365: Input Parameter:
366: . mat - the matrix
368: Output Parameter:
369: . trace - the sum of the diagonal entries
371: Level: advanced
373: .seealso: [](ch_matrices), `Mat`
374: @*/
375: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
376: {
377: Vec diag;
379: PetscFunctionBegin;
381: PetscAssertPointer(trace, 2);
382: PetscCall(MatCreateVecs(mat, &diag, NULL));
383: PetscCall(MatGetDiagonal(mat, diag));
384: PetscCall(VecSum(diag, trace));
385: PetscCall(VecDestroy(&diag));
386: PetscFunctionReturn(PETSC_SUCCESS);
387: }
389: /*@
390: MatRealPart - Zeros out the imaginary part of the matrix
392: Logically Collective
394: Input Parameter:
395: . mat - the matrix
397: Level: advanced
399: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
400: @*/
401: PetscErrorCode MatRealPart(Mat mat)
402: {
403: PetscFunctionBegin;
406: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
407: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
408: MatCheckPreallocated(mat, 1);
409: PetscUseTypeMethod(mat, realpart);
410: PetscFunctionReturn(PETSC_SUCCESS);
411: }
413: /*@C
414: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
416: Collective
418: Input Parameter:
419: . mat - the matrix
421: Output Parameters:
422: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
423: - ghosts - the global indices of the ghost points
425: Level: advanced
427: Note:
428: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
430: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
431: @*/
432: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
433: {
434: PetscFunctionBegin;
437: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
438: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
439: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
440: else {
441: if (nghosts) *nghosts = 0;
442: if (ghosts) *ghosts = NULL;
443: }
444: PetscFunctionReturn(PETSC_SUCCESS);
445: }
447: /*@
448: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
450: Logically Collective
452: Input Parameter:
453: . mat - the matrix
455: Level: advanced
457: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
458: @*/
459: PetscErrorCode MatImaginaryPart(Mat mat)
460: {
461: PetscFunctionBegin;
464: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
465: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
466: MatCheckPreallocated(mat, 1);
467: PetscUseTypeMethod(mat, imaginarypart);
468: PetscFunctionReturn(PETSC_SUCCESS);
469: }
471: /*@
472: MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure
474: Not Collective
476: Input Parameter:
477: . mat - the matrix
479: Output Parameters:
480: + missing - is any diagonal entry missing
481: - dd - first diagonal entry that is missing (optional) on this process
483: Level: advanced
485: Note:
486: This does not return diagonal entries that are in the nonzero structure but happen to have a zero numerical value
488: .seealso: [](ch_matrices), `Mat`
489: @*/
490: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
491: {
492: PetscFunctionBegin;
495: PetscAssertPointer(missing, 2);
496: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
497: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
498: PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
499: PetscFunctionReturn(PETSC_SUCCESS);
500: }
502: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
503: /*@C
504: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
505: for each row that you get to ensure that your application does
506: not bleed memory.
508: Not Collective
510: Input Parameters:
511: + mat - the matrix
512: - row - the row to get
514: Output Parameters:
515: + ncols - if not `NULL`, the number of nonzeros in `row`
516: . cols - if not `NULL`, the column numbers
517: - vals - if not `NULL`, the numerical values
519: Level: advanced
521: Notes:
522: This routine is provided for people who need to have direct access
523: to the structure of a matrix. We hope that we provide enough
524: high-level matrix routines that few users will need it.
526: `MatGetRow()` always returns 0-based column indices, regardless of
527: whether the internal representation is 0-based (default) or 1-based.
529: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
530: not wish to extract these quantities.
532: The user can only examine the values extracted with `MatGetRow()`;
533: the values CANNOT be altered. To change the matrix entries, one
534: must use `MatSetValues()`.
536: You can only have one call to `MatGetRow()` outstanding for a particular
537: matrix at a time, per processor. `MatGetRow()` can only obtain rows
538: associated with the given processor, it cannot get rows from the
539: other processors; for that we suggest using `MatCreateSubMatrices()`, then
540: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
541: is in the global number of rows.
543: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
545: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
547: Fortran Note:
548: The calling sequence is
549: .vb
550: MatGetRow(matrix,row,ncols,cols,values,ierr)
551: Mat matrix (input)
552: integer row (input)
553: integer ncols (output)
554: integer cols(maxcols) (output)
555: double precision (or double complex) values(maxcols) output
556: .ve
557: where maxcols >= maximum nonzeros in any row of the matrix.
559: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
560: @*/
561: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
562: {
563: PetscInt incols;
565: PetscFunctionBegin;
568: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
569: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
570: MatCheckPreallocated(mat, 1);
571: 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);
572: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
573: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
574: if (ncols) *ncols = incols;
575: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
576: PetscFunctionReturn(PETSC_SUCCESS);
577: }
579: /*@
580: MatConjugate - replaces the matrix values with their complex conjugates
582: Logically Collective
584: Input Parameter:
585: . mat - the matrix
587: Level: advanced
589: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
590: @*/
591: PetscErrorCode MatConjugate(Mat mat)
592: {
593: PetscFunctionBegin;
595: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
596: if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
597: PetscUseTypeMethod(mat, conjugate);
598: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
599: }
600: PetscFunctionReturn(PETSC_SUCCESS);
601: }
603: /*@C
604: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
606: Not Collective
608: Input Parameters:
609: + mat - the matrix
610: . row - the row to get
611: . ncols - the number of nonzeros
612: . cols - the columns of the nonzeros
613: - vals - if nonzero the column values
615: Level: advanced
617: Notes:
618: This routine should be called after you have finished examining the entries.
620: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
621: us of the array after it has been restored. If you pass `NULL`, it will
622: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
624: Fortran Notes:
625: The calling sequence is
626: .vb
627: MatRestoreRow(matrix,row,ncols,cols,values,ierr)
628: Mat matrix (input)
629: integer row (input)
630: integer ncols (output)
631: integer cols(maxcols) (output)
632: double precision (or double complex) values(maxcols) output
633: .ve
634: Where maxcols >= maximum nonzeros in any row of the matrix.
636: In Fortran `MatRestoreRow()` MUST be called after `MatGetRow()`
637: before another call to `MatGetRow()` can be made.
639: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
640: @*/
641: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
642: {
643: PetscFunctionBegin;
645: if (ncols) PetscAssertPointer(ncols, 3);
646: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
647: if (!mat->ops->restorerow) PetscFunctionReturn(PETSC_SUCCESS);
648: PetscUseTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
649: if (ncols) *ncols = 0;
650: if (cols) *cols = NULL;
651: if (vals) *vals = NULL;
652: PetscFunctionReturn(PETSC_SUCCESS);
653: }
655: /*@
656: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
657: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
659: Not Collective
661: Input Parameter:
662: . mat - the matrix
664: Level: advanced
666: Note:
667: 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.
669: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
670: @*/
671: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
672: {
673: PetscFunctionBegin;
676: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
677: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
678: MatCheckPreallocated(mat, 1);
679: if (!mat->ops->getrowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
680: PetscUseTypeMethod(mat, getrowuppertriangular);
681: PetscFunctionReturn(PETSC_SUCCESS);
682: }
684: /*@
685: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
687: Not Collective
689: Input Parameter:
690: . mat - the matrix
692: Level: advanced
694: Note:
695: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
697: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
698: @*/
699: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
700: {
701: PetscFunctionBegin;
704: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
705: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
706: MatCheckPreallocated(mat, 1);
707: if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
708: PetscUseTypeMethod(mat, restorerowuppertriangular);
709: PetscFunctionReturn(PETSC_SUCCESS);
710: }
712: /*@C
713: MatSetOptionsPrefix - Sets the prefix used for searching for all
714: `Mat` options in the database.
716: Logically Collective
718: Input Parameters:
719: + A - the matrix
720: - prefix - the prefix to prepend to all option names
722: Level: advanced
724: Notes:
725: A hyphen (-) must NOT be given at the beginning of the prefix name.
726: The first character of all runtime options is AUTOMATICALLY the hyphen.
728: This is NOT used for options for the factorization of the matrix. Normally the
729: prefix is automatically passed in from the PC calling the factorization. To set
730: it directly use `MatSetOptionsPrefixFactor()`
732: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
733: @*/
734: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
735: {
736: PetscFunctionBegin;
738: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
739: PetscFunctionReturn(PETSC_SUCCESS);
740: }
742: /*@C
743: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
744: for matrices created with `MatGetFactor()`
746: Logically Collective
748: Input Parameters:
749: + A - the matrix
750: - prefix - the prefix to prepend to all option names for the factored matrix
752: Level: developer
754: Notes:
755: A hyphen (-) must NOT be given at the beginning of the prefix name.
756: The first character of all runtime options is AUTOMATICALLY the hyphen.
758: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
759: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
761: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
762: @*/
763: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
764: {
765: PetscFunctionBegin;
767: if (prefix) {
768: PetscAssertPointer(prefix, 2);
769: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
770: if (prefix != A->factorprefix) {
771: PetscCall(PetscFree(A->factorprefix));
772: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
773: }
774: } else PetscCall(PetscFree(A->factorprefix));
775: PetscFunctionReturn(PETSC_SUCCESS);
776: }
778: /*@C
779: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
780: for matrices created with `MatGetFactor()`
782: Logically Collective
784: Input Parameters:
785: + A - the matrix
786: - prefix - the prefix to prepend to all option names for the factored matrix
788: Level: developer
790: Notes:
791: A hyphen (-) must NOT be given at the beginning of the prefix name.
792: The first character of all runtime options is AUTOMATICALLY the hyphen.
794: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
795: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
797: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
798: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
799: `MatSetOptionsPrefix()`
800: @*/
801: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
802: {
803: size_t len1, len2, new_len;
805: PetscFunctionBegin;
807: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
808: if (!A->factorprefix) {
809: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
810: PetscFunctionReturn(PETSC_SUCCESS);
811: }
812: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
814: PetscCall(PetscStrlen(A->factorprefix, &len1));
815: PetscCall(PetscStrlen(prefix, &len2));
816: new_len = len1 + len2 + 1;
817: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
818: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
819: PetscFunctionReturn(PETSC_SUCCESS);
820: }
822: /*@C
823: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
824: matrix options in the database.
826: Logically Collective
828: Input Parameters:
829: + A - the matrix
830: - prefix - the prefix to prepend to all option names
832: Level: advanced
834: Note:
835: A hyphen (-) must NOT be given at the beginning of the prefix name.
836: The first character of all runtime options is AUTOMATICALLY the hyphen.
838: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
839: @*/
840: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
841: {
842: PetscFunctionBegin;
844: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
845: PetscFunctionReturn(PETSC_SUCCESS);
846: }
848: /*@C
849: MatGetOptionsPrefix - Gets the prefix used for searching for all
850: matrix options in the database.
852: Not Collective
854: Input Parameter:
855: . A - the matrix
857: Output Parameter:
858: . prefix - pointer to the prefix string used
860: Level: advanced
862: Fortran Note:
863: The user should pass in a string `prefix` of
864: sufficient length to hold the prefix.
866: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
867: @*/
868: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
869: {
870: PetscFunctionBegin;
872: PetscAssertPointer(prefix, 2);
873: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
874: PetscFunctionReturn(PETSC_SUCCESS);
875: }
877: /*@
878: MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by the user.
880: Collective
882: Input Parameter:
883: . A - the matrix
885: Level: beginner
887: Notes:
888: The allocated memory will be shrunk after calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
890: Users can reset the preallocation to access the original memory.
892: Currently only supported for `MATAIJ` matrices.
894: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
895: @*/
896: PetscErrorCode MatResetPreallocation(Mat A)
897: {
898: PetscFunctionBegin;
901: PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
902: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
903: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
904: PetscFunctionReturn(PETSC_SUCCESS);
905: }
907: /*@
908: MatSetUp - Sets up the internal matrix data structures for later use.
910: Collective
912: Input Parameter:
913: . A - the matrix
915: Level: intermediate
917: Notes:
918: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
919: setting values in the matrix.
921: This routine is called internally by other matrix functions when needed so rarely needs to be called by users
923: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
924: @*/
925: PetscErrorCode MatSetUp(Mat A)
926: {
927: PetscFunctionBegin;
929: if (!((PetscObject)A)->type_name) {
930: PetscMPIInt size;
932: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
933: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
934: }
935: if (!A->preallocated) PetscTryTypeMethod(A, setup);
936: PetscCall(PetscLayoutSetUp(A->rmap));
937: PetscCall(PetscLayoutSetUp(A->cmap));
938: A->preallocated = PETSC_TRUE;
939: PetscFunctionReturn(PETSC_SUCCESS);
940: }
942: #if defined(PETSC_HAVE_SAWS)
943: #include <petscviewersaws.h>
944: #endif
946: /*
947: If threadsafety is on extraneous matrices may be printed
949: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
950: */
951: #if !defined(PETSC_HAVE_THREADSAFETY)
952: static PetscInt insidematview = 0;
953: #endif
955: /*@C
956: MatViewFromOptions - View properties of the matrix based on options set in the options database
958: Collective
960: Input Parameters:
961: + A - the matrix
962: . obj - optional additional object that provides the options prefix to use
963: - name - command line option
965: Options Database Key:
966: . -mat_view [viewertype]:... - the viewer and its options
968: Level: intermediate
970: Note:
971: .vb
972: If no value is provided ascii:stdout is used
973: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
974: for example ascii::ascii_info prints just the information about the object not all details
975: unless :append is given filename opens in write mode, overwriting what was already there
976: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
977: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
978: socket[:port] defaults to the standard output port
979: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
980: .ve
982: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
983: @*/
984: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
985: {
986: PetscFunctionBegin;
988: #if !defined(PETSC_HAVE_THREADSAFETY)
989: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
990: #endif
991: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
992: PetscFunctionReturn(PETSC_SUCCESS);
993: }
995: /*@C
996: MatView - display information about a matrix in a variety ways
998: Collective on viewer
1000: Input Parameters:
1001: + mat - the matrix
1002: - viewer - visualization context
1004: Options Database Keys:
1005: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1006: . -mat_view ::ascii_info_detail - Prints more detailed info
1007: . -mat_view - Prints matrix in ASCII format
1008: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1009: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1010: . -display <name> - Sets display name (default is host)
1011: . -draw_pause <sec> - Sets number of seconds to pause after display
1012: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1013: . -viewer_socket_machine <machine> - -
1014: . -viewer_socket_port <port> - -
1015: . -mat_view binary - save matrix to file in binary format
1016: - -viewer_binary_filename <name> - -
1018: Level: beginner
1020: Notes:
1021: The available visualization contexts include
1022: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1023: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1024: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1025: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1027: The user can open alternative visualization contexts with
1028: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1029: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
1030: specified file; corresponding input uses `MatLoad()`
1031: . `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
1032: an X window display
1033: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
1034: Currently only the `MATSEQDENSE` and `MATAIJ`
1035: matrix types support the Socket viewer.
1037: The user can call `PetscViewerPushFormat()` to specify the output
1038: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1039: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1040: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1041: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1042: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1043: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
1044: format common among all matrix types
1045: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
1046: format (which is in many cases the same as the default)
1047: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
1048: size and structure (not the matrix entries)
1049: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
1050: the matrix structure
1052: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1053: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1055: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1057: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1058: viewer is used.
1060: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1061: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1063: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1064: and then use the following mouse functions.
1065: .vb
1066: left mouse: zoom in
1067: middle mouse: zoom out
1068: right mouse: continue with the simulation
1069: .ve
1071: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1072: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1073: @*/
1074: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1075: {
1076: PetscInt rows, cols, rbs, cbs;
1077: PetscBool isascii, isstring, issaws;
1078: PetscViewerFormat format;
1079: PetscMPIInt size;
1081: PetscFunctionBegin;
1084: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1087: PetscCall(PetscViewerGetFormat(viewer, &format));
1088: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1089: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1091: #if !defined(PETSC_HAVE_THREADSAFETY)
1092: insidematview++;
1093: #endif
1094: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1095: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1096: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1097: 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");
1099: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1100: if (isascii) {
1101: if (!mat->preallocated) {
1102: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1103: #if !defined(PETSC_HAVE_THREADSAFETY)
1104: insidematview--;
1105: #endif
1106: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1107: PetscFunctionReturn(PETSC_SUCCESS);
1108: }
1109: if (!mat->assembled) {
1110: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1111: #if !defined(PETSC_HAVE_THREADSAFETY)
1112: insidematview--;
1113: #endif
1114: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1115: PetscFunctionReturn(PETSC_SUCCESS);
1116: }
1117: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1118: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1119: MatNullSpace nullsp, transnullsp;
1121: PetscCall(PetscViewerASCIIPushTab(viewer));
1122: PetscCall(MatGetSize(mat, &rows, &cols));
1123: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1124: if (rbs != 1 || cbs != 1) {
1125: 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" : ""));
1126: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1127: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1128: if (mat->factortype) {
1129: MatSolverType solver;
1130: PetscCall(MatFactorGetSolverType(mat, &solver));
1131: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1132: }
1133: if (mat->ops->getinfo) {
1134: MatInfo info;
1135: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1136: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1137: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1138: }
1139: PetscCall(MatGetNullSpace(mat, &nullsp));
1140: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1141: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1142: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1143: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1144: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1145: PetscCall(PetscViewerASCIIPushTab(viewer));
1146: PetscCall(MatProductView(mat, viewer));
1147: PetscCall(PetscViewerASCIIPopTab(viewer));
1148: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1149: IS tmp;
1151: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1152: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1153: PetscCall(PetscViewerASCIIPushTab(viewer));
1154: PetscCall(ISView(tmp, viewer));
1155: PetscCall(PetscViewerASCIIPopTab(viewer));
1156: PetscCall(ISDestroy(&tmp));
1157: }
1158: }
1159: } else if (issaws) {
1160: #if defined(PETSC_HAVE_SAWS)
1161: PetscMPIInt rank;
1163: PetscCall(PetscObjectName((PetscObject)mat));
1164: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1165: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1166: #endif
1167: } else if (isstring) {
1168: const char *type;
1169: PetscCall(MatGetType(mat, &type));
1170: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1171: PetscTryTypeMethod(mat, view, viewer);
1172: }
1173: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1174: PetscCall(PetscViewerASCIIPushTab(viewer));
1175: PetscUseTypeMethod(mat, viewnative, viewer);
1176: PetscCall(PetscViewerASCIIPopTab(viewer));
1177: } else if (mat->ops->view) {
1178: PetscCall(PetscViewerASCIIPushTab(viewer));
1179: PetscUseTypeMethod(mat, view, viewer);
1180: PetscCall(PetscViewerASCIIPopTab(viewer));
1181: }
1182: if (isascii) {
1183: PetscCall(PetscViewerGetFormat(viewer, &format));
1184: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1185: }
1186: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1187: #if !defined(PETSC_HAVE_THREADSAFETY)
1188: insidematview--;
1189: #endif
1190: PetscFunctionReturn(PETSC_SUCCESS);
1191: }
1193: #if defined(PETSC_USE_DEBUG)
1194: #include <../src/sys/totalview/tv_data_display.h>
1195: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1196: {
1197: TV_add_row("Local rows", "int", &mat->rmap->n);
1198: TV_add_row("Local columns", "int", &mat->cmap->n);
1199: TV_add_row("Global rows", "int", &mat->rmap->N);
1200: TV_add_row("Global columns", "int", &mat->cmap->N);
1201: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1202: return TV_format_OK;
1203: }
1204: #endif
1206: /*@C
1207: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1208: with `MatView()`. The matrix format is determined from the options database.
1209: Generates a parallel MPI matrix if the communicator has more than one
1210: processor. The default matrix type is `MATAIJ`.
1212: Collective
1214: Input Parameters:
1215: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1216: or some related function before a call to `MatLoad()`
1217: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1219: Options Database Key:
1220: . -matload_block_size <bs> - set block size
1222: Level: beginner
1224: Notes:
1225: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1226: `Mat` before calling this routine if you wish to set it from the options database.
1228: `MatLoad()` automatically loads into the options database any options
1229: given in the file filename.info where filename is the name of the file
1230: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1231: file will be ignored if you use the -viewer_binary_skip_info option.
1233: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1234: sets the default matrix type AIJ and sets the local and global sizes.
1235: If type and/or size is already set, then the same are used.
1237: In parallel, each processor can load a subset of rows (or the
1238: entire matrix). This routine is especially useful when a large
1239: matrix is stored on disk and only part of it is desired on each
1240: processor. For example, a parallel solver may access only some of
1241: the rows from each processor. The algorithm used here reads
1242: relatively small blocks of data rather than reading the entire
1243: matrix and then subsetting it.
1245: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1246: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1247: or the sequence like
1248: .vb
1249: `PetscViewer` v;
1250: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1251: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1252: `PetscViewerSetFromOptions`(v);
1253: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1254: `PetscViewerFileSetName`(v,"datafile");
1255: .ve
1256: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1257: $ -viewer_type {binary, hdf5}
1259: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1260: and src/mat/tutorials/ex10.c with the second approach.
1262: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1263: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1264: Multiple objects, both matrices and vectors, can be stored within the same file.
1265: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1267: Most users should not need to know the details of the binary storage
1268: format, since `MatLoad()` and `MatView()` completely hide these details.
1269: But for anyone who is interested, the standard binary matrix storage
1270: format is
1272: .vb
1273: PetscInt MAT_FILE_CLASSID
1274: PetscInt number of rows
1275: PetscInt number of columns
1276: PetscInt total number of nonzeros
1277: PetscInt *number nonzeros in each row
1278: PetscInt *column indices of all nonzeros (starting index is zero)
1279: PetscScalar *values of all nonzeros
1280: .ve
1281: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1282: 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
1283: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1285: PETSc automatically does the byte swapping for
1286: machines that store the bytes reversed. Thus if you write your own binary
1287: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1288: and `PetscBinaryWrite()` to see how this may be done.
1290: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1291: Each processor's chunk is loaded independently by its owning MPI process.
1292: Multiple objects, both matrices and vectors, can be stored within the same file.
1293: They are looked up by their PetscObject name.
1295: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1296: by default the same structure and naming of the AIJ arrays and column count
1297: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1298: $ save example.mat A b -v7.3
1299: can be directly read by this routine (see Reference 1 for details).
1301: Depending on your MATLAB version, this format might be a default,
1302: otherwise you can set it as default in Preferences.
1304: Unless -nocompression flag is used to save the file in MATLAB,
1305: PETSc must be configured with ZLIB package.
1307: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1309: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1311: Corresponding `MatView()` is not yet implemented.
1313: The loaded matrix is actually a transpose of the original one in MATLAB,
1314: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1315: With this format, matrix is automatically transposed by PETSc,
1316: unless the matrix is marked as SPD or symmetric
1317: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1319: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1321: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1322: @*/
1323: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1324: {
1325: PetscBool flg;
1327: PetscFunctionBegin;
1331: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1333: flg = PETSC_FALSE;
1334: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1335: if (flg) {
1336: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1337: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1338: }
1339: flg = PETSC_FALSE;
1340: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1341: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1343: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1344: PetscUseTypeMethod(mat, load, viewer);
1345: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1346: PetscFunctionReturn(PETSC_SUCCESS);
1347: }
1349: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1350: {
1351: Mat_Redundant *redund = *redundant;
1353: PetscFunctionBegin;
1354: if (redund) {
1355: if (redund->matseq) { /* via MatCreateSubMatrices() */
1356: PetscCall(ISDestroy(&redund->isrow));
1357: PetscCall(ISDestroy(&redund->iscol));
1358: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1359: } else {
1360: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1361: PetscCall(PetscFree(redund->sbuf_j));
1362: PetscCall(PetscFree(redund->sbuf_a));
1363: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1364: PetscCall(PetscFree(redund->rbuf_j[i]));
1365: PetscCall(PetscFree(redund->rbuf_a[i]));
1366: }
1367: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1368: }
1370: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1371: PetscCall(PetscFree(redund));
1372: }
1373: PetscFunctionReturn(PETSC_SUCCESS);
1374: }
1376: /*@C
1377: MatDestroy - Frees space taken by a matrix.
1379: Collective
1381: Input Parameter:
1382: . A - the matrix
1384: Level: beginner
1386: Developer Note:
1387: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1388: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1389: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1390: if changes are needed here.
1392: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1393: @*/
1394: PetscErrorCode MatDestroy(Mat *A)
1395: {
1396: PetscFunctionBegin;
1397: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1399: if (--((PetscObject)*A)->refct > 0) {
1400: *A = NULL;
1401: PetscFunctionReturn(PETSC_SUCCESS);
1402: }
1404: /* if memory was published with SAWs then destroy it */
1405: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1406: PetscTryTypeMethod(*A, destroy);
1408: PetscCall(PetscFree((*A)->factorprefix));
1409: PetscCall(PetscFree((*A)->defaultvectype));
1410: PetscCall(PetscFree((*A)->defaultrandtype));
1411: PetscCall(PetscFree((*A)->bsizes));
1412: PetscCall(PetscFree((*A)->solvertype));
1413: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1414: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1415: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1416: PetscCall(MatProductClear(*A));
1417: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1418: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1419: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1420: PetscCall(MatDestroy(&(*A)->schur));
1421: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1422: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1423: PetscCall(PetscHeaderDestroy(A));
1424: PetscFunctionReturn(PETSC_SUCCESS);
1425: }
1427: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1428: /*@C
1429: MatSetValues - Inserts or adds a block of values into a matrix.
1430: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1431: MUST be called after all calls to `MatSetValues()` have been completed.
1433: Not Collective
1435: Input Parameters:
1436: + mat - the matrix
1437: . v - a logically two-dimensional array of values
1438: . m - the number of rows
1439: . idxm - the global indices of the rows
1440: . n - the number of columns
1441: . idxn - the global indices of the columns
1442: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1444: Level: beginner
1446: Notes:
1447: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1449: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1450: options cannot be mixed without intervening calls to the assembly
1451: routines.
1453: `MatSetValues()` uses 0-based row and column numbers in Fortran
1454: as well as in C.
1456: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1457: simply ignored. This allows easily inserting element stiffness matrices
1458: with homogeneous Dirichlet boundary conditions that you don't want represented
1459: in the matrix.
1461: Efficiency Alert:
1462: The routine `MatSetValuesBlocked()` may offer much better efficiency
1463: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1465: Developer Note:
1466: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1467: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1469: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1470: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1471: @*/
1472: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1473: {
1474: PetscFunctionBeginHot;
1477: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1478: PetscAssertPointer(idxm, 3);
1479: PetscAssertPointer(idxn, 5);
1480: MatCheckPreallocated(mat, 1);
1482: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1483: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1485: if (PetscDefined(USE_DEBUG)) {
1486: PetscInt i, j;
1488: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1489: if (v) {
1490: for (i = 0; i < m; i++) {
1491: for (j = 0; j < n; j++) {
1492: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1493: #if defined(PETSC_USE_COMPLEX)
1494: 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]);
1495: #else
1496: 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]);
1497: #endif
1498: }
1499: }
1500: }
1501: 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);
1502: 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);
1503: }
1505: if (mat->assembled) {
1506: mat->was_assembled = PETSC_TRUE;
1507: mat->assembled = PETSC_FALSE;
1508: }
1509: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1510: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1511: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1512: PetscFunctionReturn(PETSC_SUCCESS);
1513: }
1515: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1516: /*@C
1517: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1518: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1519: MUST be called after all calls to `MatSetValues()` have been completed.
1521: Not Collective
1523: Input Parameters:
1524: + mat - the matrix
1525: . v - a logically two-dimensional array of values
1526: . ism - the rows to provide
1527: . isn - the columns to provide
1528: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1530: Level: beginner
1532: Notes:
1533: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1535: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1536: options cannot be mixed without intervening calls to the assembly
1537: routines.
1539: `MatSetValues()` uses 0-based row and column numbers in Fortran
1540: as well as in C.
1542: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1543: simply ignored. This allows easily inserting element stiffness matrices
1544: with homogeneous Dirichlet boundary conditions that you don't want represented
1545: in the matrix.
1547: Efficiency Alert:
1548: The routine `MatSetValuesBlocked()` may offer much better efficiency
1549: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1551: This is currently not optimized for any particular `ISType`
1553: Developer Note:
1554: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1555: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1557: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1558: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1559: @*/
1560: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1561: {
1562: PetscInt m, n;
1563: const PetscInt *rows, *cols;
1565: PetscFunctionBeginHot;
1567: PetscCall(ISGetIndices(ism, &rows));
1568: PetscCall(ISGetIndices(isn, &cols));
1569: PetscCall(ISGetLocalSize(ism, &m));
1570: PetscCall(ISGetLocalSize(isn, &n));
1571: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1572: PetscCall(ISRestoreIndices(ism, &rows));
1573: PetscCall(ISRestoreIndices(isn, &cols));
1574: PetscFunctionReturn(PETSC_SUCCESS);
1575: }
1577: /*@
1578: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1579: values into a matrix
1581: Not Collective
1583: Input Parameters:
1584: + mat - the matrix
1585: . row - the (block) row to set
1586: - v - a logically two-dimensional array of values
1588: Level: intermediate
1590: Notes:
1591: The values, `v`, are column-oriented (for the block version) and sorted
1593: All the nonzero values in `row` must be provided
1595: The matrix must have previously had its column indices set, likely by having been assembled.
1597: `row` must belong to this MPI process
1599: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1600: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1601: @*/
1602: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1603: {
1604: PetscInt globalrow;
1606: PetscFunctionBegin;
1609: PetscAssertPointer(v, 3);
1610: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1611: PetscCall(MatSetValuesRow(mat, globalrow, v));
1612: PetscFunctionReturn(PETSC_SUCCESS);
1613: }
1615: /*@
1616: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1617: values into a matrix
1619: Not Collective
1621: Input Parameters:
1622: + mat - the matrix
1623: . row - the (block) row to set
1624: - 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
1626: Level: advanced
1628: Notes:
1629: The values, `v`, are column-oriented for the block version.
1631: All the nonzeros in `row` must be provided
1633: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1635: `row` must belong to this process
1637: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1638: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1639: @*/
1640: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1641: {
1642: PetscFunctionBeginHot;
1645: MatCheckPreallocated(mat, 1);
1646: PetscAssertPointer(v, 3);
1647: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1648: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1649: mat->insertmode = INSERT_VALUES;
1651: if (mat->assembled) {
1652: mat->was_assembled = PETSC_TRUE;
1653: mat->assembled = PETSC_FALSE;
1654: }
1655: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1656: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1657: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1658: PetscFunctionReturn(PETSC_SUCCESS);
1659: }
1661: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1662: /*@
1663: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1664: Using structured grid indexing
1666: Not Collective
1668: Input Parameters:
1669: + mat - the matrix
1670: . m - number of rows being entered
1671: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1672: . n - number of columns being entered
1673: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1674: . v - a logically two-dimensional array of values
1675: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1677: Level: beginner
1679: Notes:
1680: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1682: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1683: options cannot be mixed without intervening calls to the assembly
1684: routines.
1686: The grid coordinates are across the entire grid, not just the local portion
1688: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1689: as well as in C.
1691: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1693: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1694: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1696: The columns and rows in the stencil passed in MUST be contained within the
1697: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1698: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1699: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1700: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1702: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1703: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1704: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1705: `DM_BOUNDARY_PERIODIC` boundary type.
1707: 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
1708: a single value per point) you can skip filling those indices.
1710: Inspired by the structured grid interface to the HYPRE package
1711: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1713: Efficiency Alert:
1714: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1715: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1717: Fortran Note:
1718: `idxm` and `idxn` should be declared as
1719: $ MatStencil idxm(4,m),idxn(4,n)
1720: and the values inserted using
1721: .vb
1722: idxm(MatStencil_i,1) = i
1723: idxm(MatStencil_j,1) = j
1724: idxm(MatStencil_k,1) = k
1725: idxm(MatStencil_c,1) = c
1726: etc
1727: .ve
1729: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1730: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1731: @*/
1732: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1733: {
1734: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1735: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1736: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1738: PetscFunctionBegin;
1739: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1742: PetscAssertPointer(idxm, 3);
1743: PetscAssertPointer(idxn, 5);
1745: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1746: jdxm = buf;
1747: jdxn = buf + m;
1748: } else {
1749: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1750: jdxm = bufm;
1751: jdxn = bufn;
1752: }
1753: for (i = 0; i < m; i++) {
1754: for (j = 0; j < 3 - sdim; j++) dxm++;
1755: tmp = *dxm++ - starts[0];
1756: for (j = 0; j < dim - 1; j++) {
1757: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1758: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1759: }
1760: if (mat->stencil.noc) dxm++;
1761: jdxm[i] = tmp;
1762: }
1763: for (i = 0; i < n; i++) {
1764: for (j = 0; j < 3 - sdim; j++) dxn++;
1765: tmp = *dxn++ - starts[0];
1766: for (j = 0; j < dim - 1; j++) {
1767: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1768: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1769: }
1770: if (mat->stencil.noc) dxn++;
1771: jdxn[i] = tmp;
1772: }
1773: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1774: PetscCall(PetscFree2(bufm, bufn));
1775: PetscFunctionReturn(PETSC_SUCCESS);
1776: }
1778: /*@
1779: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1780: Using structured grid indexing
1782: Not Collective
1784: Input Parameters:
1785: + mat - the matrix
1786: . m - number of rows being entered
1787: . idxm - grid coordinates for matrix rows being entered
1788: . n - number of columns being entered
1789: . idxn - grid coordinates for matrix columns being entered
1790: . v - a logically two-dimensional array of values
1791: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1793: Level: beginner
1795: Notes:
1796: By default the values, `v`, are row-oriented and unsorted.
1797: See `MatSetOption()` for other options.
1799: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1800: options cannot be mixed without intervening calls to the assembly
1801: routines.
1803: The grid coordinates are across the entire grid, not just the local portion
1805: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1806: as well as in C.
1808: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1810: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1811: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1813: The columns and rows in the stencil passed in MUST be contained within the
1814: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1815: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1816: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1817: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1819: Negative indices may be passed in idxm and idxn, these rows and columns are
1820: simply ignored. This allows easily inserting element stiffness matrices
1821: with homogeneous Dirichlet boundary conditions that you don't want represented
1822: in the matrix.
1824: Inspired by the structured grid interface to the HYPRE package
1825: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1827: Fortran Note:
1828: `idxm` and `idxn` should be declared as
1829: $ MatStencil idxm(4,m),idxn(4,n)
1830: and the values inserted using
1831: .vb
1832: idxm(MatStencil_i,1) = i
1833: idxm(MatStencil_j,1) = j
1834: idxm(MatStencil_k,1) = k
1835: etc
1836: .ve
1838: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1839: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1840: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1841: @*/
1842: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1843: {
1844: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1845: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1846: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1848: PetscFunctionBegin;
1849: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1852: PetscAssertPointer(idxm, 3);
1853: PetscAssertPointer(idxn, 5);
1854: PetscAssertPointer(v, 6);
1856: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1857: jdxm = buf;
1858: jdxn = buf + m;
1859: } else {
1860: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1861: jdxm = bufm;
1862: jdxn = bufn;
1863: }
1864: for (i = 0; i < m; i++) {
1865: for (j = 0; j < 3 - sdim; j++) dxm++;
1866: tmp = *dxm++ - starts[0];
1867: for (j = 0; j < sdim - 1; j++) {
1868: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1869: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1870: }
1871: dxm++;
1872: jdxm[i] = tmp;
1873: }
1874: for (i = 0; i < n; i++) {
1875: for (j = 0; j < 3 - sdim; j++) dxn++;
1876: tmp = *dxn++ - starts[0];
1877: for (j = 0; j < sdim - 1; j++) {
1878: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1879: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1880: }
1881: dxn++;
1882: jdxn[i] = tmp;
1883: }
1884: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1885: PetscCall(PetscFree2(bufm, bufn));
1886: PetscFunctionReturn(PETSC_SUCCESS);
1887: }
1889: /*@
1890: MatSetStencil - Sets the grid information for setting values into a matrix via
1891: `MatSetValuesStencil()`
1893: Not Collective
1895: Input Parameters:
1896: + mat - the matrix
1897: . dim - dimension of the grid 1, 2, or 3
1898: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1899: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1900: - dof - number of degrees of freedom per node
1902: Level: beginner
1904: Notes:
1905: Inspired by the structured grid interface to the HYPRE package
1906: (www.llnl.gov/CASC/hyper)
1908: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1909: user.
1911: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1912: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1913: @*/
1914: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1915: {
1916: PetscFunctionBegin;
1918: PetscAssertPointer(dims, 3);
1919: PetscAssertPointer(starts, 4);
1921: mat->stencil.dim = dim + (dof > 1);
1922: for (PetscInt i = 0; i < dim; i++) {
1923: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1924: mat->stencil.starts[i] = starts[dim - i - 1];
1925: }
1926: mat->stencil.dims[dim] = dof;
1927: mat->stencil.starts[dim] = 0;
1928: mat->stencil.noc = (PetscBool)(dof == 1);
1929: PetscFunctionReturn(PETSC_SUCCESS);
1930: }
1932: /*@C
1933: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
1935: Not Collective
1937: Input Parameters:
1938: + mat - the matrix
1939: . v - a logically two-dimensional array of values
1940: . m - the number of block rows
1941: . idxm - the global block indices
1942: . n - the number of block columns
1943: . idxn - the global block indices
1944: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
1946: Level: intermediate
1948: Notes:
1949: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
1950: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
1952: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
1953: NOT the total number of rows/columns; for example, if the block size is 2 and
1954: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
1955: The values in `idxm` would be 1 2; that is the first index for each block divided by
1956: the block size.
1958: You must call `MatSetBlockSize()` when constructing this matrix (before
1959: preallocating it).
1961: By default the values, `v`, are row-oriented, so the layout of
1962: `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.
1964: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
1965: options cannot be mixed without intervening calls to the assembly
1966: routines.
1968: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
1969: as well as in C.
1971: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1972: simply ignored. This allows easily inserting element stiffness matrices
1973: with homogeneous Dirichlet boundary conditions that you don't want represented
1974: in the matrix.
1976: Each time an entry is set within a sparse matrix via `MatSetValues()`,
1977: internal searching must be done to determine where to place the
1978: data in the matrix storage space. By instead inserting blocks of
1979: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
1980: reduced.
1982: Example:
1983: .vb
1984: Suppose m=n=2 and block size(bs) = 2 The array is
1986: 1 2 | 3 4
1987: 5 6 | 7 8
1988: - - - | - - -
1989: 9 10 | 11 12
1990: 13 14 | 15 16
1992: v[] should be passed in like
1993: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
1995: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1996: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
1997: .ve
1999: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2000: @*/
2001: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2002: {
2003: PetscFunctionBeginHot;
2006: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2007: PetscAssertPointer(idxm, 3);
2008: PetscAssertPointer(idxn, 5);
2009: MatCheckPreallocated(mat, 1);
2010: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2011: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2012: if (PetscDefined(USE_DEBUG)) {
2013: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2014: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2015: }
2016: if (PetscDefined(USE_DEBUG)) {
2017: PetscInt rbs, cbs, M, N, i;
2018: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2019: PetscCall(MatGetSize(mat, &M, &N));
2020: for (i = 0; i < m; i++) PetscCheck(idxm[i] * rbs < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row block index %" PetscInt_FMT " (index %" PetscInt_FMT ") greater than row length %" PetscInt_FMT, i, idxm[i], M);
2021: for (i = 0; i < n; i++) PetscCheck(idxn[i] * cbs < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column block index %" PetscInt_FMT " (index %" PetscInt_FMT ") great than column length %" PetscInt_FMT, i, idxn[i], N);
2022: }
2023: if (mat->assembled) {
2024: mat->was_assembled = PETSC_TRUE;
2025: mat->assembled = PETSC_FALSE;
2026: }
2027: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2028: if (mat->ops->setvaluesblocked) {
2029: PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2030: } else {
2031: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2032: PetscInt i, j, bs, cbs;
2034: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2035: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2036: iidxm = buf;
2037: iidxn = buf + m * bs;
2038: } else {
2039: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2040: iidxm = bufr;
2041: iidxn = bufc;
2042: }
2043: for (i = 0; i < m; i++) {
2044: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2045: }
2046: if (m != n || bs != cbs || idxm != idxn) {
2047: for (i = 0; i < n; i++) {
2048: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2049: }
2050: } else iidxn = iidxm;
2051: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2052: PetscCall(PetscFree2(bufr, bufc));
2053: }
2054: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2055: PetscFunctionReturn(PETSC_SUCCESS);
2056: }
2058: /*@C
2059: MatGetValues - Gets a block of local values from a matrix.
2061: Not Collective; can only return values that are owned by the give process
2063: Input Parameters:
2064: + mat - the matrix
2065: . v - a logically two-dimensional array for storing the values
2066: . m - the number of rows
2067: . idxm - the global indices of the rows
2068: . n - the number of columns
2069: - idxn - the global indices of the columns
2071: Level: advanced
2073: Notes:
2074: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2075: The values, `v`, are then returned in a row-oriented format,
2076: analogous to that used by default in `MatSetValues()`.
2078: `MatGetValues()` uses 0-based row and column numbers in
2079: Fortran as well as in C.
2081: `MatGetValues()` requires that the matrix has been assembled
2082: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2083: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2084: without intermediate matrix assembly.
2086: Negative row or column indices will be ignored and those locations in `v` will be
2087: left unchanged.
2089: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2090: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2091: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2093: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2094: @*/
2095: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2096: {
2097: PetscFunctionBegin;
2100: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2101: PetscAssertPointer(idxm, 3);
2102: PetscAssertPointer(idxn, 5);
2103: PetscAssertPointer(v, 6);
2104: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2105: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2106: MatCheckPreallocated(mat, 1);
2108: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2109: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2110: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2111: PetscFunctionReturn(PETSC_SUCCESS);
2112: }
2114: /*@C
2115: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2116: defined previously by `MatSetLocalToGlobalMapping()`
2118: Not Collective
2120: Input Parameters:
2121: + mat - the matrix
2122: . nrow - number of rows
2123: . irow - the row local indices
2124: . ncol - number of columns
2125: - icol - the column local indices
2127: Output Parameter:
2128: . y - a logically two-dimensional array of values
2130: Level: advanced
2132: Notes:
2133: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2135: 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,
2136: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2137: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2138: with `MatSetLocalToGlobalMapping()`.
2140: Developer Note:
2141: This is labelled with C so does not automatically generate Fortran stubs and interfaces
2142: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2144: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2145: `MatSetValuesLocal()`, `MatGetValues()`
2146: @*/
2147: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2148: {
2149: PetscFunctionBeginHot;
2152: MatCheckPreallocated(mat, 1);
2153: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2154: PetscAssertPointer(irow, 3);
2155: PetscAssertPointer(icol, 5);
2156: if (PetscDefined(USE_DEBUG)) {
2157: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2158: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2159: }
2160: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2161: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2162: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2163: else {
2164: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2165: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2166: irowm = buf;
2167: icolm = buf + nrow;
2168: } else {
2169: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2170: irowm = bufr;
2171: icolm = bufc;
2172: }
2173: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2174: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2175: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2176: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2177: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2178: PetscCall(PetscFree2(bufr, bufc));
2179: }
2180: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2181: PetscFunctionReturn(PETSC_SUCCESS);
2182: }
2184: /*@
2185: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2186: the same size. Currently, this can only be called once and creates the given matrix.
2188: Not Collective
2190: Input Parameters:
2191: + mat - the matrix
2192: . nb - the number of blocks
2193: . bs - the number of rows (and columns) in each block
2194: . rows - a concatenation of the rows for each block
2195: - v - a concatenation of logically two-dimensional arrays of values
2197: Level: advanced
2199: Notes:
2200: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2202: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2204: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2205: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2206: @*/
2207: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2208: {
2209: PetscFunctionBegin;
2212: PetscAssertPointer(rows, 4);
2213: PetscAssertPointer(v, 5);
2214: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2216: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2217: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2218: else {
2219: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2220: }
2221: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2222: PetscFunctionReturn(PETSC_SUCCESS);
2223: }
2225: /*@
2226: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2227: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2228: using a local (per-processor) numbering.
2230: Not Collective
2232: Input Parameters:
2233: + x - the matrix
2234: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2235: - cmapping - column mapping
2237: Level: intermediate
2239: Note:
2240: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2242: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2243: @*/
2244: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2245: {
2246: PetscFunctionBegin;
2251: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2252: else {
2253: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2254: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2255: }
2256: PetscFunctionReturn(PETSC_SUCCESS);
2257: }
2259: /*@
2260: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2262: Not Collective
2264: Input Parameter:
2265: . A - the matrix
2267: Output Parameters:
2268: + rmapping - row mapping
2269: - cmapping - column mapping
2271: Level: advanced
2273: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2274: @*/
2275: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2276: {
2277: PetscFunctionBegin;
2280: if (rmapping) {
2281: PetscAssertPointer(rmapping, 2);
2282: *rmapping = A->rmap->mapping;
2283: }
2284: if (cmapping) {
2285: PetscAssertPointer(cmapping, 3);
2286: *cmapping = A->cmap->mapping;
2287: }
2288: PetscFunctionReturn(PETSC_SUCCESS);
2289: }
2291: /*@
2292: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2294: Logically Collective
2296: Input Parameters:
2297: + A - the matrix
2298: . rmap - row layout
2299: - cmap - column layout
2301: Level: advanced
2303: Note:
2304: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2306: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2307: @*/
2308: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2309: {
2310: PetscFunctionBegin;
2312: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2313: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2314: PetscFunctionReturn(PETSC_SUCCESS);
2315: }
2317: /*@
2318: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2320: Not Collective
2322: Input Parameter:
2323: . A - the matrix
2325: Output Parameters:
2326: + rmap - row layout
2327: - cmap - column layout
2329: Level: advanced
2331: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2332: @*/
2333: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2334: {
2335: PetscFunctionBegin;
2338: if (rmap) {
2339: PetscAssertPointer(rmap, 2);
2340: *rmap = A->rmap;
2341: }
2342: if (cmap) {
2343: PetscAssertPointer(cmap, 3);
2344: *cmap = A->cmap;
2345: }
2346: PetscFunctionReturn(PETSC_SUCCESS);
2347: }
2349: /*@C
2350: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2351: using a local numbering of the rows and columns.
2353: Not Collective
2355: Input Parameters:
2356: + mat - the matrix
2357: . nrow - number of rows
2358: . irow - the row local indices
2359: . ncol - number of columns
2360: . icol - the column local indices
2361: . y - a logically two-dimensional array of values
2362: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2364: Level: intermediate
2366: Notes:
2367: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2369: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2370: options cannot be mixed without intervening calls to the assembly
2371: routines.
2373: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2374: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2376: Developer Note:
2377: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2378: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2380: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2381: `MatGetValuesLocal()`
2382: @*/
2383: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2384: {
2385: PetscFunctionBeginHot;
2388: MatCheckPreallocated(mat, 1);
2389: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2390: PetscAssertPointer(irow, 3);
2391: PetscAssertPointer(icol, 5);
2392: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2393: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2394: if (PetscDefined(USE_DEBUG)) {
2395: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2396: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2397: }
2399: if (mat->assembled) {
2400: mat->was_assembled = PETSC_TRUE;
2401: mat->assembled = PETSC_FALSE;
2402: }
2403: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2404: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2405: else {
2406: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2407: const PetscInt *irowm, *icolm;
2409: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2410: bufr = buf;
2411: bufc = buf + nrow;
2412: irowm = bufr;
2413: icolm = bufc;
2414: } else {
2415: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2416: irowm = bufr;
2417: icolm = bufc;
2418: }
2419: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2420: else irowm = irow;
2421: if (mat->cmap->mapping) {
2422: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2423: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2424: } else icolm = irowm;
2425: } else icolm = icol;
2426: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2427: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2428: }
2429: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2430: PetscFunctionReturn(PETSC_SUCCESS);
2431: }
2433: /*@C
2434: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2435: using a local ordering of the nodes a block at a time.
2437: Not Collective
2439: Input Parameters:
2440: + mat - the matrix
2441: . nrow - number of rows
2442: . irow - the row local indices
2443: . ncol - number of columns
2444: . icol - the column local indices
2445: . y - a logically two-dimensional array of values
2446: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2448: Level: intermediate
2450: Notes:
2451: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2452: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2454: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2455: options cannot be mixed without intervening calls to the assembly
2456: routines.
2458: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2459: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2461: Developer Note:
2462: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2463: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2465: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2466: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2467: @*/
2468: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2469: {
2470: PetscFunctionBeginHot;
2473: MatCheckPreallocated(mat, 1);
2474: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2475: PetscAssertPointer(irow, 3);
2476: PetscAssertPointer(icol, 5);
2477: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2478: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2479: if (PetscDefined(USE_DEBUG)) {
2480: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2481: 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);
2482: }
2484: if (mat->assembled) {
2485: mat->was_assembled = PETSC_TRUE;
2486: mat->assembled = PETSC_FALSE;
2487: }
2488: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2489: PetscInt irbs, rbs;
2490: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2491: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2492: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2493: }
2494: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2495: PetscInt icbs, cbs;
2496: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2497: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2498: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2499: }
2500: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2501: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2502: else {
2503: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2504: const PetscInt *irowm, *icolm;
2506: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2507: bufr = buf;
2508: bufc = buf + nrow;
2509: irowm = bufr;
2510: icolm = bufc;
2511: } else {
2512: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2513: irowm = bufr;
2514: icolm = bufc;
2515: }
2516: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2517: else irowm = irow;
2518: if (mat->cmap->mapping) {
2519: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2520: PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2521: } else icolm = irowm;
2522: } else icolm = icol;
2523: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2524: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2525: }
2526: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2527: PetscFunctionReturn(PETSC_SUCCESS);
2528: }
2530: /*@
2531: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2533: Collective
2535: Input Parameters:
2536: + mat - the matrix
2537: - x - the vector to be multiplied
2539: Output Parameter:
2540: . y - the result
2542: Level: developer
2544: Note:
2545: The vectors `x` and `y` cannot be the same. I.e., one cannot
2546: call `MatMultDiagonalBlock`(A,y,y).
2548: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2549: @*/
2550: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2551: {
2552: PetscFunctionBegin;
2558: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2559: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2560: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2561: MatCheckPreallocated(mat, 1);
2563: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2564: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2565: PetscFunctionReturn(PETSC_SUCCESS);
2566: }
2568: /*@
2569: MatMult - Computes the matrix-vector product, $y = Ax$.
2571: Neighbor-wise Collective
2573: Input Parameters:
2574: + mat - the matrix
2575: - x - the vector to be multiplied
2577: Output Parameter:
2578: . y - the result
2580: Level: beginner
2582: Note:
2583: The vectors `x` and `y` cannot be the same. I.e., one cannot
2584: call `MatMult`(A,y,y).
2586: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2587: @*/
2588: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2589: {
2590: PetscFunctionBegin;
2594: VecCheckAssembled(x);
2596: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2597: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2598: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2599: 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);
2600: 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);
2601: 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);
2602: 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);
2603: PetscCall(VecSetErrorIfLocked(y, 3));
2604: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2605: MatCheckPreallocated(mat, 1);
2607: PetscCall(VecLockReadPush(x));
2608: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2609: PetscUseTypeMethod(mat, mult, x, y);
2610: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2611: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2612: PetscCall(VecLockReadPop(x));
2613: PetscFunctionReturn(PETSC_SUCCESS);
2614: }
2616: /*@
2617: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2619: Neighbor-wise Collective
2621: Input Parameters:
2622: + mat - the matrix
2623: - x - the vector to be multiplied
2625: Output Parameter:
2626: . y - the result
2628: Level: beginner
2630: Notes:
2631: The vectors `x` and `y` cannot be the same. I.e., one cannot
2632: call `MatMultTranspose`(A,y,y).
2634: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2635: use `MatMultHermitianTranspose()`
2637: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2638: @*/
2639: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2640: {
2641: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2643: PetscFunctionBegin;
2647: VecCheckAssembled(x);
2650: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2651: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2652: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2653: 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);
2654: 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);
2655: 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);
2656: 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);
2657: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2658: MatCheckPreallocated(mat, 1);
2660: if (!mat->ops->multtranspose) {
2661: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2662: 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);
2663: } else op = mat->ops->multtranspose;
2664: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2665: PetscCall(VecLockReadPush(x));
2666: PetscCall((*op)(mat, x, y));
2667: PetscCall(VecLockReadPop(x));
2668: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2669: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2670: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2671: PetscFunctionReturn(PETSC_SUCCESS);
2672: }
2674: /*@
2675: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2677: Neighbor-wise Collective
2679: Input Parameters:
2680: + mat - the matrix
2681: - x - the vector to be multiplied
2683: Output Parameter:
2684: . y - the result
2686: Level: beginner
2688: Notes:
2689: The vectors `x` and `y` cannot be the same. I.e., one cannot
2690: call `MatMultHermitianTranspose`(A,y,y).
2692: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2694: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2696: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2697: @*/
2698: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2699: {
2700: PetscFunctionBegin;
2706: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2707: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2708: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2709: 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);
2710: 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);
2711: 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);
2712: 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);
2713: MatCheckPreallocated(mat, 1);
2715: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2716: #if defined(PETSC_USE_COMPLEX)
2717: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2718: PetscCall(VecLockReadPush(x));
2719: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2720: else PetscUseTypeMethod(mat, mult, x, y);
2721: PetscCall(VecLockReadPop(x));
2722: } else {
2723: Vec w;
2724: PetscCall(VecDuplicate(x, &w));
2725: PetscCall(VecCopy(x, w));
2726: PetscCall(VecConjugate(w));
2727: PetscCall(MatMultTranspose(mat, w, y));
2728: PetscCall(VecDestroy(&w));
2729: PetscCall(VecConjugate(y));
2730: }
2731: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2732: #else
2733: PetscCall(MatMultTranspose(mat, x, y));
2734: #endif
2735: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2736: PetscFunctionReturn(PETSC_SUCCESS);
2737: }
2739: /*@
2740: MatMultAdd - Computes $v3 = v2 + A * v1$.
2742: Neighbor-wise Collective
2744: Input Parameters:
2745: + mat - the matrix
2746: . v1 - the vector to be multiplied by `mat`
2747: - v2 - the vector to be added to the result
2749: Output Parameter:
2750: . v3 - the result
2752: Level: beginner
2754: Note:
2755: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2756: call `MatMultAdd`(A,v1,v2,v1).
2758: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2759: @*/
2760: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2761: {
2762: PetscFunctionBegin;
2769: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2770: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2771: 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);
2772: /* 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);
2773: 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); */
2774: 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);
2775: 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);
2776: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2777: MatCheckPreallocated(mat, 1);
2779: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2780: PetscCall(VecLockReadPush(v1));
2781: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2782: PetscCall(VecLockReadPop(v1));
2783: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2784: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2785: PetscFunctionReturn(PETSC_SUCCESS);
2786: }
2788: /*@
2789: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2791: Neighbor-wise Collective
2793: Input Parameters:
2794: + mat - the matrix
2795: . v1 - the vector to be multiplied by the transpose of the matrix
2796: - v2 - the vector to be added to the result
2798: Output Parameter:
2799: . v3 - the result
2801: Level: beginner
2803: Note:
2804: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2805: call `MatMultTransposeAdd`(A,v1,v2,v1).
2807: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2808: @*/
2809: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2810: {
2811: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2813: PetscFunctionBegin;
2820: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2821: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2822: 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);
2823: 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);
2824: 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);
2825: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2826: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2827: MatCheckPreallocated(mat, 1);
2829: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2830: PetscCall(VecLockReadPush(v1));
2831: PetscCall((*op)(mat, v1, v2, v3));
2832: PetscCall(VecLockReadPop(v1));
2833: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2834: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2835: PetscFunctionReturn(PETSC_SUCCESS);
2836: }
2838: /*@
2839: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2841: Neighbor-wise Collective
2843: Input Parameters:
2844: + mat - the matrix
2845: . v1 - the vector to be multiplied by the Hermitian transpose
2846: - v2 - the vector to be added to the result
2848: Output Parameter:
2849: . v3 - the result
2851: Level: beginner
2853: Note:
2854: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2855: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2857: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2858: @*/
2859: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2860: {
2861: PetscFunctionBegin;
2868: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2869: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2870: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2871: 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);
2872: 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);
2873: 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);
2874: MatCheckPreallocated(mat, 1);
2876: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2877: PetscCall(VecLockReadPush(v1));
2878: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2879: else {
2880: Vec w, z;
2881: PetscCall(VecDuplicate(v1, &w));
2882: PetscCall(VecCopy(v1, w));
2883: PetscCall(VecConjugate(w));
2884: PetscCall(VecDuplicate(v3, &z));
2885: PetscCall(MatMultTranspose(mat, w, z));
2886: PetscCall(VecDestroy(&w));
2887: PetscCall(VecConjugate(z));
2888: if (v2 != v3) {
2889: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2890: } else {
2891: PetscCall(VecAXPY(v3, 1.0, z));
2892: }
2893: PetscCall(VecDestroy(&z));
2894: }
2895: PetscCall(VecLockReadPop(v1));
2896: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2897: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2898: PetscFunctionReturn(PETSC_SUCCESS);
2899: }
2901: /*@C
2902: MatGetFactorType - gets the type of factorization a matrix is
2904: Not Collective
2906: Input Parameter:
2907: . mat - the matrix
2909: Output Parameter:
2910: . 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`
2912: Level: intermediate
2914: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2915: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2916: @*/
2917: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2918: {
2919: PetscFunctionBegin;
2922: PetscAssertPointer(t, 2);
2923: *t = mat->factortype;
2924: PetscFunctionReturn(PETSC_SUCCESS);
2925: }
2927: /*@C
2928: MatSetFactorType - sets the type of factorization a matrix is
2930: Logically Collective
2932: Input Parameters:
2933: + mat - the matrix
2934: - 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`
2936: Level: intermediate
2938: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2939: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2940: @*/
2941: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
2942: {
2943: PetscFunctionBegin;
2946: mat->factortype = t;
2947: PetscFunctionReturn(PETSC_SUCCESS);
2948: }
2950: /*@C
2951: MatGetInfo - Returns information about matrix storage (number of
2952: nonzeros, memory, etc.).
2954: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
2956: Input Parameters:
2957: + mat - the matrix
2958: - 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)
2960: Output Parameter:
2961: . info - matrix information context
2963: Options Database Key:
2964: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
2966: Notes:
2967: The `MatInfo` context contains a variety of matrix data, including
2968: number of nonzeros allocated and used, number of mallocs during
2969: matrix assembly, etc. Additional information for factored matrices
2970: is provided (such as the fill ratio, number of mallocs during
2971: factorization, etc.).
2973: Example:
2974: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2975: data within the MatInfo context. For example,
2976: .vb
2977: MatInfo info;
2978: Mat A;
2979: double mal, nz_a, nz_u;
2981: MatGetInfo(A, MAT_LOCAL, &info);
2982: mal = info.mallocs;
2983: nz_a = info.nz_allocated;
2984: .ve
2986: Fortran users should declare info as a double precision
2987: array of dimension `MAT_INFO_SIZE`, and then extract the parameters
2988: of interest. See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2989: a complete list of parameter names.
2990: .vb
2991: double precision info(MAT_INFO_SIZE)
2992: double precision mal, nz_a
2993: Mat A
2994: integer ierr
2996: call MatGetInfo(A, MAT_LOCAL, info, ierr)
2997: mal = info(MAT_INFO_MALLOCS)
2998: nz_a = info(MAT_INFO_NZ_ALLOCATED)
2999: .ve
3001: Level: intermediate
3003: Developer Note:
3004: The Fortran interface is not autogenerated as the
3005: interface definition cannot be generated correctly [due to `MatInfo` argument]
3007: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3008: @*/
3009: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3010: {
3011: PetscFunctionBegin;
3014: PetscAssertPointer(info, 3);
3015: MatCheckPreallocated(mat, 1);
3016: PetscUseTypeMethod(mat, getinfo, flag, info);
3017: PetscFunctionReturn(PETSC_SUCCESS);
3018: }
3020: /*
3021: This is used by external packages where it is not easy to get the info from the actual
3022: matrix factorization.
3023: */
3024: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3025: {
3026: PetscFunctionBegin;
3027: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3028: PetscFunctionReturn(PETSC_SUCCESS);
3029: }
3031: /*@C
3032: MatLUFactor - Performs in-place LU factorization of matrix.
3034: Collective
3036: Input Parameters:
3037: + mat - the matrix
3038: . row - row permutation
3039: . col - column permutation
3040: - info - options for factorization, includes
3041: .vb
3042: fill - expected fill as ratio of original fill.
3043: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3044: Run with the option -info to determine an optimal value to use
3045: .ve
3047: Level: developer
3049: Notes:
3050: Most users should employ the `KSP` interface for linear solvers
3051: instead of working directly with matrix algebra routines such as this.
3052: See, e.g., `KSPCreate()`.
3054: This changes the state of the matrix to a factored matrix; it cannot be used
3055: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3057: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3058: when not using `KSP`.
3060: Developer Note:
3061: The Fortran interface is not autogenerated as the
3062: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3064: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3065: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3066: @*/
3067: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3068: {
3069: MatFactorInfo tinfo;
3071: PetscFunctionBegin;
3075: if (info) PetscAssertPointer(info, 4);
3077: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3078: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3079: MatCheckPreallocated(mat, 1);
3080: if (!info) {
3081: PetscCall(MatFactorInfoInitialize(&tinfo));
3082: info = &tinfo;
3083: }
3085: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3086: PetscUseTypeMethod(mat, lufactor, row, col, info);
3087: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3088: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3089: PetscFunctionReturn(PETSC_SUCCESS);
3090: }
3092: /*@C
3093: MatILUFactor - Performs in-place ILU factorization of matrix.
3095: Collective
3097: Input Parameters:
3098: + mat - the matrix
3099: . row - row permutation
3100: . col - column permutation
3101: - info - structure containing
3102: .vb
3103: levels - number of levels of fill.
3104: expected fill - as ratio of original fill.
3105: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3106: missing diagonal entries)
3107: .ve
3109: Level: developer
3111: Notes:
3112: Most users should employ the `KSP` interface for linear solvers
3113: instead of working directly with matrix algebra routines such as this.
3114: See, e.g., `KSPCreate()`.
3116: Probably really in-place only when level of fill is zero, otherwise allocates
3117: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3118: when not using `KSP`.
3120: Developer Note:
3121: The Fortran interface is not autogenerated as the
3122: interface definition cannot be generated correctly [due to MatFactorInfo]
3124: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3125: @*/
3126: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3127: {
3128: PetscFunctionBegin;
3132: PetscAssertPointer(info, 4);
3134: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3135: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3136: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3137: MatCheckPreallocated(mat, 1);
3139: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3140: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3141: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3142: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3143: PetscFunctionReturn(PETSC_SUCCESS);
3144: }
3146: /*@C
3147: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3148: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3150: Collective
3152: Input Parameters:
3153: + fact - the factor matrix obtained with `MatGetFactor()`
3154: . mat - the matrix
3155: . row - the row permutation
3156: . col - the column permutation
3157: - info - options for factorization, includes
3158: .vb
3159: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3160: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3161: .ve
3163: Level: developer
3165: Notes:
3166: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3168: Most users should employ the simplified `KSP` interface for linear solvers
3169: instead of working directly with matrix algebra routines such as this.
3170: See, e.g., `KSPCreate()`.
3172: Developer Note:
3173: The Fortran interface is not autogenerated as the
3174: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3176: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3177: @*/
3178: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3179: {
3180: MatFactorInfo tinfo;
3182: PetscFunctionBegin;
3187: if (info) PetscAssertPointer(info, 5);
3190: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3191: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3192: MatCheckPreallocated(mat, 2);
3193: if (!info) {
3194: PetscCall(MatFactorInfoInitialize(&tinfo));
3195: info = &tinfo;
3196: }
3198: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3199: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3200: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3201: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3202: PetscFunctionReturn(PETSC_SUCCESS);
3203: }
3205: /*@C
3206: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3207: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3209: Collective
3211: Input Parameters:
3212: + fact - the factor matrix obtained with `MatGetFactor()`
3213: . mat - the matrix
3214: - info - options for factorization
3216: Level: developer
3218: Notes:
3219: See `MatLUFactor()` for in-place factorization. See
3220: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3222: Most users should employ the `KSP` interface for linear solvers
3223: instead of working directly with matrix algebra routines such as this.
3224: See, e.g., `KSPCreate()`.
3226: Developer Note:
3227: The Fortran interface is not autogenerated as the
3228: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3230: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3231: @*/
3232: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3233: {
3234: MatFactorInfo tinfo;
3236: PetscFunctionBegin;
3241: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3242: 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,
3243: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3245: MatCheckPreallocated(mat, 2);
3246: if (!info) {
3247: PetscCall(MatFactorInfoInitialize(&tinfo));
3248: info = &tinfo;
3249: }
3251: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3252: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3253: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3254: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3255: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3256: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3257: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3258: PetscFunctionReturn(PETSC_SUCCESS);
3259: }
3261: /*@C
3262: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3263: symmetric matrix.
3265: Collective
3267: Input Parameters:
3268: + mat - the matrix
3269: . perm - row and column permutations
3270: - info - expected fill as ratio of original fill
3272: Level: developer
3274: Notes:
3275: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3276: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3278: Most users should employ the `KSP` interface for linear solvers
3279: instead of working directly with matrix algebra routines such as this.
3280: See, e.g., `KSPCreate()`.
3282: Developer Note:
3283: The Fortran interface is not autogenerated as the
3284: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3286: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3287: `MatGetOrdering()`
3288: @*/
3289: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3290: {
3291: MatFactorInfo tinfo;
3293: PetscFunctionBegin;
3296: if (info) PetscAssertPointer(info, 3);
3298: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3299: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3300: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3301: MatCheckPreallocated(mat, 1);
3302: if (!info) {
3303: PetscCall(MatFactorInfoInitialize(&tinfo));
3304: info = &tinfo;
3305: }
3307: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3308: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3309: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3310: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3311: PetscFunctionReturn(PETSC_SUCCESS);
3312: }
3314: /*@C
3315: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3316: of a symmetric matrix.
3318: Collective
3320: Input Parameters:
3321: + fact - the factor matrix obtained with `MatGetFactor()`
3322: . mat - the matrix
3323: . perm - row and column permutations
3324: - info - options for factorization, includes
3325: .vb
3326: fill - expected fill as ratio of original fill.
3327: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3328: Run with the option -info to determine an optimal value to use
3329: .ve
3331: Level: developer
3333: Notes:
3334: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3335: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3337: Most users should employ the `KSP` interface for linear solvers
3338: instead of working directly with matrix algebra routines such as this.
3339: See, e.g., `KSPCreate()`.
3341: Developer Note:
3342: The Fortran interface is not autogenerated as the
3343: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3345: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3346: `MatGetOrdering()`
3347: @*/
3348: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3349: {
3350: MatFactorInfo tinfo;
3352: PetscFunctionBegin;
3356: if (info) PetscAssertPointer(info, 4);
3359: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3360: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3361: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3362: MatCheckPreallocated(mat, 2);
3363: if (!info) {
3364: PetscCall(MatFactorInfoInitialize(&tinfo));
3365: info = &tinfo;
3366: }
3368: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3369: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3370: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3371: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3372: PetscFunctionReturn(PETSC_SUCCESS);
3373: }
3375: /*@C
3376: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3377: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3378: `MatCholeskyFactorSymbolic()`.
3380: Collective
3382: Input Parameters:
3383: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3384: . mat - the initial matrix that is to be factored
3385: - info - options for factorization
3387: Level: developer
3389: Note:
3390: Most users should employ the `KSP` interface for linear solvers
3391: instead of working directly with matrix algebra routines such as this.
3392: See, e.g., `KSPCreate()`.
3394: Developer Note:
3395: The Fortran interface is not autogenerated as the
3396: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3398: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3399: @*/
3400: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3401: {
3402: MatFactorInfo tinfo;
3404: PetscFunctionBegin;
3409: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3410: 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,
3411: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3412: MatCheckPreallocated(mat, 2);
3413: if (!info) {
3414: PetscCall(MatFactorInfoInitialize(&tinfo));
3415: info = &tinfo;
3416: }
3418: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3419: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3420: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3421: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3422: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3423: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3424: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3425: PetscFunctionReturn(PETSC_SUCCESS);
3426: }
3428: /*@
3429: MatQRFactor - Performs in-place QR factorization of matrix.
3431: Collective
3433: Input Parameters:
3434: + mat - the matrix
3435: . col - column permutation
3436: - info - options for factorization, includes
3437: .vb
3438: fill - expected fill as ratio of original fill.
3439: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3440: Run with the option -info to determine an optimal value to use
3441: .ve
3443: Level: developer
3445: Notes:
3446: Most users should employ the `KSP` interface for linear solvers
3447: instead of working directly with matrix algebra routines such as this.
3448: See, e.g., `KSPCreate()`.
3450: This changes the state of the matrix to a factored matrix; it cannot be used
3451: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3453: Developer Note:
3454: The Fortran interface is not autogenerated as the
3455: interface definition cannot be generated correctly [due to MatFactorInfo]
3457: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3458: `MatSetUnfactored()`
3459: @*/
3460: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3461: {
3462: PetscFunctionBegin;
3465: if (info) PetscAssertPointer(info, 3);
3467: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3468: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3469: MatCheckPreallocated(mat, 1);
3470: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3471: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3472: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3473: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3474: PetscFunctionReturn(PETSC_SUCCESS);
3475: }
3477: /*@
3478: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3479: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3481: Collective
3483: Input Parameters:
3484: + fact - the factor matrix obtained with `MatGetFactor()`
3485: . mat - the matrix
3486: . col - column permutation
3487: - info - options for factorization, includes
3488: .vb
3489: fill - expected fill as ratio of original fill.
3490: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3491: Run with the option -info to determine an optimal value to use
3492: .ve
3494: Level: developer
3496: Note:
3497: Most users should employ the `KSP` interface for linear solvers
3498: instead of working directly with matrix algebra routines such as this.
3499: See, e.g., `KSPCreate()`.
3501: Developer Note:
3502: The Fortran interface is not autogenerated as the
3503: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3505: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3506: @*/
3507: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3508: {
3509: MatFactorInfo tinfo;
3511: PetscFunctionBegin;
3515: if (info) PetscAssertPointer(info, 4);
3518: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3519: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3520: MatCheckPreallocated(mat, 2);
3521: if (!info) {
3522: PetscCall(MatFactorInfoInitialize(&tinfo));
3523: info = &tinfo;
3524: }
3526: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3527: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3528: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3529: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3530: PetscFunctionReturn(PETSC_SUCCESS);
3531: }
3533: /*@
3534: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3535: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3537: Collective
3539: Input Parameters:
3540: + fact - the factor matrix obtained with `MatGetFactor()`
3541: . mat - the matrix
3542: - info - options for factorization
3544: Level: developer
3546: Notes:
3547: See `MatQRFactor()` for in-place factorization.
3549: Most users should employ the `KSP` interface for linear solvers
3550: instead of working directly with matrix algebra routines such as this.
3551: See, e.g., `KSPCreate()`.
3553: Developer Note:
3554: The Fortran interface is not autogenerated as the
3555: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3557: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3558: @*/
3559: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3560: {
3561: MatFactorInfo tinfo;
3563: PetscFunctionBegin;
3568: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3569: 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,
3570: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3572: MatCheckPreallocated(mat, 2);
3573: if (!info) {
3574: PetscCall(MatFactorInfoInitialize(&tinfo));
3575: info = &tinfo;
3576: }
3578: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3579: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3580: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3581: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3582: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3583: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3584: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3585: PetscFunctionReturn(PETSC_SUCCESS);
3586: }
3588: /*@
3589: MatSolve - Solves $A x = b$, given a factored matrix.
3591: Neighbor-wise Collective
3593: Input Parameters:
3594: + mat - the factored matrix
3595: - b - the right-hand-side vector
3597: Output Parameter:
3598: . x - the result vector
3600: Level: developer
3602: Notes:
3603: The vectors `b` and `x` cannot be the same. I.e., one cannot
3604: call `MatSolve`(A,x,x).
3606: Most users should employ the `KSP` interface for linear solvers
3607: instead of working directly with matrix algebra routines such as this.
3608: See, e.g., `KSPCreate()`.
3610: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3611: @*/
3612: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3613: {
3614: PetscFunctionBegin;
3619: PetscCheckSameComm(mat, 1, b, 2);
3620: PetscCheckSameComm(mat, 1, x, 3);
3621: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3622: 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);
3623: 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);
3624: 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);
3625: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3626: MatCheckPreallocated(mat, 1);
3628: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3629: if (mat->factorerrortype) {
3630: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3631: PetscCall(VecSetInf(x));
3632: } else PetscUseTypeMethod(mat, solve, b, x);
3633: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3634: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3635: PetscFunctionReturn(PETSC_SUCCESS);
3636: }
3638: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3639: {
3640: Vec b, x;
3641: PetscInt N, i;
3642: PetscErrorCode (*f)(Mat, Vec, Vec);
3643: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3645: PetscFunctionBegin;
3646: if (A->factorerrortype) {
3647: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3648: PetscCall(MatSetInf(X));
3649: PetscFunctionReturn(PETSC_SUCCESS);
3650: }
3651: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3652: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3653: PetscCall(MatBoundToCPU(A, &Abound));
3654: if (!Abound) {
3655: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3656: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3657: }
3658: #if PetscDefined(HAVE_CUDA)
3659: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3660: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3661: #elif PetscDefined(HAVE_HIP)
3662: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3663: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3664: #endif
3665: PetscCall(MatGetSize(B, NULL, &N));
3666: for (i = 0; i < N; i++) {
3667: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3668: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3669: PetscCall((*f)(A, b, x));
3670: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3671: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3672: }
3673: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3674: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3675: PetscFunctionReturn(PETSC_SUCCESS);
3676: }
3678: /*@
3679: MatMatSolve - Solves $A X = B$, given a factored matrix.
3681: Neighbor-wise Collective
3683: Input Parameters:
3684: + A - the factored matrix
3685: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3687: Output Parameter:
3688: . X - the result matrix (dense matrix)
3690: Level: developer
3692: Note:
3693: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3694: otherwise, `B` and `X` cannot be the same.
3696: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3697: @*/
3698: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3699: {
3700: PetscFunctionBegin;
3705: PetscCheckSameComm(A, 1, B, 2);
3706: PetscCheckSameComm(A, 1, X, 3);
3707: 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);
3708: 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);
3709: 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");
3710: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3711: MatCheckPreallocated(A, 1);
3713: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3714: if (!A->ops->matsolve) {
3715: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3716: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3717: } else PetscUseTypeMethod(A, matsolve, B, X);
3718: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3719: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3720: PetscFunctionReturn(PETSC_SUCCESS);
3721: }
3723: /*@
3724: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3726: Neighbor-wise Collective
3728: Input Parameters:
3729: + A - the factored matrix
3730: - B - the right-hand-side matrix (`MATDENSE` matrix)
3732: Output Parameter:
3733: . X - the result matrix (dense matrix)
3735: Level: developer
3737: Note:
3738: The matrices `B` and `X` cannot be the same. I.e., one cannot
3739: call `MatMatSolveTranspose`(A,X,X).
3741: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3742: @*/
3743: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3744: {
3745: PetscFunctionBegin;
3750: PetscCheckSameComm(A, 1, B, 2);
3751: PetscCheckSameComm(A, 1, X, 3);
3752: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3753: 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);
3754: 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);
3755: 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);
3756: 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");
3757: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3758: MatCheckPreallocated(A, 1);
3760: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3761: if (!A->ops->matsolvetranspose) {
3762: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3763: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3764: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3765: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3766: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3767: PetscFunctionReturn(PETSC_SUCCESS);
3768: }
3770: /*@
3771: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3773: Neighbor-wise Collective
3775: Input Parameters:
3776: + A - the factored matrix
3777: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3779: Output Parameter:
3780: . X - the result matrix (dense matrix)
3782: Level: developer
3784: Note:
3785: 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
3786: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3788: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3789: @*/
3790: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3791: {
3792: PetscFunctionBegin;
3797: PetscCheckSameComm(A, 1, Bt, 2);
3798: PetscCheckSameComm(A, 1, X, 3);
3800: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3801: 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);
3802: 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);
3803: 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");
3804: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3805: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3806: MatCheckPreallocated(A, 1);
3808: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3809: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3810: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3811: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3812: PetscFunctionReturn(PETSC_SUCCESS);
3813: }
3815: /*@
3816: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3817: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3819: Neighbor-wise Collective
3821: Input Parameters:
3822: + mat - the factored matrix
3823: - b - the right-hand-side vector
3825: Output Parameter:
3826: . x - the result vector
3828: Level: developer
3830: Notes:
3831: `MatSolve()` should be used for most applications, as it performs
3832: a forward solve followed by a backward solve.
3834: The vectors `b` and `x` cannot be the same, i.e., one cannot
3835: call `MatForwardSolve`(A,x,x).
3837: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3838: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3839: `MatForwardSolve()` solves $U^T*D y = b$, and
3840: `MatBackwardSolve()` solves $U x = y$.
3841: Thus they do not provide a symmetric preconditioner.
3843: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3844: @*/
3845: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3846: {
3847: PetscFunctionBegin;
3852: PetscCheckSameComm(mat, 1, b, 2);
3853: PetscCheckSameComm(mat, 1, x, 3);
3854: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3855: 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);
3856: 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);
3857: 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);
3858: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3859: MatCheckPreallocated(mat, 1);
3861: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3862: PetscUseTypeMethod(mat, forwardsolve, b, x);
3863: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3864: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3865: PetscFunctionReturn(PETSC_SUCCESS);
3866: }
3868: /*@
3869: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3870: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3872: Neighbor-wise Collective
3874: Input Parameters:
3875: + mat - the factored matrix
3876: - b - the right-hand-side vector
3878: Output Parameter:
3879: . x - the result vector
3881: Level: developer
3883: Notes:
3884: `MatSolve()` should be used for most applications, as it performs
3885: a forward solve followed by a backward solve.
3887: The vectors `b` and `x` cannot be the same. I.e., one cannot
3888: call `MatBackwardSolve`(A,x,x).
3890: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3891: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3892: `MatForwardSolve()` solves $U^T*D y = b$, and
3893: `MatBackwardSolve()` solves $U x = y$.
3894: Thus they do not provide a symmetric preconditioner.
3896: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3897: @*/
3898: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3899: {
3900: PetscFunctionBegin;
3905: PetscCheckSameComm(mat, 1, b, 2);
3906: PetscCheckSameComm(mat, 1, x, 3);
3907: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3908: 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);
3909: 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);
3910: 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);
3911: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3912: MatCheckPreallocated(mat, 1);
3914: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3915: PetscUseTypeMethod(mat, backwardsolve, b, x);
3916: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3917: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3918: PetscFunctionReturn(PETSC_SUCCESS);
3919: }
3921: /*@
3922: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
3924: Neighbor-wise Collective
3926: Input Parameters:
3927: + mat - the factored matrix
3928: . b - the right-hand-side vector
3929: - y - the vector to be added to
3931: Output Parameter:
3932: . x - the result vector
3934: Level: developer
3936: Note:
3937: The vectors `b` and `x` cannot be the same. I.e., one cannot
3938: call `MatSolveAdd`(A,x,y,x).
3940: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3941: @*/
3942: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3943: {
3944: PetscScalar one = 1.0;
3945: Vec tmp;
3947: PetscFunctionBegin;
3953: PetscCheckSameComm(mat, 1, b, 2);
3954: PetscCheckSameComm(mat, 1, y, 3);
3955: PetscCheckSameComm(mat, 1, x, 4);
3956: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3957: 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);
3958: 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);
3959: 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);
3960: 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);
3961: 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);
3962: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3963: MatCheckPreallocated(mat, 1);
3965: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
3966: if (mat->factorerrortype) {
3967: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3968: PetscCall(VecSetInf(x));
3969: } else if (mat->ops->solveadd) {
3970: PetscUseTypeMethod(mat, solveadd, b, y, x);
3971: } else {
3972: /* do the solve then the add manually */
3973: if (x != y) {
3974: PetscCall(MatSolve(mat, b, x));
3975: PetscCall(VecAXPY(x, one, y));
3976: } else {
3977: PetscCall(VecDuplicate(x, &tmp));
3978: PetscCall(VecCopy(x, tmp));
3979: PetscCall(MatSolve(mat, b, x));
3980: PetscCall(VecAXPY(x, one, tmp));
3981: PetscCall(VecDestroy(&tmp));
3982: }
3983: }
3984: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
3985: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3986: PetscFunctionReturn(PETSC_SUCCESS);
3987: }
3989: /*@
3990: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
3992: Neighbor-wise Collective
3994: Input Parameters:
3995: + mat - the factored matrix
3996: - b - the right-hand-side vector
3998: Output Parameter:
3999: . x - the result vector
4001: Level: developer
4003: Notes:
4004: The vectors `b` and `x` cannot be the same. I.e., one cannot
4005: call `MatSolveTranspose`(A,x,x).
4007: Most users should employ the `KSP` interface for linear solvers
4008: instead of working directly with matrix algebra routines such as this.
4009: See, e.g., `KSPCreate()`.
4011: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4012: @*/
4013: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4014: {
4015: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4017: PetscFunctionBegin;
4022: PetscCheckSameComm(mat, 1, b, 2);
4023: PetscCheckSameComm(mat, 1, x, 3);
4024: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4025: 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);
4026: 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);
4027: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4028: MatCheckPreallocated(mat, 1);
4029: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4030: if (mat->factorerrortype) {
4031: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4032: PetscCall(VecSetInf(x));
4033: } else {
4034: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4035: PetscCall((*f)(mat, b, x));
4036: }
4037: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4038: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4039: PetscFunctionReturn(PETSC_SUCCESS);
4040: }
4042: /*@
4043: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4044: factored matrix.
4046: Neighbor-wise Collective
4048: Input Parameters:
4049: + mat - the factored matrix
4050: . b - the right-hand-side vector
4051: - y - the vector to be added to
4053: Output Parameter:
4054: . x - the result vector
4056: Level: developer
4058: Note:
4059: The vectors `b` and `x` cannot be the same. I.e., one cannot
4060: call `MatSolveTransposeAdd`(A,x,y,x).
4062: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4063: @*/
4064: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4065: {
4066: PetscScalar one = 1.0;
4067: Vec tmp;
4068: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4070: PetscFunctionBegin;
4076: PetscCheckSameComm(mat, 1, b, 2);
4077: PetscCheckSameComm(mat, 1, y, 3);
4078: PetscCheckSameComm(mat, 1, x, 4);
4079: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4080: 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);
4081: 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);
4082: 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);
4083: 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);
4084: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4085: MatCheckPreallocated(mat, 1);
4087: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4088: if (mat->factorerrortype) {
4089: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4090: PetscCall(VecSetInf(x));
4091: } else if (f) {
4092: PetscCall((*f)(mat, b, y, x));
4093: } else {
4094: /* do the solve then the add manually */
4095: if (x != y) {
4096: PetscCall(MatSolveTranspose(mat, b, x));
4097: PetscCall(VecAXPY(x, one, y));
4098: } else {
4099: PetscCall(VecDuplicate(x, &tmp));
4100: PetscCall(VecCopy(x, tmp));
4101: PetscCall(MatSolveTranspose(mat, b, x));
4102: PetscCall(VecAXPY(x, one, tmp));
4103: PetscCall(VecDestroy(&tmp));
4104: }
4105: }
4106: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4107: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4108: PetscFunctionReturn(PETSC_SUCCESS);
4109: }
4111: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4112: /*@
4113: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4115: Neighbor-wise Collective
4117: Input Parameters:
4118: + mat - the matrix
4119: . b - the right hand side
4120: . omega - the relaxation factor
4121: . flag - flag indicating the type of SOR (see below)
4122: . shift - diagonal shift
4123: . its - the number of iterations
4124: - lits - the number of local iterations
4126: Output Parameter:
4127: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4129: SOR Flags:
4130: + `SOR_FORWARD_SWEEP` - forward SOR
4131: . `SOR_BACKWARD_SWEEP` - backward SOR
4132: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4133: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4134: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4135: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4136: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4137: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4138: upper/lower triangular part of matrix to
4139: vector (with omega)
4140: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4142: Level: developer
4144: Notes:
4145: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4146: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4147: on each processor.
4149: Application programmers will not generally use `MatSOR()` directly,
4150: but instead will employ the `KSP`/`PC` interface.
4152: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing
4154: Most users should employ the `KSP` interface for linear solvers
4155: instead of working directly with matrix algebra routines such as this.
4156: See, e.g., `KSPCreate()`.
4158: Vectors `x` and `b` CANNOT be the same
4160: The flags are implemented as bitwise inclusive or operations.
4161: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4162: to specify a zero initial guess for SSOR.
4164: Developer Note:
4165: We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes
4167: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4168: @*/
4169: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4170: {
4171: PetscFunctionBegin;
4176: PetscCheckSameComm(mat, 1, b, 2);
4177: PetscCheckSameComm(mat, 1, x, 8);
4178: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4179: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4180: 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);
4181: 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);
4182: 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);
4183: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4184: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4185: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4187: MatCheckPreallocated(mat, 1);
4188: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4189: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4190: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4191: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4192: PetscFunctionReturn(PETSC_SUCCESS);
4193: }
4195: /*
4196: Default matrix copy routine.
4197: */
4198: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4199: {
4200: PetscInt i, rstart = 0, rend = 0, nz;
4201: const PetscInt *cwork;
4202: const PetscScalar *vwork;
4204: PetscFunctionBegin;
4205: if (B->assembled) PetscCall(MatZeroEntries(B));
4206: if (str == SAME_NONZERO_PATTERN) {
4207: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4208: for (i = rstart; i < rend; i++) {
4209: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4210: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4211: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4212: }
4213: } else {
4214: PetscCall(MatAYPX(B, 0.0, A, str));
4215: }
4216: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4217: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4218: PetscFunctionReturn(PETSC_SUCCESS);
4219: }
4221: /*@
4222: MatCopy - Copies a matrix to another matrix.
4224: Collective
4226: Input Parameters:
4227: + A - the matrix
4228: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4230: Output Parameter:
4231: . B - where the copy is put
4233: Level: intermediate
4235: Notes:
4236: If you use `SAME_NONZERO_PATTERN` then the two matrices must have the same nonzero pattern or the routine will crash.
4238: `MatCopy()` copies the matrix entries of a matrix to another existing
4239: matrix (after first zeroing the second matrix). A related routine is
4240: `MatConvert()`, which first creates a new matrix and then copies the data.
4242: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4243: @*/
4244: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4245: {
4246: PetscInt i;
4248: PetscFunctionBegin;
4253: PetscCheckSameComm(A, 1, B, 2);
4254: MatCheckPreallocated(B, 2);
4255: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4256: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4257: 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,
4258: A->cmap->N, B->cmap->N);
4259: MatCheckPreallocated(A, 1);
4260: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4262: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4263: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4264: else PetscCall(MatCopy_Basic(A, B, str));
4266: B->stencil.dim = A->stencil.dim;
4267: B->stencil.noc = A->stencil.noc;
4268: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4269: B->stencil.dims[i] = A->stencil.dims[i];
4270: B->stencil.starts[i] = A->stencil.starts[i];
4271: }
4273: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4274: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4275: PetscFunctionReturn(PETSC_SUCCESS);
4276: }
4278: /*@C
4279: MatConvert - Converts a matrix to another matrix, either of the same
4280: or different type.
4282: Collective
4284: Input Parameters:
4285: + mat - the matrix
4286: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4287: same type as the original matrix.
4288: - reuse - denotes if the destination matrix is to be created or reused.
4289: 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
4290: `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).
4292: Output Parameter:
4293: . M - pointer to place new matrix
4295: Level: intermediate
4297: Notes:
4298: `MatConvert()` first creates a new matrix and then copies the data from
4299: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4300: entries of one matrix to another already existing matrix context.
4302: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4303: the MPI communicator of the generated matrix is always the same as the communicator
4304: of the input matrix.
4306: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4307: @*/
4308: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4309: {
4310: PetscBool sametype, issame, flg;
4311: PetscBool3 issymmetric, ishermitian;
4312: char convname[256], mtype[256];
4313: Mat B;
4315: PetscFunctionBegin;
4318: PetscAssertPointer(M, 4);
4319: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4320: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4321: MatCheckPreallocated(mat, 1);
4323: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4324: if (flg) newtype = mtype;
4326: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4327: PetscCall(PetscStrcmp(newtype, "same", &issame));
4328: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4329: if (reuse == MAT_REUSE_MATRIX) {
4331: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4332: }
4334: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4335: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4336: PetscFunctionReturn(PETSC_SUCCESS);
4337: }
4339: /* Cache Mat options because some converters use MatHeaderReplace */
4340: issymmetric = mat->symmetric;
4341: ishermitian = mat->hermitian;
4343: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4344: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4345: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4346: } else {
4347: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4348: const char *prefix[3] = {"seq", "mpi", ""};
4349: PetscInt i;
4350: /*
4351: Order of precedence:
4352: 0) See if newtype is a superclass of the current matrix.
4353: 1) See if a specialized converter is known to the current matrix.
4354: 2) See if a specialized converter is known to the desired matrix class.
4355: 3) See if a good general converter is registered for the desired class
4356: (as of 6/27/03 only MATMPIADJ falls into this category).
4357: 4) See if a good general converter is known for the current matrix.
4358: 5) Use a really basic converter.
4359: */
4361: /* 0) See if newtype is a superclass of the current matrix.
4362: i.e mat is mpiaij and newtype is aij */
4363: for (i = 0; i < 2; i++) {
4364: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4365: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4366: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4367: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4368: if (flg) {
4369: if (reuse == MAT_INPLACE_MATRIX) {
4370: PetscCall(PetscInfo(mat, "Early return\n"));
4371: PetscFunctionReturn(PETSC_SUCCESS);
4372: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4373: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4374: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4375: PetscFunctionReturn(PETSC_SUCCESS);
4376: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4377: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4378: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4379: PetscFunctionReturn(PETSC_SUCCESS);
4380: }
4381: }
4382: }
4383: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4384: for (i = 0; i < 3; i++) {
4385: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4386: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4387: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4388: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4389: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4390: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4391: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4392: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4393: if (conv) goto foundconv;
4394: }
4396: /* 2) See if a specialized converter is known to the desired matrix class. */
4397: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4398: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4399: PetscCall(MatSetType(B, newtype));
4400: for (i = 0; i < 3; i++) {
4401: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4402: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4403: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4404: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4405: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4406: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4407: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4408: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4409: if (conv) {
4410: PetscCall(MatDestroy(&B));
4411: goto foundconv;
4412: }
4413: }
4415: /* 3) See if a good general converter is registered for the desired class */
4416: conv = B->ops->convertfrom;
4417: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4418: PetscCall(MatDestroy(&B));
4419: if (conv) goto foundconv;
4421: /* 4) See if a good general converter is known for the current matrix */
4422: if (mat->ops->convert) conv = mat->ops->convert;
4423: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4424: if (conv) goto foundconv;
4426: /* 5) Use a really basic converter. */
4427: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4428: conv = MatConvert_Basic;
4430: foundconv:
4431: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4432: PetscCall((*conv)(mat, newtype, reuse, M));
4433: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4434: /* the block sizes must be same if the mappings are copied over */
4435: (*M)->rmap->bs = mat->rmap->bs;
4436: (*M)->cmap->bs = mat->cmap->bs;
4437: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4438: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4439: (*M)->rmap->mapping = mat->rmap->mapping;
4440: (*M)->cmap->mapping = mat->cmap->mapping;
4441: }
4442: (*M)->stencil.dim = mat->stencil.dim;
4443: (*M)->stencil.noc = mat->stencil.noc;
4444: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4445: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4446: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4447: }
4448: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4449: }
4450: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4452: /* Copy Mat options */
4453: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4454: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4455: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4456: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4457: PetscFunctionReturn(PETSC_SUCCESS);
4458: }
4460: /*@C
4461: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4463: Not Collective
4465: Input Parameter:
4466: . mat - the matrix, must be a factored matrix
4468: Output Parameter:
4469: . type - the string name of the package (do not free this string)
4471: Level: intermediate
4473: Fortran Note:
4474: Pass in an empty string and the package name will be copied into it. Make sure the string is long enough.
4476: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4477: @*/
4478: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4479: {
4480: PetscErrorCode (*conv)(Mat, MatSolverType *);
4482: PetscFunctionBegin;
4485: PetscAssertPointer(type, 2);
4486: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4487: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4488: if (conv) PetscCall((*conv)(mat, type));
4489: else *type = MATSOLVERPETSC;
4490: PetscFunctionReturn(PETSC_SUCCESS);
4491: }
4493: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4494: struct _MatSolverTypeForSpecifcType {
4495: MatType mtype;
4496: /* no entry for MAT_FACTOR_NONE */
4497: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4498: MatSolverTypeForSpecifcType next;
4499: };
4501: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4502: struct _MatSolverTypeHolder {
4503: char *name;
4504: MatSolverTypeForSpecifcType handlers;
4505: MatSolverTypeHolder next;
4506: };
4508: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4510: /*@C
4511: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4513: Input Parameters:
4514: + package - name of the package, for example petsc or superlu
4515: . mtype - the matrix type that works with this package
4516: . ftype - the type of factorization supported by the package
4517: - createfactor - routine that will create the factored matrix ready to be used
4519: Level: developer
4521: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4522: `MatGetFactor()`
4523: @*/
4524: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4525: {
4526: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4527: PetscBool flg;
4528: MatSolverTypeForSpecifcType inext, iprev = NULL;
4530: PetscFunctionBegin;
4531: PetscCall(MatInitializePackage());
4532: if (!next) {
4533: PetscCall(PetscNew(&MatSolverTypeHolders));
4534: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4535: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4536: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4537: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4538: PetscFunctionReturn(PETSC_SUCCESS);
4539: }
4540: while (next) {
4541: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4542: if (flg) {
4543: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4544: inext = next->handlers;
4545: while (inext) {
4546: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4547: if (flg) {
4548: inext->createfactor[(int)ftype - 1] = createfactor;
4549: PetscFunctionReturn(PETSC_SUCCESS);
4550: }
4551: iprev = inext;
4552: inext = inext->next;
4553: }
4554: PetscCall(PetscNew(&iprev->next));
4555: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4556: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4557: PetscFunctionReturn(PETSC_SUCCESS);
4558: }
4559: prev = next;
4560: next = next->next;
4561: }
4562: PetscCall(PetscNew(&prev->next));
4563: PetscCall(PetscStrallocpy(package, &prev->next->name));
4564: PetscCall(PetscNew(&prev->next->handlers));
4565: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4566: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4567: PetscFunctionReturn(PETSC_SUCCESS);
4568: }
4570: /*@C
4571: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4573: Input Parameters:
4574: + 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
4575: . ftype - the type of factorization supported by the type
4576: - mtype - the matrix type that works with this type
4578: Output Parameters:
4579: + foundtype - `PETSC_TRUE` if the type was registered
4580: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4581: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4583: Calling sequence of `createfactor`:
4584: + A - the matrix providing the factor matrix
4585: . mtype - the `MatType` of the factor requested
4586: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4588: Level: developer
4590: Note:
4591: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4592: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4593: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4595: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4596: `MatInitializePackage()`
4597: @*/
4598: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType mtype, Mat *B))
4599: {
4600: MatSolverTypeHolder next = MatSolverTypeHolders;
4601: PetscBool flg;
4602: MatSolverTypeForSpecifcType inext;
4604: PetscFunctionBegin;
4605: if (foundtype) *foundtype = PETSC_FALSE;
4606: if (foundmtype) *foundmtype = PETSC_FALSE;
4607: if (createfactor) *createfactor = NULL;
4609: if (type) {
4610: while (next) {
4611: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4612: if (flg) {
4613: if (foundtype) *foundtype = PETSC_TRUE;
4614: inext = next->handlers;
4615: while (inext) {
4616: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4617: if (flg) {
4618: if (foundmtype) *foundmtype = PETSC_TRUE;
4619: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4620: PetscFunctionReturn(PETSC_SUCCESS);
4621: }
4622: inext = inext->next;
4623: }
4624: }
4625: next = next->next;
4626: }
4627: } else {
4628: while (next) {
4629: inext = next->handlers;
4630: while (inext) {
4631: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4632: if (flg && inext->createfactor[(int)ftype - 1]) {
4633: if (foundtype) *foundtype = PETSC_TRUE;
4634: if (foundmtype) *foundmtype = PETSC_TRUE;
4635: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4636: PetscFunctionReturn(PETSC_SUCCESS);
4637: }
4638: inext = inext->next;
4639: }
4640: next = next->next;
4641: }
4642: /* try with base classes inext->mtype */
4643: next = MatSolverTypeHolders;
4644: while (next) {
4645: inext = next->handlers;
4646: while (inext) {
4647: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4648: if (flg && inext->createfactor[(int)ftype - 1]) {
4649: if (foundtype) *foundtype = PETSC_TRUE;
4650: if (foundmtype) *foundmtype = PETSC_TRUE;
4651: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4652: PetscFunctionReturn(PETSC_SUCCESS);
4653: }
4654: inext = inext->next;
4655: }
4656: next = next->next;
4657: }
4658: }
4659: PetscFunctionReturn(PETSC_SUCCESS);
4660: }
4662: PetscErrorCode MatSolverTypeDestroy(void)
4663: {
4664: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4665: MatSolverTypeForSpecifcType inext, iprev;
4667: PetscFunctionBegin;
4668: while (next) {
4669: PetscCall(PetscFree(next->name));
4670: inext = next->handlers;
4671: while (inext) {
4672: PetscCall(PetscFree(inext->mtype));
4673: iprev = inext;
4674: inext = inext->next;
4675: PetscCall(PetscFree(iprev));
4676: }
4677: prev = next;
4678: next = next->next;
4679: PetscCall(PetscFree(prev));
4680: }
4681: MatSolverTypeHolders = NULL;
4682: PetscFunctionReturn(PETSC_SUCCESS);
4683: }
4685: /*@C
4686: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4688: Logically Collective
4690: Input Parameter:
4691: . mat - the matrix
4693: Output Parameter:
4694: . flg - `PETSC_TRUE` if uses the ordering
4696: Level: developer
4698: Note:
4699: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4700: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4702: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4703: @*/
4704: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4705: {
4706: PetscFunctionBegin;
4707: *flg = mat->canuseordering;
4708: PetscFunctionReturn(PETSC_SUCCESS);
4709: }
4711: /*@C
4712: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4714: Logically Collective
4716: Input Parameters:
4717: + mat - the matrix obtained with `MatGetFactor()`
4718: - ftype - the factorization type to be used
4720: Output Parameter:
4721: . otype - the preferred ordering type
4723: Level: developer
4725: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4726: @*/
4727: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4728: {
4729: PetscFunctionBegin;
4730: *otype = mat->preferredordering[ftype];
4731: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4732: PetscFunctionReturn(PETSC_SUCCESS);
4733: }
4735: /*@C
4736: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4738: Collective
4740: Input Parameters:
4741: + mat - the matrix
4742: . type - name of solver type, for example, superlu, petsc (to use PETSc's solver if it is available), if this is 'NULL' then the first result that satisfies
4743: the other criteria is returned
4744: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4746: Output Parameter:
4747: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4749: Options Database Keys:
4750: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4751: - -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4752: One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.
4754: Level: intermediate
4756: Notes:
4757: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4758: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4760: Users usually access the factorization solvers via `KSP`
4762: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4763: such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package or --with-package-dir
4765: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4766: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4767: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4769: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4770: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4771: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4773: Developer Note:
4774: This should actually be called `MatCreateFactor()` since it creates a new factor object
4776: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4777: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4778: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4779: @*/
4780: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4781: {
4782: PetscBool foundtype, foundmtype;
4783: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4785: PetscFunctionBegin;
4789: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4790: MatCheckPreallocated(mat, 1);
4792: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4793: if (!foundtype) {
4794: if (type) {
4795: 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],
4796: ((PetscObject)mat)->type_name, type);
4797: } else {
4798: 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);
4799: }
4800: }
4801: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4802: 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);
4804: PetscCall((*conv)(mat, ftype, f));
4805: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4806: PetscFunctionReturn(PETSC_SUCCESS);
4807: }
4809: /*@C
4810: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4812: Not Collective
4814: Input Parameters:
4815: + mat - the matrix
4816: . type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4817: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4819: Output Parameter:
4820: . flg - PETSC_TRUE if the factorization is available
4822: Level: intermediate
4824: Notes:
4825: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4826: such as pastix, superlu, mumps etc.
4828: PETSc must have been ./configure to use the external solver, using the option --download-package
4830: Developer Note:
4831: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4833: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4834: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4835: @*/
4836: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4837: {
4838: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4840: PetscFunctionBegin;
4842: PetscAssertPointer(flg, 4);
4844: *flg = PETSC_FALSE;
4845: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4847: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4848: MatCheckPreallocated(mat, 1);
4850: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4851: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4852: PetscFunctionReturn(PETSC_SUCCESS);
4853: }
4855: /*@
4856: MatDuplicate - Duplicates a matrix including the non-zero structure.
4858: Collective
4860: Input Parameters:
4861: + mat - the matrix
4862: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4863: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4865: Output Parameter:
4866: . M - pointer to place new matrix
4868: Level: intermediate
4870: Notes:
4871: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4873: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4875: 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.
4877: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4878: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4879: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4881: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4882: @*/
4883: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4884: {
4885: Mat B;
4886: VecType vtype;
4887: PetscInt i;
4888: PetscObject dm, container_h, container_d;
4889: void (*viewf)(void);
4891: PetscFunctionBegin;
4894: PetscAssertPointer(M, 3);
4895: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4896: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4897: MatCheckPreallocated(mat, 1);
4899: *M = NULL;
4900: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4901: PetscUseTypeMethod(mat, duplicate, op, M);
4902: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4903: B = *M;
4905: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4906: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4907: PetscCall(MatGetVecType(mat, &vtype));
4908: PetscCall(MatSetVecType(B, vtype));
4910: B->stencil.dim = mat->stencil.dim;
4911: B->stencil.noc = mat->stencil.noc;
4912: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4913: B->stencil.dims[i] = mat->stencil.dims[i];
4914: B->stencil.starts[i] = mat->stencil.starts[i];
4915: }
4917: B->nooffproczerorows = mat->nooffproczerorows;
4918: B->nooffprocentries = mat->nooffprocentries;
4920: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4921: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4922: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4923: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4924: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4925: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4926: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4927: PetscFunctionReturn(PETSC_SUCCESS);
4928: }
4930: /*@
4931: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
4933: Logically Collective
4935: Input Parameter:
4936: . mat - the matrix
4938: Output Parameter:
4939: . v - the diagonal of the matrix
4941: Level: intermediate
4943: Note:
4944: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
4945: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
4946: is larger than `ndiag`, the values of the remaining entries are unspecified.
4948: Currently only correct in parallel for square matrices.
4950: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
4951: @*/
4952: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
4953: {
4954: PetscFunctionBegin;
4958: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4959: MatCheckPreallocated(mat, 1);
4960: if (PetscDefined(USE_DEBUG)) {
4961: PetscInt nv, row, col, ndiag;
4963: PetscCall(VecGetLocalSize(v, &nv));
4964: PetscCall(MatGetLocalSize(mat, &row, &col));
4965: ndiag = PetscMin(row, col);
4966: 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);
4967: }
4969: PetscUseTypeMethod(mat, getdiagonal, v);
4970: PetscCall(PetscObjectStateIncrease((PetscObject)v));
4971: PetscFunctionReturn(PETSC_SUCCESS);
4972: }
4974: /*@C
4975: MatGetRowMin - Gets the minimum value (of the real part) of each
4976: row of the matrix
4978: Logically Collective
4980: Input Parameter:
4981: . mat - the matrix
4983: Output Parameters:
4984: + v - the vector for storing the maximums
4985: - idx - the indices of the column found for each row (optional)
4987: Level: intermediate
4989: Note:
4990: The result of this call are the same as if one converted the matrix to dense format
4991: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
4993: This code is only implemented for a couple of matrix formats.
4995: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
4996: `MatGetRowMax()`
4997: @*/
4998: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
4999: {
5000: PetscFunctionBegin;
5004: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5006: if (!mat->cmap->N) {
5007: PetscCall(VecSet(v, PETSC_MAX_REAL));
5008: if (idx) {
5009: PetscInt i, m = mat->rmap->n;
5010: for (i = 0; i < m; i++) idx[i] = -1;
5011: }
5012: } else {
5013: MatCheckPreallocated(mat, 1);
5014: }
5015: PetscUseTypeMethod(mat, getrowmin, v, idx);
5016: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5017: PetscFunctionReturn(PETSC_SUCCESS);
5018: }
5020: /*@C
5021: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5022: row of the matrix
5024: Logically Collective
5026: Input Parameter:
5027: . mat - the matrix
5029: Output Parameters:
5030: + v - the vector for storing the minimums
5031: - idx - the indices of the column found for each row (or `NULL` if not needed)
5033: Level: intermediate
5035: Notes:
5036: if a row is completely empty or has only 0.0 values then the `idx` value for that
5037: row is 0 (the first column).
5039: This code is only implemented for a couple of matrix formats.
5041: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5042: @*/
5043: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5044: {
5045: PetscFunctionBegin;
5049: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5050: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5052: if (!mat->cmap->N) {
5053: PetscCall(VecSet(v, 0.0));
5054: if (idx) {
5055: PetscInt i, m = mat->rmap->n;
5056: for (i = 0; i < m; i++) idx[i] = -1;
5057: }
5058: } else {
5059: MatCheckPreallocated(mat, 1);
5060: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5061: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5062: }
5063: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5064: PetscFunctionReturn(PETSC_SUCCESS);
5065: }
5067: /*@C
5068: MatGetRowMax - Gets the maximum value (of the real part) of each
5069: row of the matrix
5071: Logically Collective
5073: Input Parameter:
5074: . mat - the matrix
5076: Output Parameters:
5077: + v - the vector for storing the maximums
5078: - idx - the indices of the column found for each row (optional)
5080: Level: intermediate
5082: Notes:
5083: The result of this call are the same as if one converted the matrix to dense format
5084: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5086: This code is only implemented for a couple of matrix formats.
5088: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5089: @*/
5090: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5091: {
5092: PetscFunctionBegin;
5096: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5098: if (!mat->cmap->N) {
5099: PetscCall(VecSet(v, PETSC_MIN_REAL));
5100: if (idx) {
5101: PetscInt i, m = mat->rmap->n;
5102: for (i = 0; i < m; i++) idx[i] = -1;
5103: }
5104: } else {
5105: MatCheckPreallocated(mat, 1);
5106: PetscUseTypeMethod(mat, getrowmax, v, idx);
5107: }
5108: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5109: PetscFunctionReturn(PETSC_SUCCESS);
5110: }
5112: /*@C
5113: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5114: row of the matrix
5116: Logically Collective
5118: Input Parameter:
5119: . mat - the matrix
5121: Output Parameters:
5122: + v - the vector for storing the maximums
5123: - idx - the indices of the column found for each row (or `NULL` if not needed)
5125: Level: intermediate
5127: Notes:
5128: if a row is completely empty or has only 0.0 values then the `idx` value for that
5129: row is 0 (the first column).
5131: This code is only implemented for a couple of matrix formats.
5133: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5134: @*/
5135: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5136: {
5137: PetscFunctionBegin;
5141: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5143: if (!mat->cmap->N) {
5144: PetscCall(VecSet(v, 0.0));
5145: if (idx) {
5146: PetscInt i, m = mat->rmap->n;
5147: for (i = 0; i < m; i++) idx[i] = -1;
5148: }
5149: } else {
5150: MatCheckPreallocated(mat, 1);
5151: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5152: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5153: }
5154: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5155: PetscFunctionReturn(PETSC_SUCCESS);
5156: }
5158: /*@C
5159: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5161: Logically Collective
5163: Input Parameter:
5164: . mat - the matrix
5166: Output Parameter:
5167: . v - the vector for storing the sum
5169: Level: intermediate
5171: This code is only implemented for a couple of matrix formats.
5173: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5174: @*/
5175: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5176: {
5177: PetscFunctionBegin;
5181: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5183: if (!mat->cmap->N) {
5184: PetscCall(VecSet(v, 0.0));
5185: } else {
5186: MatCheckPreallocated(mat, 1);
5187: PetscUseTypeMethod(mat, getrowsumabs, v);
5188: }
5189: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5190: PetscFunctionReturn(PETSC_SUCCESS);
5191: }
5193: /*@
5194: MatGetRowSum - Gets the sum of each row of the matrix
5196: Logically or Neighborhood Collective
5198: Input Parameter:
5199: . mat - the matrix
5201: Output Parameter:
5202: . v - the vector for storing the sum of rows
5204: Level: intermediate
5206: Note:
5207: This code is slow since it is not currently specialized for different formats
5209: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5210: @*/
5211: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5212: {
5213: Vec ones;
5215: PetscFunctionBegin;
5219: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5220: MatCheckPreallocated(mat, 1);
5221: PetscCall(MatCreateVecs(mat, &ones, NULL));
5222: PetscCall(VecSet(ones, 1.));
5223: PetscCall(MatMult(mat, ones, v));
5224: PetscCall(VecDestroy(&ones));
5225: PetscFunctionReturn(PETSC_SUCCESS);
5226: }
5228: /*@
5229: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5230: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5232: Collective
5234: Input Parameter:
5235: . mat - the matrix to provide the transpose
5237: Output Parameter:
5238: . 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
5240: Level: advanced
5242: Note:
5243: 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
5244: routine allows bypassing that call.
5246: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5247: @*/
5248: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5249: {
5250: PetscContainer rB = NULL;
5251: MatParentState *rb = NULL;
5253: PetscFunctionBegin;
5254: PetscCall(PetscNew(&rb));
5255: rb->id = ((PetscObject)mat)->id;
5256: rb->state = 0;
5257: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5258: PetscCall(PetscContainerCreate(PetscObjectComm((PetscObject)B), &rB));
5259: PetscCall(PetscContainerSetPointer(rB, rb));
5260: PetscCall(PetscContainerSetUserDestroy(rB, PetscContainerUserDestroyDefault));
5261: PetscCall(PetscObjectCompose((PetscObject)B, "MatTransposeParent", (PetscObject)rB));
5262: PetscCall(PetscObjectDereference((PetscObject)rB));
5263: PetscFunctionReturn(PETSC_SUCCESS);
5264: }
5266: /*@
5267: MatTranspose - Computes an in-place or out-of-place transpose of a matrix.
5269: Collective
5271: Input Parameters:
5272: + mat - the matrix to transpose
5273: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5275: Output Parameter:
5276: . B - the transpose
5278: Level: intermediate
5280: Notes:
5281: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5283: `MAT_REUSE_MATRIX` uses the `B` matrix obtained from a previous call to this function with `MAT_INITIAL_MATRIX`. If you already have a matrix to contain the
5284: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5286: 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.
5288: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.
5290: If mat is unchanged from the last call this function returns immediately without recomputing the result
5292: If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`
5294: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5295: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5296: @*/
5297: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5298: {
5299: PetscContainer rB = NULL;
5300: MatParentState *rb = NULL;
5302: PetscFunctionBegin;
5305: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5306: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5307: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5308: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5309: MatCheckPreallocated(mat, 1);
5310: if (reuse == MAT_REUSE_MATRIX) {
5311: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5312: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5313: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5314: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5315: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5316: }
5318: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5319: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5320: PetscUseTypeMethod(mat, transpose, reuse, B);
5321: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5322: }
5323: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5325: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5326: if (reuse != MAT_INPLACE_MATRIX) {
5327: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5328: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5329: rb->state = ((PetscObject)mat)->state;
5330: rb->nonzerostate = mat->nonzerostate;
5331: }
5332: PetscFunctionReturn(PETSC_SUCCESS);
5333: }
5335: /*@
5336: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5338: Collective
5340: Input Parameter:
5341: . A - the matrix to transpose
5343: Output Parameter:
5344: . 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
5345: numerical portion.
5347: Level: intermediate
5349: Note:
5350: This is not supported for many matrix types, use `MatTranspose()` in those cases
5352: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5353: @*/
5354: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5355: {
5356: PetscFunctionBegin;
5359: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5360: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5361: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5362: PetscUseTypeMethod(A, transposesymbolic, B);
5363: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5365: PetscCall(MatTransposeSetPrecursor(A, *B));
5366: PetscFunctionReturn(PETSC_SUCCESS);
5367: }
5369: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5370: {
5371: PetscContainer rB;
5372: MatParentState *rb;
5374: PetscFunctionBegin;
5377: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5378: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5379: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5380: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5381: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5382: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5383: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5384: PetscFunctionReturn(PETSC_SUCCESS);
5385: }
5387: /*@
5388: MatIsTranspose - Test whether a matrix is another one's transpose,
5389: or its own, in which case it tests symmetry.
5391: Collective
5393: Input Parameters:
5394: + A - the matrix to test
5395: . B - the matrix to test against, this can equal the first parameter
5396: - tol - tolerance, differences between entries smaller than this are counted as zero
5398: Output Parameter:
5399: . flg - the result
5401: Level: intermediate
5403: Notes:
5404: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5405: test involves parallel copies of the block off-diagonal parts of the matrix.
5407: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5408: @*/
5409: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5410: {
5411: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5413: PetscFunctionBegin;
5416: PetscAssertPointer(flg, 4);
5417: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5418: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5419: *flg = PETSC_FALSE;
5420: if (f && g) {
5421: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5422: PetscCall((*f)(A, B, tol, flg));
5423: } else {
5424: MatType mattype;
5426: PetscCall(MatGetType(f ? B : A, &mattype));
5427: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5428: }
5429: PetscFunctionReturn(PETSC_SUCCESS);
5430: }
5432: /*@
5433: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5435: Collective
5437: Input Parameters:
5438: + mat - the matrix to transpose and complex conjugate
5439: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5441: Output Parameter:
5442: . B - the Hermitian transpose
5444: Level: intermediate
5446: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5447: @*/
5448: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5449: {
5450: PetscFunctionBegin;
5451: PetscCall(MatTranspose(mat, reuse, B));
5452: #if defined(PETSC_USE_COMPLEX)
5453: PetscCall(MatConjugate(*B));
5454: #endif
5455: PetscFunctionReturn(PETSC_SUCCESS);
5456: }
5458: /*@
5459: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5461: Collective
5463: Input Parameters:
5464: + A - the matrix to test
5465: . B - the matrix to test against, this can equal the first parameter
5466: - tol - tolerance, differences between entries smaller than this are counted as zero
5468: Output Parameter:
5469: . flg - the result
5471: Level: intermediate
5473: Notes:
5474: Only available for `MATAIJ` matrices.
5476: The sequential algorithm
5477: has a running time of the order of the number of nonzeros; the parallel
5478: test involves parallel copies of the block off-diagonal parts of the matrix.
5480: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5481: @*/
5482: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5483: {
5484: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5486: PetscFunctionBegin;
5489: PetscAssertPointer(flg, 4);
5490: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5491: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5492: if (f && g) {
5493: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5494: PetscCall((*f)(A, B, tol, flg));
5495: }
5496: PetscFunctionReturn(PETSC_SUCCESS);
5497: }
5499: /*@
5500: MatPermute - Creates a new matrix with rows and columns permuted from the
5501: original.
5503: Collective
5505: Input Parameters:
5506: + mat - the matrix to permute
5507: . row - row permutation, each processor supplies only the permutation for its rows
5508: - col - column permutation, each processor supplies only the permutation for its columns
5510: Output Parameter:
5511: . B - the permuted matrix
5513: Level: advanced
5515: Note:
5516: The index sets map from row/col of permuted matrix to row/col of original matrix.
5517: The index sets should be on the same communicator as mat and have the same local sizes.
5519: Developer Note:
5520: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5521: exploit the fact that row and col are permutations, consider implementing the
5522: more general `MatCreateSubMatrix()` instead.
5524: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5525: @*/
5526: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5527: {
5528: PetscFunctionBegin;
5533: PetscAssertPointer(B, 4);
5534: PetscCheckSameComm(mat, 1, row, 2);
5535: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5536: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5537: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5538: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5539: MatCheckPreallocated(mat, 1);
5541: if (mat->ops->permute) {
5542: PetscUseTypeMethod(mat, permute, row, col, B);
5543: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5544: } else {
5545: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5546: }
5547: PetscFunctionReturn(PETSC_SUCCESS);
5548: }
5550: /*@
5551: MatEqual - Compares two matrices.
5553: Collective
5555: Input Parameters:
5556: + A - the first matrix
5557: - B - the second matrix
5559: Output Parameter:
5560: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5562: Level: intermediate
5564: .seealso: [](ch_matrices), `Mat`
5565: @*/
5566: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5567: {
5568: PetscFunctionBegin;
5573: PetscAssertPointer(flg, 3);
5574: PetscCheckSameComm(A, 1, B, 2);
5575: MatCheckPreallocated(A, 1);
5576: MatCheckPreallocated(B, 2);
5577: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5578: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5579: 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,
5580: B->cmap->N);
5581: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5582: PetscUseTypeMethod(A, equal, B, flg);
5583: } else {
5584: PetscCall(MatMultEqual(A, B, 10, flg));
5585: }
5586: PetscFunctionReturn(PETSC_SUCCESS);
5587: }
5589: /*@
5590: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5591: matrices that are stored as vectors. Either of the two scaling
5592: matrices can be `NULL`.
5594: Collective
5596: Input Parameters:
5597: + mat - the matrix to be scaled
5598: . l - the left scaling vector (or `NULL`)
5599: - r - the right scaling vector (or `NULL`)
5601: Level: intermediate
5603: Note:
5604: `MatDiagonalScale()` computes $A = LAR$, where
5605: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5606: The L scales the rows of the matrix, the R scales the columns of the matrix.
5608: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5609: @*/
5610: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5611: {
5612: PetscFunctionBegin;
5615: if (l) {
5617: PetscCheckSameComm(mat, 1, l, 2);
5618: }
5619: if (r) {
5621: PetscCheckSameComm(mat, 1, r, 3);
5622: }
5623: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5624: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5625: MatCheckPreallocated(mat, 1);
5626: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5628: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5629: PetscUseTypeMethod(mat, diagonalscale, l, r);
5630: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5631: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5632: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5633: PetscFunctionReturn(PETSC_SUCCESS);
5634: }
5636: /*@
5637: MatScale - Scales all elements of a matrix by a given number.
5639: Logically Collective
5641: Input Parameters:
5642: + mat - the matrix to be scaled
5643: - a - the scaling value
5645: Level: intermediate
5647: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5648: @*/
5649: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5650: {
5651: PetscFunctionBegin;
5654: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5655: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5657: MatCheckPreallocated(mat, 1);
5659: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5660: if (a != (PetscScalar)1.0) {
5661: PetscUseTypeMethod(mat, scale, a);
5662: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5663: }
5664: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5665: PetscFunctionReturn(PETSC_SUCCESS);
5666: }
5668: /*@
5669: MatNorm - Calculates various norms of a matrix.
5671: Collective
5673: Input Parameters:
5674: + mat - the matrix
5675: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5677: Output Parameter:
5678: . nrm - the resulting norm
5680: Level: intermediate
5682: .seealso: [](ch_matrices), `Mat`
5683: @*/
5684: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5685: {
5686: PetscFunctionBegin;
5689: PetscAssertPointer(nrm, 3);
5691: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5692: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5693: MatCheckPreallocated(mat, 1);
5695: PetscUseTypeMethod(mat, norm, type, nrm);
5696: PetscFunctionReturn(PETSC_SUCCESS);
5697: }
5699: /*
5700: This variable is used to prevent counting of MatAssemblyBegin() that
5701: are called from within a MatAssemblyEnd().
5702: */
5703: static PetscInt MatAssemblyEnd_InUse = 0;
5704: /*@
5705: MatAssemblyBegin - Begins assembling the matrix. This routine should
5706: be called after completing all calls to `MatSetValues()`.
5708: Collective
5710: Input Parameters:
5711: + mat - the matrix
5712: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5714: Level: beginner
5716: Notes:
5717: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5718: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5720: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5721: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5722: using the matrix.
5724: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5725: 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
5726: a global collective operation requiring all processes that share the matrix.
5728: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5729: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5730: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5732: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5733: @*/
5734: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5735: {
5736: PetscFunctionBegin;
5739: MatCheckPreallocated(mat, 1);
5740: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5741: if (mat->assembled) {
5742: mat->was_assembled = PETSC_TRUE;
5743: mat->assembled = PETSC_FALSE;
5744: }
5746: if (!MatAssemblyEnd_InUse) {
5747: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5748: PetscTryTypeMethod(mat, assemblybegin, type);
5749: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5750: } else PetscTryTypeMethod(mat, assemblybegin, type);
5751: PetscFunctionReturn(PETSC_SUCCESS);
5752: }
5754: /*@
5755: MatAssembled - Indicates if a matrix has been assembled and is ready for
5756: use; for example, in matrix-vector product.
5758: Not Collective
5760: Input Parameter:
5761: . mat - the matrix
5763: Output Parameter:
5764: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5766: Level: advanced
5768: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5769: @*/
5770: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5771: {
5772: PetscFunctionBegin;
5774: PetscAssertPointer(assembled, 2);
5775: *assembled = mat->assembled;
5776: PetscFunctionReturn(PETSC_SUCCESS);
5777: }
5779: /*@
5780: MatAssemblyEnd - Completes assembling the matrix. This routine should
5781: be called after `MatAssemblyBegin()`.
5783: Collective
5785: Input Parameters:
5786: + mat - the matrix
5787: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5789: Options Database Keys:
5790: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5791: . -mat_view ::ascii_info_detail - Prints more detailed info
5792: . -mat_view - Prints matrix in ASCII format
5793: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5794: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5795: . -display <name> - Sets display name (default is host)
5796: . -draw_pause <sec> - Sets number of seconds to pause after display
5797: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5798: . -viewer_socket_machine <machine> - Machine to use for socket
5799: . -viewer_socket_port <port> - Port number to use for socket
5800: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5802: Level: beginner
5804: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5805: @*/
5806: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5807: {
5808: static PetscInt inassm = 0;
5809: PetscBool flg = PETSC_FALSE;
5811: PetscFunctionBegin;
5815: inassm++;
5816: MatAssemblyEnd_InUse++;
5817: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5818: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5819: PetscTryTypeMethod(mat, assemblyend, type);
5820: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5821: } else PetscTryTypeMethod(mat, assemblyend, type);
5823: /* Flush assembly is not a true assembly */
5824: if (type != MAT_FLUSH_ASSEMBLY) {
5825: if (mat->num_ass) {
5826: if (!mat->symmetry_eternal) {
5827: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5828: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5829: }
5830: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5831: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5832: }
5833: mat->num_ass++;
5834: mat->assembled = PETSC_TRUE;
5835: mat->ass_nonzerostate = mat->nonzerostate;
5836: }
5838: mat->insertmode = NOT_SET_VALUES;
5839: MatAssemblyEnd_InUse--;
5840: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5841: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5842: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5844: if (mat->checksymmetryonassembly) {
5845: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5846: if (flg) {
5847: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5848: } else {
5849: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5850: }
5851: }
5852: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5853: }
5854: inassm--;
5855: PetscFunctionReturn(PETSC_SUCCESS);
5856: }
5858: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5859: /*@
5860: MatSetOption - Sets a parameter option for a matrix. Some options
5861: may be specific to certain storage formats. Some options
5862: determine how values will be inserted (or added). Sorted,
5863: row-oriented input will generally assemble the fastest. The default
5864: is row-oriented.
5866: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5868: Input Parameters:
5869: + mat - the matrix
5870: . op - the option, one of those listed below (and possibly others),
5871: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5873: Options Describing Matrix Structure:
5874: + `MAT_SPD` - symmetric positive definite
5875: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5876: . `MAT_HERMITIAN` - transpose is the complex conjugation
5877: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5878: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5879: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5880: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5882: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5883: do not need to be computed (usually at a high cost)
5885: Options For Use with `MatSetValues()`:
5886: Insert a logically dense subblock, which can be
5887: . `MAT_ROW_ORIENTED` - row-oriented (default)
5889: These options reflect the data you pass in with `MatSetValues()`; it has
5890: nothing to do with how the data is stored internally in the matrix
5891: data structure.
5893: When (re)assembling a matrix, we can restrict the input for
5894: efficiency/debugging purposes. These options include
5895: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5896: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5897: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5898: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5899: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5900: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5901: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5902: performance for very large process counts.
5903: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5904: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5905: functions, instead sending only neighbor messages.
5907: Level: intermediate
5909: Notes:
5910: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5912: Some options are relevant only for particular matrix types and
5913: are thus ignored by others. Other options are not supported by
5914: certain matrix types and will generate an error message if set.
5916: If using Fortran to compute a matrix, one may need to
5917: use the column-oriented option (or convert to the row-oriented
5918: format).
5920: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5921: that would generate a new entry in the nonzero structure is instead
5922: ignored. Thus, if memory has not already been allocated for this particular
5923: data, then the insertion is ignored. For dense matrices, in which
5924: the entire array is allocated, no entries are ever ignored.
5925: Set after the first `MatAssemblyEnd()`. If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5927: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5928: that would generate a new entry in the nonzero structure instead produces
5929: 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
5931: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5932: that would generate a new entry that has not been preallocated will
5933: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5934: only.) This is a useful flag when debugging matrix memory preallocation.
5935: If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5937: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5938: other processors should be dropped, rather than stashed.
5939: This is useful if you know that the "owning" processor is also
5940: always generating the correct matrix entries, so that PETSc need
5941: not transfer duplicate entries generated on another processor.
5943: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
5944: searches during matrix assembly. When this flag is set, the hash table
5945: is created during the first matrix assembly. This hash table is
5946: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
5947: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
5948: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
5949: supported by `MATMPIBAIJ` format only.
5951: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
5952: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
5954: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
5955: a zero location in the matrix
5957: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
5959: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
5960: zero row routines and thus improves performance for very large process counts.
5962: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
5963: part of the matrix (since they should match the upper triangular part).
5965: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
5966: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
5967: with finite difference schemes with non-periodic boundary conditions.
5969: Developer Note:
5970: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
5971: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
5972: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
5973: not changed.
5975: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
5976: @*/
5977: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
5978: {
5979: PetscFunctionBegin;
5981: if (op > 0) {
5984: }
5986: 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);
5988: switch (op) {
5989: case MAT_FORCE_DIAGONAL_ENTRIES:
5990: mat->force_diagonals = flg;
5991: PetscFunctionReturn(PETSC_SUCCESS);
5992: case MAT_NO_OFF_PROC_ENTRIES:
5993: mat->nooffprocentries = flg;
5994: PetscFunctionReturn(PETSC_SUCCESS);
5995: case MAT_SUBSET_OFF_PROC_ENTRIES:
5996: mat->assembly_subset = flg;
5997: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
5998: #if !defined(PETSC_HAVE_MPIUNI)
5999: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6000: #endif
6001: mat->stash.first_assembly_done = PETSC_FALSE;
6002: }
6003: PetscFunctionReturn(PETSC_SUCCESS);
6004: case MAT_NO_OFF_PROC_ZERO_ROWS:
6005: mat->nooffproczerorows = flg;
6006: PetscFunctionReturn(PETSC_SUCCESS);
6007: case MAT_SPD:
6008: if (flg) {
6009: mat->spd = PETSC_BOOL3_TRUE;
6010: mat->symmetric = PETSC_BOOL3_TRUE;
6011: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6012: } else {
6013: mat->spd = PETSC_BOOL3_FALSE;
6014: }
6015: break;
6016: case MAT_SYMMETRIC:
6017: mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6018: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6019: #if !defined(PETSC_USE_COMPLEX)
6020: mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6021: #endif
6022: break;
6023: case MAT_HERMITIAN:
6024: mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6025: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6026: #if !defined(PETSC_USE_COMPLEX)
6027: mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6028: #endif
6029: break;
6030: case MAT_STRUCTURALLY_SYMMETRIC:
6031: mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6032: break;
6033: case MAT_SYMMETRY_ETERNAL:
6034: 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");
6035: mat->symmetry_eternal = flg;
6036: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6037: break;
6038: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6039: 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");
6040: mat->structural_symmetry_eternal = flg;
6041: break;
6042: case MAT_SPD_ETERNAL:
6043: 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");
6044: mat->spd_eternal = flg;
6045: if (flg) {
6046: mat->structural_symmetry_eternal = PETSC_TRUE;
6047: mat->symmetry_eternal = PETSC_TRUE;
6048: }
6049: break;
6050: case MAT_STRUCTURE_ONLY:
6051: mat->structure_only = flg;
6052: break;
6053: case MAT_SORTED_FULL:
6054: mat->sortedfull = flg;
6055: break;
6056: default:
6057: break;
6058: }
6059: PetscTryTypeMethod(mat, setoption, op, flg);
6060: PetscFunctionReturn(PETSC_SUCCESS);
6061: }
6063: /*@
6064: MatGetOption - Gets a parameter option that has been set for a matrix.
6066: Logically Collective
6068: Input Parameters:
6069: + mat - the matrix
6070: - op - the option, this only responds to certain options, check the code for which ones
6072: Output Parameter:
6073: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6075: Level: intermediate
6077: Notes:
6078: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6080: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6081: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6083: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6084: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6085: @*/
6086: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6087: {
6088: PetscFunctionBegin;
6092: 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);
6093: 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()");
6095: switch (op) {
6096: case MAT_NO_OFF_PROC_ENTRIES:
6097: *flg = mat->nooffprocentries;
6098: break;
6099: case MAT_NO_OFF_PROC_ZERO_ROWS:
6100: *flg = mat->nooffproczerorows;
6101: break;
6102: case MAT_SYMMETRIC:
6103: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6104: break;
6105: case MAT_HERMITIAN:
6106: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6107: break;
6108: case MAT_STRUCTURALLY_SYMMETRIC:
6109: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6110: break;
6111: case MAT_SPD:
6112: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6113: break;
6114: case MAT_SYMMETRY_ETERNAL:
6115: *flg = mat->symmetry_eternal;
6116: break;
6117: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6118: *flg = mat->symmetry_eternal;
6119: break;
6120: default:
6121: break;
6122: }
6123: PetscFunctionReturn(PETSC_SUCCESS);
6124: }
6126: /*@
6127: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6128: this routine retains the old nonzero structure.
6130: Logically Collective
6132: Input Parameter:
6133: . mat - the matrix
6135: Level: intermediate
6137: Note:
6138: 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.
6139: See the Performance chapter of the users manual for information on preallocating matrices.
6141: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6142: @*/
6143: PetscErrorCode MatZeroEntries(Mat mat)
6144: {
6145: PetscFunctionBegin;
6148: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6149: 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");
6150: MatCheckPreallocated(mat, 1);
6152: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6153: PetscUseTypeMethod(mat, zeroentries);
6154: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6155: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6156: PetscFunctionReturn(PETSC_SUCCESS);
6157: }
6159: /*@
6160: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6161: of a set of rows and columns of a matrix.
6163: Collective
6165: Input Parameters:
6166: + mat - the matrix
6167: . numRows - the number of rows/columns to zero
6168: . rows - the global row indices
6169: . diag - value put in the diagonal of the eliminated rows
6170: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6171: - b - optional vector of the right hand side, that will be adjusted by provided solution entries
6173: Level: intermediate
6175: Notes:
6176: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6178: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6179: 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
6181: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6182: Krylov method to take advantage of the known solution on the zeroed rows.
6184: For the parallel case, all processes that share the matrix (i.e.,
6185: those in the communicator used for matrix creation) MUST call this
6186: routine, regardless of whether any rows being zeroed are owned by
6187: them.
6189: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6190: 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
6191: missing.
6193: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6194: list only rows local to itself).
6196: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6198: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6199: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6200: @*/
6201: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6202: {
6203: PetscFunctionBegin;
6206: if (numRows) PetscAssertPointer(rows, 3);
6207: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6208: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6209: MatCheckPreallocated(mat, 1);
6211: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6212: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6213: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6214: PetscFunctionReturn(PETSC_SUCCESS);
6215: }
6217: /*@
6218: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6219: of a set of rows and columns of a matrix.
6221: Collective
6223: Input Parameters:
6224: + mat - the matrix
6225: . is - the rows to zero
6226: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6227: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6228: - b - optional vector of right hand side, that will be adjusted by provided solution
6230: Level: intermediate
6232: Note:
6233: See `MatZeroRowsColumns()` for details on how this routine operates.
6235: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6236: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6237: @*/
6238: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6239: {
6240: PetscInt numRows;
6241: const PetscInt *rows;
6243: PetscFunctionBegin;
6248: PetscCall(ISGetLocalSize(is, &numRows));
6249: PetscCall(ISGetIndices(is, &rows));
6250: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6251: PetscCall(ISRestoreIndices(is, &rows));
6252: PetscFunctionReturn(PETSC_SUCCESS);
6253: }
6255: /*@
6256: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6257: of a set of rows of a matrix.
6259: Collective
6261: Input Parameters:
6262: + mat - the matrix
6263: . numRows - the number of rows to zero
6264: . rows - the global row indices
6265: . diag - value put in the diagonal of the zeroed rows
6266: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6267: - b - optional vector of right hand side, that will be adjusted by provided solution entries
6269: Level: intermediate
6271: Notes:
6272: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6274: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6276: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6277: Krylov method to take advantage of the known solution on the zeroed rows.
6279: 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)
6280: from the matrix.
6282: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6283: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense and block diagonal
6284: formats this does not alter the nonzero structure.
6286: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6287: of the matrix is not changed the values are
6288: merely zeroed.
6290: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6291: formats can optionally remove the main diagonal entry from the
6292: nonzero structure as well, by passing 0.0 as the final argument).
6294: For the parallel case, all processes that share the matrix (i.e.,
6295: those in the communicator used for matrix creation) MUST call this
6296: routine, regardless of whether any rows being zeroed are owned by
6297: them.
6299: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6300: list only rows local to itself).
6302: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6303: owns that are to be zeroed. This saves a global synchronization in the implementation.
6305: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6306: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6307: @*/
6308: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6309: {
6310: PetscFunctionBegin;
6313: if (numRows) PetscAssertPointer(rows, 3);
6314: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6315: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6316: MatCheckPreallocated(mat, 1);
6318: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6319: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6320: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6321: PetscFunctionReturn(PETSC_SUCCESS);
6322: }
6324: /*@
6325: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6326: of a set of rows of a matrix.
6328: Collective
6330: Input Parameters:
6331: + mat - the matrix
6332: . is - index set of rows to remove (if `NULL` then no row is removed)
6333: . diag - value put in all diagonals of eliminated rows
6334: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6335: - b - optional vector of right hand side, that will be adjusted by provided solution
6337: Level: intermediate
6339: Note:
6340: See `MatZeroRows()` for details on how this routine operates.
6342: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6343: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6344: @*/
6345: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6346: {
6347: PetscInt numRows = 0;
6348: const PetscInt *rows = NULL;
6350: PetscFunctionBegin;
6353: if (is) {
6355: PetscCall(ISGetLocalSize(is, &numRows));
6356: PetscCall(ISGetIndices(is, &rows));
6357: }
6358: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6359: if (is) PetscCall(ISRestoreIndices(is, &rows));
6360: PetscFunctionReturn(PETSC_SUCCESS);
6361: }
6363: /*@
6364: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6365: of a set of rows of a matrix. These rows must be local to the process.
6367: Collective
6369: Input Parameters:
6370: + mat - the matrix
6371: . numRows - the number of rows to remove
6372: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6373: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6374: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6375: - b - optional vector of right hand side, that will be adjusted by provided solution
6377: Level: intermediate
6379: Notes:
6380: See `MatZeroRows()` for details on how this routine operates.
6382: The grid coordinates are across the entire grid, not just the local portion
6384: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6385: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6386: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6387: `DM_BOUNDARY_PERIODIC` boundary type.
6389: 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
6390: a single value per point) you can skip filling those indices.
6392: Fortran Note:
6393: `idxm` and `idxn` should be declared as
6394: $ MatStencil idxm(4, m)
6395: and the values inserted using
6396: .vb
6397: idxm(MatStencil_i, 1) = i
6398: idxm(MatStencil_j, 1) = j
6399: idxm(MatStencil_k, 1) = k
6400: idxm(MatStencil_c, 1) = c
6401: etc
6402: .ve
6404: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsl()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6405: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6406: @*/
6407: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6408: {
6409: PetscInt dim = mat->stencil.dim;
6410: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6411: PetscInt *dims = mat->stencil.dims + 1;
6412: PetscInt *starts = mat->stencil.starts;
6413: PetscInt *dxm = (PetscInt *)rows;
6414: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6416: PetscFunctionBegin;
6419: if (numRows) PetscAssertPointer(rows, 3);
6421: PetscCall(PetscMalloc1(numRows, &jdxm));
6422: for (i = 0; i < numRows; ++i) {
6423: /* Skip unused dimensions (they are ordered k, j, i, c) */
6424: for (j = 0; j < 3 - sdim; ++j) dxm++;
6425: /* Local index in X dir */
6426: tmp = *dxm++ - starts[0];
6427: /* Loop over remaining dimensions */
6428: for (j = 0; j < dim - 1; ++j) {
6429: /* If nonlocal, set index to be negative */
6430: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6431: /* Update local index */
6432: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6433: }
6434: /* Skip component slot if necessary */
6435: if (mat->stencil.noc) dxm++;
6436: /* Local row number */
6437: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6438: }
6439: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6440: PetscCall(PetscFree(jdxm));
6441: PetscFunctionReturn(PETSC_SUCCESS);
6442: }
6444: /*@
6445: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6446: of a set of rows and columns of a matrix.
6448: Collective
6450: Input Parameters:
6451: + mat - the matrix
6452: . numRows - the number of rows/columns to remove
6453: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
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: Notes:
6461: See `MatZeroRowsColumns()` for details on how this routine operates.
6463: The grid coordinates are across the entire grid, not just the local portion
6465: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6466: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6467: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6468: `DM_BOUNDARY_PERIODIC` boundary type.
6470: 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
6471: a single value per point) you can skip filling those indices.
6473: Fortran Note:
6474: `idxm` and `idxn` should be declared as
6475: $ MatStencil idxm(4, m)
6476: and the values inserted using
6477: .vb
6478: idxm(MatStencil_i, 1) = i
6479: idxm(MatStencil_j, 1) = j
6480: idxm(MatStencil_k, 1) = k
6481: idxm(MatStencil_c, 1) = c
6482: etc
6483: .ve
6485: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6486: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6487: @*/
6488: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6489: {
6490: PetscInt dim = mat->stencil.dim;
6491: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6492: PetscInt *dims = mat->stencil.dims + 1;
6493: PetscInt *starts = mat->stencil.starts;
6494: PetscInt *dxm = (PetscInt *)rows;
6495: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6497: PetscFunctionBegin;
6500: if (numRows) PetscAssertPointer(rows, 3);
6502: PetscCall(PetscMalloc1(numRows, &jdxm));
6503: for (i = 0; i < numRows; ++i) {
6504: /* Skip unused dimensions (they are ordered k, j, i, c) */
6505: for (j = 0; j < 3 - sdim; ++j) dxm++;
6506: /* Local index in X dir */
6507: tmp = *dxm++ - starts[0];
6508: /* Loop over remaining dimensions */
6509: for (j = 0; j < dim - 1; ++j) {
6510: /* If nonlocal, set index to be negative */
6511: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6512: /* Update local index */
6513: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6514: }
6515: /* Skip component slot if necessary */
6516: if (mat->stencil.noc) dxm++;
6517: /* Local row number */
6518: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6519: }
6520: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6521: PetscCall(PetscFree(jdxm));
6522: PetscFunctionReturn(PETSC_SUCCESS);
6523: }
6525: /*@C
6526: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6527: of a set of rows of a matrix; using local numbering of rows.
6529: Collective
6531: Input Parameters:
6532: + mat - the matrix
6533: . numRows - the number of rows to remove
6534: . rows - the local row indices
6535: . diag - value put in all diagonals of eliminated rows
6536: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6537: - b - optional vector of right hand side, that will be adjusted by provided solution
6539: Level: intermediate
6541: Notes:
6542: Before calling `MatZeroRowsLocal()`, the user must first set the
6543: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6545: See `MatZeroRows()` for details on how this routine operates.
6547: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6548: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6549: @*/
6550: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6551: {
6552: PetscFunctionBegin;
6555: if (numRows) PetscAssertPointer(rows, 3);
6556: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6557: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6558: MatCheckPreallocated(mat, 1);
6560: if (mat->ops->zerorowslocal) {
6561: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6562: } else {
6563: IS is, newis;
6564: const PetscInt *newRows;
6566: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6567: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6568: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6569: PetscCall(ISGetIndices(newis, &newRows));
6570: PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6571: PetscCall(ISRestoreIndices(newis, &newRows));
6572: PetscCall(ISDestroy(&newis));
6573: PetscCall(ISDestroy(&is));
6574: }
6575: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6576: PetscFunctionReturn(PETSC_SUCCESS);
6577: }
6579: /*@
6580: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6581: of a set of rows of a matrix; using local numbering of rows.
6583: Collective
6585: Input Parameters:
6586: + mat - the matrix
6587: . is - index set of rows to remove
6588: . diag - value put in all diagonals of eliminated rows
6589: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6590: - b - optional vector of right hand side, that will be adjusted by provided solution
6592: Level: intermediate
6594: Notes:
6595: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6596: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6598: See `MatZeroRows()` for details on how this routine operates.
6600: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6601: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6602: @*/
6603: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6604: {
6605: PetscInt numRows;
6606: const PetscInt *rows;
6608: PetscFunctionBegin;
6612: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6613: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6614: MatCheckPreallocated(mat, 1);
6616: PetscCall(ISGetLocalSize(is, &numRows));
6617: PetscCall(ISGetIndices(is, &rows));
6618: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6619: PetscCall(ISRestoreIndices(is, &rows));
6620: PetscFunctionReturn(PETSC_SUCCESS);
6621: }
6623: /*@
6624: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6625: of a set of rows and columns of a matrix; using local numbering of rows.
6627: Collective
6629: Input Parameters:
6630: + mat - the matrix
6631: . numRows - the number of rows to remove
6632: . rows - the global row indices
6633: . diag - value put in all diagonals of eliminated rows
6634: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6635: - b - optional vector of right hand side, that will be adjusted by provided solution
6637: Level: intermediate
6639: Notes:
6640: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6641: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6643: See `MatZeroRowsColumns()` for details on how this routine operates.
6645: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6646: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6647: @*/
6648: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6649: {
6650: IS is, newis;
6651: const PetscInt *newRows;
6653: PetscFunctionBegin;
6656: if (numRows) PetscAssertPointer(rows, 3);
6657: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6658: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6659: MatCheckPreallocated(mat, 1);
6661: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6662: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6663: PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6664: PetscCall(ISGetIndices(newis, &newRows));
6665: PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6666: PetscCall(ISRestoreIndices(newis, &newRows));
6667: PetscCall(ISDestroy(&newis));
6668: PetscCall(ISDestroy(&is));
6669: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6670: PetscFunctionReturn(PETSC_SUCCESS);
6671: }
6673: /*@
6674: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6675: of a set of rows and columns of a matrix; using local numbering of rows.
6677: Collective
6679: Input Parameters:
6680: + mat - the matrix
6681: . is - index set of rows to remove
6682: . diag - value put in all diagonals of eliminated rows
6683: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6684: - b - optional vector of right hand side, that will be adjusted by provided solution
6686: Level: intermediate
6688: Notes:
6689: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6690: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6692: See `MatZeroRowsColumns()` for details on how this routine operates.
6694: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6695: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6696: @*/
6697: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6698: {
6699: PetscInt numRows;
6700: const PetscInt *rows;
6702: PetscFunctionBegin;
6706: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6707: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6708: MatCheckPreallocated(mat, 1);
6710: PetscCall(ISGetLocalSize(is, &numRows));
6711: PetscCall(ISGetIndices(is, &rows));
6712: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6713: PetscCall(ISRestoreIndices(is, &rows));
6714: PetscFunctionReturn(PETSC_SUCCESS);
6715: }
6717: /*@C
6718: MatGetSize - Returns the numbers of rows and columns in a matrix.
6720: Not Collective
6722: Input Parameter:
6723: . mat - the matrix
6725: Output Parameters:
6726: + m - the number of global rows
6727: - n - the number of global columns
6729: Level: beginner
6731: Note:
6732: Both output parameters can be `NULL` on input.
6734: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6735: @*/
6736: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6737: {
6738: PetscFunctionBegin;
6740: if (m) *m = mat->rmap->N;
6741: if (n) *n = mat->cmap->N;
6742: PetscFunctionReturn(PETSC_SUCCESS);
6743: }
6745: /*@C
6746: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6747: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6749: Not Collective
6751: Input Parameter:
6752: . mat - the matrix
6754: Output Parameters:
6755: + m - the number of local rows, use `NULL` to not obtain this value
6756: - n - the number of local columns, use `NULL` to not obtain this value
6758: Level: beginner
6760: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6761: @*/
6762: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6763: {
6764: PetscFunctionBegin;
6766: if (m) PetscAssertPointer(m, 2);
6767: if (n) PetscAssertPointer(n, 3);
6768: if (m) *m = mat->rmap->n;
6769: if (n) *n = mat->cmap->n;
6770: PetscFunctionReturn(PETSC_SUCCESS);
6771: }
6773: /*@C
6774: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6775: vector one multiplies this matrix by that are owned by this processor.
6777: Not Collective, unless matrix has not been allocated, then collective
6779: Input Parameter:
6780: . mat - the matrix
6782: Output Parameters:
6783: + m - the global index of the first local column, use `NULL` to not obtain this value
6784: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6786: Level: developer
6788: Note:
6789: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6790: Layouts](sec_matlayout) for details on matrix layouts.
6792: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6793: @*/
6794: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6795: {
6796: PetscFunctionBegin;
6799: if (m) PetscAssertPointer(m, 2);
6800: if (n) PetscAssertPointer(n, 3);
6801: MatCheckPreallocated(mat, 1);
6802: if (m) *m = mat->cmap->rstart;
6803: if (n) *n = mat->cmap->rend;
6804: PetscFunctionReturn(PETSC_SUCCESS);
6805: }
6807: /*@C
6808: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6809: this MPI process.
6811: Not Collective
6813: Input Parameter:
6814: . mat - the matrix
6816: Output Parameters:
6817: + m - the global index of the first local row, use `NULL` to not obtain this value
6818: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6820: Level: beginner
6822: Note:
6823: For all matrices it returns the range of matrix rows associated with rows of a vector that
6824: would contain the result of a matrix vector product with this matrix. See [Matrix
6825: Layouts](sec_matlayout) for details on matrix layouts.
6827: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`,
6828: `PetscLayout`
6829: @*/
6830: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6831: {
6832: PetscFunctionBegin;
6835: if (m) PetscAssertPointer(m, 2);
6836: if (n) PetscAssertPointer(n, 3);
6837: MatCheckPreallocated(mat, 1);
6838: if (m) *m = mat->rmap->rstart;
6839: if (n) *n = mat->rmap->rend;
6840: PetscFunctionReturn(PETSC_SUCCESS);
6841: }
6843: /*@C
6844: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6845: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6847: Not Collective, unless matrix has not been allocated
6849: Input Parameter:
6850: . mat - the matrix
6852: Output Parameter:
6853: . ranges - start of each processors portion plus one more than the total length at the end
6855: Level: beginner
6857: Note:
6858: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6859: would contain the result of a matrix vector product with this matrix. See [Matrix
6860: Layouts](sec_matlayout) for details on matrix layouts.
6862: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6863: @*/
6864: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt **ranges)
6865: {
6866: PetscFunctionBegin;
6869: MatCheckPreallocated(mat, 1);
6870: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6871: PetscFunctionReturn(PETSC_SUCCESS);
6872: }
6874: /*@C
6875: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6876: vector one multiplies this vector by that are owned by each processor.
6878: Not Collective, unless matrix has not been allocated
6880: Input Parameter:
6881: . mat - the matrix
6883: Output Parameter:
6884: . ranges - start of each processors portion plus one more than the total length at the end
6886: Level: beginner
6888: Note:
6889: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6890: Layouts](sec_matlayout) for details on matrix layouts.
6892: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`
6893: @*/
6894: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt **ranges)
6895: {
6896: PetscFunctionBegin;
6899: MatCheckPreallocated(mat, 1);
6900: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
6901: PetscFunctionReturn(PETSC_SUCCESS);
6902: }
6904: /*@C
6905: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
6907: Not Collective
6909: Input Parameter:
6910: . A - matrix
6912: Output Parameters:
6913: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
6914: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
6916: Level: intermediate
6918: Note:
6919: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
6920: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
6921: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
6922: details on matrix layouts.
6924: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatSetValues()`, ``MATELEMENTAL``, ``MATSCALAPACK``
6925: @*/
6926: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
6927: {
6928: PetscErrorCode (*f)(Mat, IS *, IS *);
6930: PetscFunctionBegin;
6931: MatCheckPreallocated(A, 1);
6932: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
6933: if (f) {
6934: PetscCall((*f)(A, rows, cols));
6935: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6936: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
6937: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
6938: }
6939: PetscFunctionReturn(PETSC_SUCCESS);
6940: }
6942: /*@C
6943: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
6944: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
6945: to complete the factorization.
6947: Collective
6949: Input Parameters:
6950: + fact - the factorized matrix obtained with `MatGetFactor()`
6951: . mat - the matrix
6952: . row - row permutation
6953: . col - column permutation
6954: - info - structure containing
6955: .vb
6956: levels - number of levels of fill.
6957: expected fill - as ratio of original fill.
6958: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6959: missing diagonal entries)
6960: .ve
6962: Level: developer
6964: Notes:
6965: See [Matrix Factorization](sec_matfactor) for additional information.
6967: Most users should employ the `KSP` interface for linear solvers
6968: instead of working directly with matrix algebra routines such as this.
6969: See, e.g., `KSPCreate()`.
6971: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
6973: Developer Note:
6974: The Fortran interface is not autogenerated as the
6975: interface definition cannot be generated correctly [due to `MatFactorInfo`]
6977: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
6978: `MatGetOrdering()`, `MatFactorInfo`
6979: @*/
6980: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
6981: {
6982: PetscFunctionBegin;
6987: PetscAssertPointer(info, 5);
6988: PetscAssertPointer(fact, 1);
6989: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
6990: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6991: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6992: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6993: MatCheckPreallocated(mat, 2);
6995: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
6996: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
6997: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
6998: PetscFunctionReturn(PETSC_SUCCESS);
6999: }
7001: /*@C
7002: MatICCFactorSymbolic - Performs symbolic incomplete
7003: Cholesky factorization for a symmetric matrix. Use
7004: `MatCholeskyFactorNumeric()` to complete the factorization.
7006: Collective
7008: Input Parameters:
7009: + fact - the factorized matrix obtained with `MatGetFactor()`
7010: . mat - the matrix to be factored
7011: . perm - row and column permutation
7012: - info - structure containing
7013: .vb
7014: levels - number of levels of fill.
7015: expected fill - as ratio of original fill.
7016: .ve
7018: Level: developer
7020: Notes:
7021: Most users should employ the `KSP` interface for linear solvers
7022: instead of working directly with matrix algebra routines such as this.
7023: See, e.g., `KSPCreate()`.
7025: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7027: Developer Note:
7028: The Fortran interface is not autogenerated as the
7029: interface definition cannot be generated correctly [due to `MatFactorInfo`]
7031: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7032: @*/
7033: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7034: {
7035: PetscFunctionBegin;
7039: PetscAssertPointer(info, 4);
7040: PetscAssertPointer(fact, 1);
7041: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7042: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7043: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7044: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7045: MatCheckPreallocated(mat, 2);
7047: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7048: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7049: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7050: PetscFunctionReturn(PETSC_SUCCESS);
7051: }
7053: /*@C
7054: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7055: points to an array of valid matrices, they may be reused to store the new
7056: submatrices.
7058: Collective
7060: Input Parameters:
7061: + mat - the matrix
7062: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7063: . irow - index set of rows to extract
7064: . icol - index set of columns to extract
7065: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7067: Output Parameter:
7068: . submat - the array of submatrices
7070: Level: advanced
7072: Notes:
7073: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7074: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7075: to extract a parallel submatrix.
7077: Some matrix types place restrictions on the row and column
7078: indices, such as that they be sorted or that they be equal to each other.
7080: The index sets may not have duplicate entries.
7082: When extracting submatrices from a parallel matrix, each processor can
7083: form a different submatrix by setting the rows and columns of its
7084: individual index sets according to the local submatrix desired.
7086: When finished using the submatrices, the user should destroy
7087: them with `MatDestroySubMatrices()`.
7089: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7090: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7092: This routine creates the matrices in submat; you should NOT create them before
7093: calling it. It also allocates the array of matrix pointers submat.
7095: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7096: request one row/column in a block, they must request all rows/columns that are in
7097: that block. For example, if the block size is 2 you cannot request just row 0 and
7098: column 0.
7100: Fortran Note:
7101: The Fortran interface is slightly different from that given below; it
7102: requires one to pass in as `submat` a `Mat` (integer) array of size at least n+1.
7104: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7105: @*/
7106: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7107: {
7108: PetscInt i;
7109: PetscBool eq;
7111: PetscFunctionBegin;
7114: if (n) {
7115: PetscAssertPointer(irow, 3);
7117: PetscAssertPointer(icol, 4);
7119: }
7120: PetscAssertPointer(submat, 6);
7121: if (n && scall == MAT_REUSE_MATRIX) {
7122: PetscAssertPointer(*submat, 6);
7124: }
7125: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7126: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7127: MatCheckPreallocated(mat, 1);
7128: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7129: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7130: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7131: for (i = 0; i < n; i++) {
7132: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7133: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7134: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7135: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7136: if (mat->boundtocpu && mat->bindingpropagates) {
7137: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7138: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7139: }
7140: #endif
7141: }
7142: PetscFunctionReturn(PETSC_SUCCESS);
7143: }
7145: /*@C
7146: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).
7148: Collective
7150: Input Parameters:
7151: + mat - the matrix
7152: . n - the number of submatrixes to be extracted
7153: . irow - index set of rows to extract
7154: . icol - index set of columns to extract
7155: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7157: Output Parameter:
7158: . submat - the array of submatrices
7160: Level: advanced
7162: Note:
7163: This is used by `PCGASM`
7165: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7166: @*/
7167: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7168: {
7169: PetscInt i;
7170: PetscBool eq;
7172: PetscFunctionBegin;
7175: if (n) {
7176: PetscAssertPointer(irow, 3);
7178: PetscAssertPointer(icol, 4);
7180: }
7181: PetscAssertPointer(submat, 6);
7182: if (n && scall == MAT_REUSE_MATRIX) {
7183: PetscAssertPointer(*submat, 6);
7185: }
7186: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7187: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7188: MatCheckPreallocated(mat, 1);
7190: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7191: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7192: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7193: for (i = 0; i < n; i++) {
7194: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7195: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7196: }
7197: PetscFunctionReturn(PETSC_SUCCESS);
7198: }
7200: /*@C
7201: MatDestroyMatrices - Destroys an array of matrices.
7203: Collective
7205: Input Parameters:
7206: + n - the number of local matrices
7207: - mat - the matrices (this is a pointer to the array of matrices)
7209: Level: advanced
7211: Note:
7212: Frees not only the matrices, but also the array that contains the matrices
7214: Fortran Note:
7215: This does not free the array.
7217: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()` `MatDestroySubMatrices()`
7218: @*/
7219: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7220: {
7221: PetscInt i;
7223: PetscFunctionBegin;
7224: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7225: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7226: PetscAssertPointer(mat, 2);
7228: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7230: /* memory is allocated even if n = 0 */
7231: PetscCall(PetscFree(*mat));
7232: PetscFunctionReturn(PETSC_SUCCESS);
7233: }
7235: /*@C
7236: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7238: Collective
7240: Input Parameters:
7241: + n - the number of local matrices
7242: - mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7243: sequence of `MatCreateSubMatrices()`)
7245: Level: advanced
7247: Note:
7248: Frees not only the matrices, but also the array that contains the matrices
7250: Fortran Note:
7251: This does not free the array.
7253: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7254: @*/
7255: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7256: {
7257: Mat mat0;
7259: PetscFunctionBegin;
7260: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7261: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7262: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7263: PetscAssertPointer(mat, 2);
7265: mat0 = (*mat)[0];
7266: if (mat0 && mat0->ops->destroysubmatrices) {
7267: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7268: } else {
7269: PetscCall(MatDestroyMatrices(n, mat));
7270: }
7271: PetscFunctionReturn(PETSC_SUCCESS);
7272: }
7274: /*@C
7275: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7277: Collective
7279: Input Parameter:
7280: . mat - the matrix
7282: Output Parameter:
7283: . matstruct - the sequential matrix with the nonzero structure of mat
7285: Level: developer
7287: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7288: @*/
7289: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7290: {
7291: PetscFunctionBegin;
7293: PetscAssertPointer(matstruct, 2);
7296: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7297: MatCheckPreallocated(mat, 1);
7299: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7300: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7301: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7302: PetscFunctionReturn(PETSC_SUCCESS);
7303: }
7305: /*@C
7306: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7308: Collective
7310: Input Parameter:
7311: . mat - the matrix (this is a pointer to the array of matrices, just to match the calling
7312: sequence of `MatGetSeqNonzeroStructure()`)
7314: Level: advanced
7316: Note:
7317: Frees not only the matrices, but also the array that contains the matrices
7319: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7320: @*/
7321: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7322: {
7323: PetscFunctionBegin;
7324: PetscAssertPointer(mat, 1);
7325: PetscCall(MatDestroy(mat));
7326: PetscFunctionReturn(PETSC_SUCCESS);
7327: }
7329: /*@
7330: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7331: replaces the index sets by larger ones that represent submatrices with
7332: additional overlap.
7334: Collective
7336: Input Parameters:
7337: + mat - the matrix
7338: . n - the number of index sets
7339: . is - the array of index sets (these index sets will changed during the call)
7340: - ov - the additional overlap requested
7342: Options Database Key:
7343: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7345: Level: developer
7347: Note:
7348: The computed overlap preserves the matrix block sizes when the blocks are square.
7349: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7350: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7352: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7353: @*/
7354: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7355: {
7356: PetscInt i, bs, cbs;
7358: PetscFunctionBegin;
7362: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7363: if (n) {
7364: PetscAssertPointer(is, 3);
7366: }
7367: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7368: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7369: MatCheckPreallocated(mat, 1);
7371: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7372: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7373: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7374: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7375: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7376: if (bs == cbs) {
7377: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7378: }
7379: PetscFunctionReturn(PETSC_SUCCESS);
7380: }
7382: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7384: /*@
7385: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7386: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7387: additional overlap.
7389: Collective
7391: Input Parameters:
7392: + mat - the matrix
7393: . n - the number of index sets
7394: . is - the array of index sets (these index sets will changed during the call)
7395: - ov - the additional overlap requested
7397: ` Options Database Key:
7398: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7400: Level: developer
7402: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7403: @*/
7404: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7405: {
7406: PetscInt i;
7408: PetscFunctionBegin;
7411: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7412: if (n) {
7413: PetscAssertPointer(is, 3);
7415: }
7416: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7417: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7418: MatCheckPreallocated(mat, 1);
7419: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7420: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7421: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7422: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7423: PetscFunctionReturn(PETSC_SUCCESS);
7424: }
7426: /*@
7427: MatGetBlockSize - Returns the matrix block size.
7429: Not Collective
7431: Input Parameter:
7432: . mat - the matrix
7434: Output Parameter:
7435: . bs - block size
7437: Level: intermediate
7439: Notes:
7440: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7442: If the block size has not been set yet this routine returns 1.
7444: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7445: @*/
7446: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7447: {
7448: PetscFunctionBegin;
7450: PetscAssertPointer(bs, 2);
7451: *bs = PetscAbs(mat->rmap->bs);
7452: PetscFunctionReturn(PETSC_SUCCESS);
7453: }
7455: /*@
7456: MatGetBlockSizes - Returns the matrix block row and column sizes.
7458: Not Collective
7460: Input Parameter:
7461: . mat - the matrix
7463: Output Parameters:
7464: + rbs - row block size
7465: - cbs - column block size
7467: Level: intermediate
7469: Notes:
7470: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7471: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7473: If a block size has not been set yet this routine returns 1.
7475: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7476: @*/
7477: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7478: {
7479: PetscFunctionBegin;
7481: if (rbs) PetscAssertPointer(rbs, 2);
7482: if (cbs) PetscAssertPointer(cbs, 3);
7483: if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7484: if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7485: PetscFunctionReturn(PETSC_SUCCESS);
7486: }
7488: /*@
7489: MatSetBlockSize - Sets the matrix block size.
7491: Logically Collective
7493: Input Parameters:
7494: + mat - the matrix
7495: - bs - block size
7497: Level: intermediate
7499: Notes:
7500: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7501: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7503: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7504: is compatible with the matrix local sizes.
7506: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7507: @*/
7508: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7509: {
7510: PetscFunctionBegin;
7513: PetscCall(MatSetBlockSizes(mat, bs, bs));
7514: PetscFunctionReturn(PETSC_SUCCESS);
7515: }
7517: typedef struct {
7518: PetscInt n;
7519: IS *is;
7520: Mat *mat;
7521: PetscObjectState nonzerostate;
7522: Mat C;
7523: } EnvelopeData;
7525: static PetscErrorCode EnvelopeDataDestroy(void *ptr)
7526: {
7527: EnvelopeData *edata = (EnvelopeData *)ptr;
7529: PetscFunctionBegin;
7530: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7531: PetscCall(PetscFree(edata->is));
7532: PetscCall(PetscFree(edata));
7533: PetscFunctionReturn(PETSC_SUCCESS);
7534: }
7536: /*@
7537: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7538: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7540: Collective
7542: Input Parameter:
7543: . mat - the matrix
7545: Level: intermediate
7547: Notes:
7548: There can be zeros within the blocks
7550: The blocks can overlap between processes, including laying on more than two processes
7552: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7553: @*/
7554: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7555: {
7556: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7557: PetscInt *diag, *odiag, sc;
7558: VecScatter scatter;
7559: PetscScalar *seqv;
7560: const PetscScalar *parv;
7561: const PetscInt *ia, *ja;
7562: PetscBool set, flag, done;
7563: Mat AA = mat, A;
7564: MPI_Comm comm;
7565: PetscMPIInt rank, size, tag;
7566: MPI_Status status;
7567: PetscContainer container;
7568: EnvelopeData *edata;
7569: Vec seq, par;
7570: IS isglobal;
7572: PetscFunctionBegin;
7574: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7575: if (!set || !flag) {
7576: /* TODO: only needs nonzero structure of transpose */
7577: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7578: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7579: }
7580: PetscCall(MatAIJGetLocalMat(AA, &A));
7581: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7582: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7584: PetscCall(MatGetLocalSize(mat, &n, NULL));
7585: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7586: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7587: PetscCallMPI(MPI_Comm_size(comm, &size));
7588: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7590: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7592: if (rank > 0) {
7593: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7594: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7595: }
7596: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7597: for (i = 0; i < n; i++) {
7598: env = PetscMax(env, ja[ia[i + 1] - 1]);
7599: II = rstart + i;
7600: if (env == II) {
7601: starts[lblocks] = tbs;
7602: sizes[lblocks++] = 1 + II - tbs;
7603: tbs = 1 + II;
7604: }
7605: }
7606: if (rank < size - 1) {
7607: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7608: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7609: }
7611: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7612: if (!set || !flag) PetscCall(MatDestroy(&AA));
7613: PetscCall(MatDestroy(&A));
7615: PetscCall(PetscNew(&edata));
7616: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7617: edata->n = lblocks;
7618: /* create IS needed for extracting blocks from the original matrix */
7619: PetscCall(PetscMalloc1(lblocks, &edata->is));
7620: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7622: /* Create the resulting inverse matrix structure with preallocation information */
7623: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7624: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7625: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7626: PetscCall(MatSetType(edata->C, MATAIJ));
7628: /* Communicate the start and end of each row, from each block to the correct rank */
7629: /* TODO: Use PetscSF instead of VecScatter */
7630: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7631: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7632: PetscCall(VecGetArrayWrite(seq, &seqv));
7633: for (PetscInt i = 0; i < lblocks; i++) {
7634: for (PetscInt j = 0; j < sizes[i]; j++) {
7635: seqv[cnt] = starts[i];
7636: seqv[cnt + 1] = starts[i] + sizes[i];
7637: cnt += 2;
7638: }
7639: }
7640: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7641: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7642: sc -= cnt;
7643: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7644: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7645: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7646: PetscCall(ISDestroy(&isglobal));
7647: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7648: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7649: PetscCall(VecScatterDestroy(&scatter));
7650: PetscCall(VecDestroy(&seq));
7651: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7652: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7653: PetscCall(VecGetArrayRead(par, &parv));
7654: cnt = 0;
7655: PetscCall(MatGetSize(mat, NULL, &n));
7656: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7657: PetscInt start, end, d = 0, od = 0;
7659: start = (PetscInt)PetscRealPart(parv[cnt]);
7660: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7661: cnt += 2;
7663: if (start < cstart) {
7664: od += cstart - start + n - cend;
7665: d += cend - cstart;
7666: } else if (start < cend) {
7667: od += n - cend;
7668: d += cend - start;
7669: } else od += n - start;
7670: if (end <= cstart) {
7671: od -= cstart - end + n - cend;
7672: d -= cend - cstart;
7673: } else if (end < cend) {
7674: od -= n - cend;
7675: d -= cend - end;
7676: } else od -= n - end;
7678: odiag[i] = od;
7679: diag[i] = d;
7680: }
7681: PetscCall(VecRestoreArrayRead(par, &parv));
7682: PetscCall(VecDestroy(&par));
7683: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7684: PetscCall(PetscFree2(diag, odiag));
7685: PetscCall(PetscFree2(sizes, starts));
7687: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7688: PetscCall(PetscContainerSetPointer(container, edata));
7689: PetscCall(PetscContainerSetUserDestroy(container, (PetscErrorCode(*)(void *))EnvelopeDataDestroy));
7690: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7691: PetscCall(PetscObjectDereference((PetscObject)container));
7692: PetscFunctionReturn(PETSC_SUCCESS);
7693: }
7695: /*@
7696: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7698: Collective
7700: Input Parameters:
7701: + A - the matrix
7702: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7704: Output Parameter:
7705: . C - matrix with inverted block diagonal of `A`
7707: Level: advanced
7709: Note:
7710: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7712: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7713: @*/
7714: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7715: {
7716: PetscContainer container;
7717: EnvelopeData *edata;
7718: PetscObjectState nonzerostate;
7720: PetscFunctionBegin;
7721: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7722: if (!container) {
7723: PetscCall(MatComputeVariableBlockEnvelope(A));
7724: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7725: }
7726: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7727: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7728: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7729: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7731: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7732: *C = edata->C;
7734: for (PetscInt i = 0; i < edata->n; i++) {
7735: Mat D;
7736: PetscScalar *dvalues;
7738: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7739: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7740: PetscCall(MatSeqDenseInvert(D));
7741: PetscCall(MatDenseGetArray(D, &dvalues));
7742: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7743: PetscCall(MatDestroy(&D));
7744: }
7745: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7746: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7747: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7748: PetscFunctionReturn(PETSC_SUCCESS);
7749: }
7751: /*@
7752: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7754: Logically Collective
7756: Input Parameters:
7757: + mat - the matrix
7758: . nblocks - the number of blocks on this process, each block can only exist on a single process
7759: - bsizes - the block sizes
7761: Level: intermediate
7763: Notes:
7764: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7766: 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.
7768: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7769: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7770: @*/
7771: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, PetscInt *bsizes)
7772: {
7773: PetscInt i, ncnt = 0, nlocal;
7775: PetscFunctionBegin;
7777: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7778: 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);
7779: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
7780: 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);
7781: PetscCall(PetscFree(mat->bsizes));
7782: mat->nblocks = nblocks;
7783: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7784: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7785: PetscFunctionReturn(PETSC_SUCCESS);
7786: }
7788: /*@C
7789: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7791: Logically Collective; No Fortran Support
7793: Input Parameter:
7794: . mat - the matrix
7796: Output Parameters:
7797: + nblocks - the number of blocks on this process
7798: - bsizes - the block sizes
7800: Level: intermediate
7802: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7803: @*/
7804: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt **bsizes)
7805: {
7806: PetscFunctionBegin;
7808: *nblocks = mat->nblocks;
7809: *bsizes = mat->bsizes;
7810: PetscFunctionReturn(PETSC_SUCCESS);
7811: }
7813: /*@
7814: MatSetBlockSizes - Sets the matrix block row and column sizes.
7816: Logically Collective
7818: Input Parameters:
7819: + mat - the matrix
7820: . rbs - row block size
7821: - cbs - column block size
7823: Level: intermediate
7825: Notes:
7826: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7827: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7828: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7830: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7831: are compatible with the matrix local sizes.
7833: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
7835: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7836: @*/
7837: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7838: {
7839: PetscFunctionBegin;
7843: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7844: if (mat->rmap->refcnt) {
7845: ISLocalToGlobalMapping l2g = NULL;
7846: PetscLayout nmap = NULL;
7848: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7849: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7850: PetscCall(PetscLayoutDestroy(&mat->rmap));
7851: mat->rmap = nmap;
7852: mat->rmap->mapping = l2g;
7853: }
7854: if (mat->cmap->refcnt) {
7855: ISLocalToGlobalMapping l2g = NULL;
7856: PetscLayout nmap = NULL;
7858: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7859: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7860: PetscCall(PetscLayoutDestroy(&mat->cmap));
7861: mat->cmap = nmap;
7862: mat->cmap->mapping = l2g;
7863: }
7864: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7865: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7866: PetscFunctionReturn(PETSC_SUCCESS);
7867: }
7869: /*@
7870: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
7872: Logically Collective
7874: Input Parameters:
7875: + mat - the matrix
7876: . fromRow - matrix from which to copy row block size
7877: - fromCol - matrix from which to copy column block size (can be same as fromRow)
7879: Level: developer
7881: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7882: @*/
7883: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7884: {
7885: PetscFunctionBegin;
7889: if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7890: if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7891: PetscFunctionReturn(PETSC_SUCCESS);
7892: }
7894: /*@
7895: MatResidual - Default routine to calculate the residual r = b - Ax
7897: Collective
7899: Input Parameters:
7900: + mat - the matrix
7901: . b - the right-hand-side
7902: - x - the approximate solution
7904: Output Parameter:
7905: . r - location to store the residual
7907: Level: developer
7909: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
7910: @*/
7911: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
7912: {
7913: PetscFunctionBegin;
7919: MatCheckPreallocated(mat, 1);
7920: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
7921: if (!mat->ops->residual) {
7922: PetscCall(MatMult(mat, x, r));
7923: PetscCall(VecAYPX(r, -1.0, b));
7924: } else {
7925: PetscUseTypeMethod(mat, residual, b, x, r);
7926: }
7927: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
7928: PetscFunctionReturn(PETSC_SUCCESS);
7929: }
7931: /*MC
7932: MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix
7934: Synopsis:
7935: MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)
7937: Not Collective
7939: Input Parameters:
7940: + A - the matrix
7941: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
7942: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7943: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
7944: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7945: always used.
7947: Output Parameters:
7948: + n - number of local rows in the (possibly compressed) matrix
7949: . ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7950: . ja - the column indices
7951: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7952: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
7954: Level: developer
7956: Note:
7957: Use `MatRestoreRowIJF90()` when you no longer need access to the data
7959: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
7960: M*/
7962: /*MC
7963: MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`
7965: Synopsis:
7966: MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)
7968: Not Collective
7970: Input Parameters:
7971: + A - the matrix
7972: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
7973: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7974: inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
7975: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7976: always used.
7977: . n - number of local rows in the (possibly compressed) matrix
7978: . ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7979: . ja - the column indices
7980: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7981: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
7983: Level: developer
7985: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
7986: M*/
7988: /*@C
7989: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
7991: Collective
7993: Input Parameters:
7994: + mat - the matrix
7995: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
7996: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7997: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
7998: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7999: always used.
8001: Output Parameters:
8002: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8003: . 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
8004: . ja - the column indices, use `NULL` if not needed
8005: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8006: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8008: Level: developer
8010: Notes:
8011: You CANNOT change any of the ia[] or ja[] values.
8013: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8015: Fortran Notes:
8016: Use
8017: .vb
8018: PetscInt, pointer :: ia(:),ja(:)
8019: call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8020: ! Access the ith and jth entries via ia(i) and ja(j)
8021: .ve
8023: `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`
8025: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8026: @*/
8027: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8028: {
8029: PetscFunctionBegin;
8032: if (n) PetscAssertPointer(n, 5);
8033: if (ia) PetscAssertPointer(ia, 6);
8034: if (ja) PetscAssertPointer(ja, 7);
8035: if (done) PetscAssertPointer(done, 8);
8036: MatCheckPreallocated(mat, 1);
8037: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8038: else {
8039: if (done) *done = PETSC_TRUE;
8040: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8041: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8042: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8043: }
8044: PetscFunctionReturn(PETSC_SUCCESS);
8045: }
8047: /*@C
8048: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8050: Collective
8052: Input Parameters:
8053: + mat - the matrix
8054: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8055: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8056: symmetrized
8057: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8058: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8059: always used.
8060: . n - number of columns in the (possibly compressed) matrix
8061: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8062: - ja - the row indices
8064: Output Parameter:
8065: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8067: Level: developer
8069: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8070: @*/
8071: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8072: {
8073: PetscFunctionBegin;
8076: PetscAssertPointer(n, 5);
8077: if (ia) PetscAssertPointer(ia, 6);
8078: if (ja) PetscAssertPointer(ja, 7);
8079: PetscAssertPointer(done, 8);
8080: MatCheckPreallocated(mat, 1);
8081: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8082: else {
8083: *done = PETSC_TRUE;
8084: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8085: }
8086: PetscFunctionReturn(PETSC_SUCCESS);
8087: }
8089: /*@C
8090: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8092: Collective
8094: Input Parameters:
8095: + mat - the matrix
8096: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8097: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8098: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8099: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8100: always used.
8101: . n - size of (possibly compressed) matrix
8102: . ia - the row pointers
8103: - ja - the column indices
8105: Output Parameter:
8106: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8108: Level: developer
8110: Note:
8111: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8112: us of the array after it has been restored. If you pass `NULL`, it will
8113: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8115: Fortran Note:
8116: `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`
8118: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8119: @*/
8120: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8121: {
8122: PetscFunctionBegin;
8125: if (ia) PetscAssertPointer(ia, 6);
8126: if (ja) PetscAssertPointer(ja, 7);
8127: if (done) PetscAssertPointer(done, 8);
8128: MatCheckPreallocated(mat, 1);
8130: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8131: else {
8132: if (done) *done = PETSC_TRUE;
8133: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8134: if (n) *n = 0;
8135: if (ia) *ia = NULL;
8136: if (ja) *ja = NULL;
8137: }
8138: PetscFunctionReturn(PETSC_SUCCESS);
8139: }
8141: /*@C
8142: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8144: Collective
8146: Input Parameters:
8147: + mat - the matrix
8148: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8149: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8150: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8151: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8152: always used.
8154: Output Parameters:
8155: + n - size of (possibly compressed) matrix
8156: . ia - the column pointers
8157: . ja - the row indices
8158: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8160: Level: developer
8162: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8163: @*/
8164: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8165: {
8166: PetscFunctionBegin;
8169: if (ia) PetscAssertPointer(ia, 6);
8170: if (ja) PetscAssertPointer(ja, 7);
8171: PetscAssertPointer(done, 8);
8172: MatCheckPreallocated(mat, 1);
8174: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8175: else {
8176: *done = PETSC_TRUE;
8177: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8178: if (n) *n = 0;
8179: if (ia) *ia = NULL;
8180: if (ja) *ja = NULL;
8181: }
8182: PetscFunctionReturn(PETSC_SUCCESS);
8183: }
8185: /*@C
8186: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8187: `MatGetColumnIJ()`.
8189: Collective
8191: Input Parameters:
8192: + mat - the matrix
8193: . ncolors - maximum color value
8194: . n - number of entries in colorarray
8195: - colorarray - array indicating color for each column
8197: Output Parameter:
8198: . iscoloring - coloring generated using colorarray information
8200: Level: developer
8202: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8203: @*/
8204: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8205: {
8206: PetscFunctionBegin;
8209: PetscAssertPointer(colorarray, 4);
8210: PetscAssertPointer(iscoloring, 5);
8211: MatCheckPreallocated(mat, 1);
8213: if (!mat->ops->coloringpatch) {
8214: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8215: } else {
8216: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8217: }
8218: PetscFunctionReturn(PETSC_SUCCESS);
8219: }
8221: /*@
8222: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8224: Logically Collective
8226: Input Parameter:
8227: . mat - the factored matrix to be reset
8229: Level: developer
8231: Notes:
8232: This routine should be used only with factored matrices formed by in-place
8233: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8234: format). This option can save memory, for example, when solving nonlinear
8235: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8236: ILU(0) preconditioner.
8238: One can specify in-place ILU(0) factorization by calling
8239: .vb
8240: PCType(pc,PCILU);
8241: PCFactorSeUseInPlace(pc);
8242: .ve
8243: or by using the options -pc_type ilu -pc_factor_in_place
8245: In-place factorization ILU(0) can also be used as a local
8246: solver for the blocks within the block Jacobi or additive Schwarz
8247: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8248: for details on setting local solver options.
8250: Most users should employ the `KSP` interface for linear solvers
8251: instead of working directly with matrix algebra routines such as this.
8252: See, e.g., `KSPCreate()`.
8254: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8255: @*/
8256: PetscErrorCode MatSetUnfactored(Mat mat)
8257: {
8258: PetscFunctionBegin;
8261: MatCheckPreallocated(mat, 1);
8262: mat->factortype = MAT_FACTOR_NONE;
8263: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8264: PetscUseTypeMethod(mat, setunfactored);
8265: PetscFunctionReturn(PETSC_SUCCESS);
8266: }
8268: /*MC
8269: MatDenseGetArrayF90 - Accesses a matrix array from Fortran
8271: Synopsis:
8272: MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)
8274: Not Collective
8276: Input Parameter:
8277: . x - matrix
8279: Output Parameters:
8280: + xx_v - the Fortran pointer to the array
8281: - ierr - error code
8283: Example of Usage:
8284: .vb
8285: PetscScalar, pointer xx_v(:,:)
8286: ....
8287: call MatDenseGetArrayF90(x,xx_v,ierr)
8288: a = xx_v(3)
8289: call MatDenseRestoreArrayF90(x,xx_v,ierr)
8290: .ve
8292: Level: advanced
8294: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8295: M*/
8297: /*MC
8298: MatDenseRestoreArrayF90 - Restores a matrix array that has been
8299: accessed with `MatDenseGetArrayF90()`.
8301: Synopsis:
8302: MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)
8304: Not Collective
8306: Input Parameters:
8307: + x - matrix
8308: - xx_v - the Fortran90 pointer to the array
8310: Output Parameter:
8311: . ierr - error code
8313: Example of Usage:
8314: .vb
8315: PetscScalar, pointer xx_v(:,:)
8316: ....
8317: call MatDenseGetArrayF90(x,xx_v,ierr)
8318: a = xx_v(3)
8319: call MatDenseRestoreArrayF90(x,xx_v,ierr)
8320: .ve
8322: Level: advanced
8324: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8325: M*/
8327: /*MC
8328: MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.
8330: Synopsis:
8331: MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)
8333: Not Collective
8335: Input Parameter:
8336: . x - matrix
8338: Output Parameters:
8339: + xx_v - the Fortran pointer to the array
8340: - ierr - error code
8342: Example of Usage:
8343: .vb
8344: PetscScalar, pointer xx_v(:)
8345: ....
8346: call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8347: a = xx_v(3)
8348: call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8349: .ve
8351: Level: advanced
8353: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8354: M*/
8356: /*MC
8357: MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8358: accessed with `MatSeqAIJGetArrayF90()`.
8360: Synopsis:
8361: MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)
8363: Not Collective
8365: Input Parameters:
8366: + x - matrix
8367: - xx_v - the Fortran90 pointer to the array
8369: Output Parameter:
8370: . ierr - error code
8372: Example of Usage:
8373: .vb
8374: PetscScalar, pointer xx_v(:)
8375: ....
8376: call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8377: a = xx_v(3)
8378: call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8379: .ve
8381: Level: advanced
8383: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8384: M*/
8386: /*@
8387: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8388: as the original matrix.
8390: Collective
8392: Input Parameters:
8393: + mat - the original matrix
8394: . isrow - parallel `IS` containing the rows this processor should obtain
8395: . 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.
8396: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8398: Output Parameter:
8399: . newmat - the new submatrix, of the same type as the original matrix
8401: Level: advanced
8403: Notes:
8404: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8406: Some matrix types place restrictions on the row and column indices, such
8407: 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;
8408: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8410: The index sets may not have duplicate entries.
8412: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8413: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8414: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8415: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8416: you are finished using it.
8418: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8419: the input matrix.
8421: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8423: If `isrow` and `iscol` have a nontrivial block-size then the resulting matrix has this block-size as well. This feature
8424: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8426: Example usage:
8427: Consider the following 8x8 matrix with 34 non-zero values, that is
8428: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8429: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8430: as follows
8431: .vb
8432: 1 2 0 | 0 3 0 | 0 4
8433: Proc0 0 5 6 | 7 0 0 | 8 0
8434: 9 0 10 | 11 0 0 | 12 0
8435: -------------------------------------
8436: 13 0 14 | 15 16 17 | 0 0
8437: Proc1 0 18 0 | 19 20 21 | 0 0
8438: 0 0 0 | 22 23 0 | 24 0
8439: -------------------------------------
8440: Proc2 25 26 27 | 0 0 28 | 29 0
8441: 30 0 0 | 31 32 33 | 0 34
8442: .ve
8444: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8446: .vb
8447: 2 0 | 0 3 0 | 0
8448: Proc0 5 6 | 7 0 0 | 8
8449: -------------------------------
8450: Proc1 18 0 | 19 20 21 | 0
8451: -------------------------------
8452: Proc2 26 27 | 0 0 28 | 29
8453: 0 0 | 31 32 33 | 0
8454: .ve
8456: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8457: @*/
8458: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8459: {
8460: PetscMPIInt size;
8461: Mat *local;
8462: IS iscoltmp;
8463: PetscBool flg;
8465: PetscFunctionBegin;
8469: PetscAssertPointer(newmat, 5);
8472: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8473: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8475: MatCheckPreallocated(mat, 1);
8476: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8478: if (!iscol || isrow == iscol) {
8479: PetscBool stride;
8480: PetscMPIInt grabentirematrix = 0, grab;
8481: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8482: if (stride) {
8483: PetscInt first, step, n, rstart, rend;
8484: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8485: if (step == 1) {
8486: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8487: if (rstart == first) {
8488: PetscCall(ISGetLocalSize(isrow, &n));
8489: if (n == rend - rstart) grabentirematrix = 1;
8490: }
8491: }
8492: }
8493: PetscCall(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8494: if (grab) {
8495: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8496: if (cll == MAT_INITIAL_MATRIX) {
8497: *newmat = mat;
8498: PetscCall(PetscObjectReference((PetscObject)mat));
8499: }
8500: PetscFunctionReturn(PETSC_SUCCESS);
8501: }
8502: }
8504: if (!iscol) {
8505: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8506: } else {
8507: iscoltmp = iscol;
8508: }
8510: /* if original matrix is on just one processor then use submatrix generated */
8511: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8512: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8513: goto setproperties;
8514: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8515: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8516: *newmat = *local;
8517: PetscCall(PetscFree(local));
8518: goto setproperties;
8519: } else if (!mat->ops->createsubmatrix) {
8520: /* Create a new matrix type that implements the operation using the full matrix */
8521: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8522: switch (cll) {
8523: case MAT_INITIAL_MATRIX:
8524: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8525: break;
8526: case MAT_REUSE_MATRIX:
8527: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8528: break;
8529: default:
8530: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8531: }
8532: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8533: goto setproperties;
8534: }
8536: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8537: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8538: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8540: setproperties:
8541: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8542: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8543: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8544: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8545: PetscFunctionReturn(PETSC_SUCCESS);
8546: }
8548: /*@
8549: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8551: Not Collective
8553: Input Parameters:
8554: + A - the matrix we wish to propagate options from
8555: - B - the matrix we wish to propagate options to
8557: Level: beginner
8559: Note:
8560: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8562: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8563: @*/
8564: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8565: {
8566: PetscFunctionBegin;
8569: B->symmetry_eternal = A->symmetry_eternal;
8570: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8571: B->symmetric = A->symmetric;
8572: B->structurally_symmetric = A->structurally_symmetric;
8573: B->spd = A->spd;
8574: B->hermitian = A->hermitian;
8575: PetscFunctionReturn(PETSC_SUCCESS);
8576: }
8578: /*@
8579: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8580: used during the assembly process to store values that belong to
8581: other processors.
8583: Not Collective
8585: Input Parameters:
8586: + mat - the matrix
8587: . size - the initial size of the stash.
8588: - bsize - the initial size of the block-stash(if used).
8590: Options Database Keys:
8591: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8592: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8594: Level: intermediate
8596: Notes:
8597: The block-stash is used for values set with `MatSetValuesBlocked()` while
8598: the stash is used for values set with `MatSetValues()`
8600: Run with the option -info and look for output of the form
8601: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8602: to determine the appropriate value, MM, to use for size and
8603: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8604: to determine the value, BMM to use for bsize
8606: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8607: @*/
8608: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8609: {
8610: PetscFunctionBegin;
8613: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8614: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8615: PetscFunctionReturn(PETSC_SUCCESS);
8616: }
8618: /*@
8619: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8620: the matrix
8622: Neighbor-wise Collective
8624: Input Parameters:
8625: + A - the matrix
8626: . x - the vector to be multiplied by the interpolation operator
8627: - y - the vector to be added to the result
8629: Output Parameter:
8630: . w - the resulting vector
8632: Level: intermediate
8634: Notes:
8635: `w` may be the same vector as `y`.
8637: This allows one to use either the restriction or interpolation (its transpose)
8638: matrix to do the interpolation
8640: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8641: @*/
8642: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8643: {
8644: PetscInt M, N, Ny;
8646: PetscFunctionBegin;
8651: PetscCall(MatGetSize(A, &M, &N));
8652: PetscCall(VecGetSize(y, &Ny));
8653: if (M == Ny) {
8654: PetscCall(MatMultAdd(A, x, y, w));
8655: } else {
8656: PetscCall(MatMultTransposeAdd(A, x, y, w));
8657: }
8658: PetscFunctionReturn(PETSC_SUCCESS);
8659: }
8661: /*@
8662: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8663: the matrix
8665: Neighbor-wise Collective
8667: Input Parameters:
8668: + A - the matrix
8669: - x - the vector to be interpolated
8671: Output Parameter:
8672: . y - the resulting vector
8674: Level: intermediate
8676: Note:
8677: This allows one to use either the restriction or interpolation (its transpose)
8678: matrix to do the interpolation
8680: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8681: @*/
8682: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8683: {
8684: PetscInt M, N, Ny;
8686: PetscFunctionBegin;
8690: PetscCall(MatGetSize(A, &M, &N));
8691: PetscCall(VecGetSize(y, &Ny));
8692: if (M == Ny) {
8693: PetscCall(MatMult(A, x, y));
8694: } else {
8695: PetscCall(MatMultTranspose(A, x, y));
8696: }
8697: PetscFunctionReturn(PETSC_SUCCESS);
8698: }
8700: /*@
8701: MatRestrict - $y = A*x$ or $A^T*x$
8703: Neighbor-wise Collective
8705: Input Parameters:
8706: + A - the matrix
8707: - x - the vector to be restricted
8709: Output Parameter:
8710: . y - the resulting vector
8712: Level: intermediate
8714: Note:
8715: This allows one to use either the restriction or interpolation (its transpose)
8716: matrix to do the restriction
8718: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8719: @*/
8720: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8721: {
8722: PetscInt M, N, Nx;
8724: PetscFunctionBegin;
8728: PetscCall(MatGetSize(A, &M, &N));
8729: PetscCall(VecGetSize(x, &Nx));
8730: if (M == Nx) {
8731: PetscCall(MatMultTranspose(A, x, y));
8732: } else {
8733: PetscCall(MatMult(A, x, y));
8734: }
8735: PetscFunctionReturn(PETSC_SUCCESS);
8736: }
8738: /*@
8739: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8741: Neighbor-wise Collective
8743: Input Parameters:
8744: + A - the matrix
8745: . x - the input dense matrix to be multiplied
8746: - w - the input dense matrix to be added to the result
8748: Output Parameter:
8749: . y - the output dense matrix
8751: Level: intermediate
8753: Note:
8754: This allows one to use either the restriction or interpolation (its transpose)
8755: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8756: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8758: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8759: @*/
8760: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8761: {
8762: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8763: PetscBool trans = PETSC_TRUE;
8764: MatReuse reuse = MAT_INITIAL_MATRIX;
8766: PetscFunctionBegin;
8772: PetscCall(MatGetSize(A, &M, &N));
8773: PetscCall(MatGetSize(x, &Mx, &Nx));
8774: if (N == Mx) trans = PETSC_FALSE;
8775: 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);
8776: Mo = trans ? N : M;
8777: if (*y) {
8778: PetscCall(MatGetSize(*y, &My, &Ny));
8779: if (Mo == My && Nx == Ny) {
8780: reuse = MAT_REUSE_MATRIX;
8781: } else {
8782: 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);
8783: PetscCall(MatDestroy(y));
8784: }
8785: }
8787: if (w && *y == w) { /* this is to minimize changes in PCMG */
8788: PetscBool flg;
8790: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8791: if (w) {
8792: PetscInt My, Ny, Mw, Nw;
8794: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8795: PetscCall(MatGetSize(*y, &My, &Ny));
8796: PetscCall(MatGetSize(w, &Mw, &Nw));
8797: if (!flg || My != Mw || Ny != Nw) w = NULL;
8798: }
8799: if (!w) {
8800: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8801: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8802: PetscCall(PetscObjectDereference((PetscObject)w));
8803: } else {
8804: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8805: }
8806: }
8807: if (!trans) {
8808: PetscCall(MatMatMult(A, x, reuse, PETSC_DEFAULT, y));
8809: } else {
8810: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DEFAULT, y));
8811: }
8812: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8813: PetscFunctionReturn(PETSC_SUCCESS);
8814: }
8816: /*@
8817: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8819: Neighbor-wise Collective
8821: Input Parameters:
8822: + A - the matrix
8823: - x - the input dense matrix
8825: Output Parameter:
8826: . y - the output dense matrix
8828: Level: intermediate
8830: Note:
8831: This allows one to use either the restriction or interpolation (its transpose)
8832: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8833: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8835: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8836: @*/
8837: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8838: {
8839: PetscFunctionBegin;
8840: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8841: PetscFunctionReturn(PETSC_SUCCESS);
8842: }
8844: /*@
8845: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8847: Neighbor-wise Collective
8849: Input Parameters:
8850: + A - the matrix
8851: - x - the input dense matrix
8853: Output Parameter:
8854: . y - the output dense matrix
8856: Level: intermediate
8858: Note:
8859: This allows one to use either the restriction or interpolation (its transpose)
8860: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8861: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8863: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8864: @*/
8865: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8866: {
8867: PetscFunctionBegin;
8868: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8869: PetscFunctionReturn(PETSC_SUCCESS);
8870: }
8872: /*@
8873: MatGetNullSpace - retrieves the null space of a matrix.
8875: Logically Collective
8877: Input Parameters:
8878: + mat - the matrix
8879: - nullsp - the null space object
8881: Level: developer
8883: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8884: @*/
8885: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8886: {
8887: PetscFunctionBegin;
8889: PetscAssertPointer(nullsp, 2);
8890: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8891: PetscFunctionReturn(PETSC_SUCCESS);
8892: }
8894: /*@C
8895: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8897: Logically Collective
8899: Input Parameters:
8900: + n - the number of matrices
8901: - mat - the array of matrices
8903: Output Parameters:
8904: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space
8906: Level: developer
8908: Note:
8909: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8911: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8912: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8913: @*/
8914: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8915: {
8916: PetscFunctionBegin;
8917: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8918: PetscAssertPointer(mat, 2);
8919: PetscAssertPointer(nullsp, 3);
8921: PetscCall(PetscCalloc1(3 * n, nullsp));
8922: for (PetscInt i = 0; i < n; i++) {
8924: (*nullsp)[i] = mat[i]->nullsp;
8925: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8926: (*nullsp)[n + i] = mat[i]->nearnullsp;
8927: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8928: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8929: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8930: }
8931: PetscFunctionReturn(PETSC_SUCCESS);
8932: }
8934: /*@C
8935: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8937: Logically Collective
8939: Input Parameters:
8940: + n - the number of matrices
8941: . mat - the array of matrices
8942: - nullsp - an array of null spaces, `NULL` if the null space does not exist
8944: Level: developer
8946: Note:
8947: Call `MatGetNullSpaces()` to create `nullsp`
8949: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8950: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8951: @*/
8952: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8953: {
8954: PetscFunctionBegin;
8955: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8956: PetscAssertPointer(mat, 2);
8957: PetscAssertPointer(nullsp, 3);
8958: PetscAssertPointer(*nullsp, 3);
8960: for (PetscInt i = 0; i < n; i++) {
8962: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8963: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8964: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8965: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8966: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8967: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8968: }
8969: PetscCall(PetscFree(*nullsp));
8970: PetscFunctionReturn(PETSC_SUCCESS);
8971: }
8973: /*@
8974: MatSetNullSpace - attaches a null space to a matrix.
8976: Logically Collective
8978: Input Parameters:
8979: + mat - the matrix
8980: - nullsp - the null space object
8982: Level: advanced
8984: Notes:
8985: This null space is used by the `KSP` linear solvers to solve singular systems.
8987: 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`
8989: 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 to
8990: to zero but the linear system will still be solved in a least squares sense.
8992: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8993: the domain of a matrix A (from $R^n$ to $R^m$ (m rows, n columns) $R^n$ = the direct sum of the null space of A, n(A), + the range of $A^T$, $R(A^T)$.
8994: 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
8995: 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
8996: 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($A^T$).
8997: This \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
8999: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
9000: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9001: routine also automatically calls `MatSetTransposeNullSpace()`.
9003: The user should call `MatNullSpaceDestroy()`.
9005: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9006: `KSPSetPCSide()`
9007: @*/
9008: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9009: {
9010: PetscFunctionBegin;
9013: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9014: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9015: mat->nullsp = nullsp;
9016: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9017: PetscFunctionReturn(PETSC_SUCCESS);
9018: }
9020: /*@
9021: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9023: Logically Collective
9025: Input Parameters:
9026: + mat - the matrix
9027: - nullsp - the null space object
9029: Level: developer
9031: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9032: @*/
9033: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9034: {
9035: PetscFunctionBegin;
9038: PetscAssertPointer(nullsp, 2);
9039: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9040: PetscFunctionReturn(PETSC_SUCCESS);
9041: }
9043: /*@
9044: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9046: Logically Collective
9048: Input Parameters:
9049: + mat - the matrix
9050: - nullsp - the null space object
9052: Level: advanced
9054: Notes:
9055: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9057: See `MatSetNullSpace()`
9059: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9060: @*/
9061: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9062: {
9063: PetscFunctionBegin;
9066: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9067: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9068: mat->transnullsp = nullsp;
9069: PetscFunctionReturn(PETSC_SUCCESS);
9070: }
9072: /*@
9073: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9074: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9076: Logically Collective
9078: Input Parameters:
9079: + mat - the matrix
9080: - nullsp - the null space object
9082: Level: advanced
9084: Notes:
9085: Overwrites any previous near null space that may have been attached
9087: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9089: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9090: @*/
9091: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9092: {
9093: PetscFunctionBegin;
9097: MatCheckPreallocated(mat, 1);
9098: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9099: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9100: mat->nearnullsp = nullsp;
9101: PetscFunctionReturn(PETSC_SUCCESS);
9102: }
9104: /*@
9105: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9107: Not Collective
9109: Input Parameter:
9110: . mat - the matrix
9112: Output Parameter:
9113: . nullsp - the null space object, `NULL` if not set
9115: Level: advanced
9117: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9118: @*/
9119: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9120: {
9121: PetscFunctionBegin;
9124: PetscAssertPointer(nullsp, 2);
9125: MatCheckPreallocated(mat, 1);
9126: *nullsp = mat->nearnullsp;
9127: PetscFunctionReturn(PETSC_SUCCESS);
9128: }
9130: /*@C
9131: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9133: Collective
9135: Input Parameters:
9136: + mat - the matrix
9137: . row - row/column permutation
9138: - info - information on desired factorization process
9140: Level: developer
9142: Notes:
9143: Probably really in-place only when level of fill is zero, otherwise allocates
9144: new space to store factored matrix and deletes previous memory.
9146: Most users should employ the `KSP` interface for linear solvers
9147: instead of working directly with matrix algebra routines such as this.
9148: See, e.g., `KSPCreate()`.
9150: Developer Note:
9151: The Fortran interface is not autogenerated as the
9152: interface definition cannot be generated correctly [due to `MatFactorInfo`]
9154: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9155: @*/
9156: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9157: {
9158: PetscFunctionBegin;
9162: PetscAssertPointer(info, 3);
9163: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9164: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9165: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9166: MatCheckPreallocated(mat, 1);
9167: PetscUseTypeMethod(mat, iccfactor, row, info);
9168: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9169: PetscFunctionReturn(PETSC_SUCCESS);
9170: }
9172: /*@
9173: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9174: ghosted ones.
9176: Not Collective
9178: Input Parameters:
9179: + mat - the matrix
9180: - diag - the diagonal values, including ghost ones
9182: Level: developer
9184: Notes:
9185: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9187: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9189: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9190: @*/
9191: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9192: {
9193: PetscMPIInt size;
9195: PetscFunctionBegin;
9200: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9201: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9202: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9203: if (size == 1) {
9204: PetscInt n, m;
9205: PetscCall(VecGetSize(diag, &n));
9206: PetscCall(MatGetSize(mat, NULL, &m));
9207: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9208: PetscCall(MatDiagonalScale(mat, NULL, diag));
9209: } else {
9210: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9211: }
9212: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9213: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9214: PetscFunctionReturn(PETSC_SUCCESS);
9215: }
9217: /*@
9218: MatGetInertia - Gets the inertia from a factored matrix
9220: Collective
9222: Input Parameter:
9223: . mat - the matrix
9225: Output Parameters:
9226: + nneg - number of negative eigenvalues
9227: . nzero - number of zero eigenvalues
9228: - npos - number of positive eigenvalues
9230: Level: advanced
9232: Note:
9233: Matrix must have been factored by `MatCholeskyFactor()`
9235: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9236: @*/
9237: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9238: {
9239: PetscFunctionBegin;
9242: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9243: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9244: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9245: PetscFunctionReturn(PETSC_SUCCESS);
9246: }
9248: /*@C
9249: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9251: Neighbor-wise Collective
9253: Input Parameters:
9254: + mat - the factored matrix obtained with `MatGetFactor()`
9255: - b - the right-hand-side vectors
9257: Output Parameter:
9258: . x - the result vectors
9260: Level: developer
9262: Note:
9263: The vectors `b` and `x` cannot be the same. I.e., one cannot
9264: call `MatSolves`(A,x,x).
9266: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9267: @*/
9268: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9269: {
9270: PetscFunctionBegin;
9273: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9274: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9275: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9277: MatCheckPreallocated(mat, 1);
9278: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9279: PetscUseTypeMethod(mat, solves, b, x);
9280: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9281: PetscFunctionReturn(PETSC_SUCCESS);
9282: }
9284: /*@
9285: MatIsSymmetric - Test whether a matrix is symmetric
9287: Collective
9289: Input Parameters:
9290: + A - the matrix to test
9291: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9293: Output Parameter:
9294: . flg - the result
9296: Level: intermediate
9298: Notes:
9299: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9301: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9303: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9304: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9306: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9307: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9308: @*/
9309: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9310: {
9311: PetscFunctionBegin;
9313: PetscAssertPointer(flg, 3);
9314: if (A->symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->symmetric);
9315: else {
9316: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9317: else PetscCall(MatIsTranspose(A, A, tol, flg));
9318: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9319: }
9320: PetscFunctionReturn(PETSC_SUCCESS);
9321: }
9323: /*@
9324: MatIsHermitian - Test whether a matrix is Hermitian
9326: Collective
9328: Input Parameters:
9329: + A - the matrix to test
9330: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9332: Output Parameter:
9333: . flg - the result
9335: Level: intermediate
9337: Notes:
9338: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9340: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9342: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9343: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9345: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9346: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9347: @*/
9348: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9349: {
9350: PetscFunctionBegin;
9352: PetscAssertPointer(flg, 3);
9353: if (A->hermitian != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->hermitian);
9354: else {
9355: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9356: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9357: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9358: }
9359: PetscFunctionReturn(PETSC_SUCCESS);
9360: }
9362: /*@
9363: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9365: Not Collective
9367: Input Parameter:
9368: . A - the matrix to check
9370: Output Parameters:
9371: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9372: - flg - the result (only valid if set is `PETSC_TRUE`)
9374: Level: advanced
9376: Notes:
9377: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9378: if you want it explicitly checked
9380: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9381: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9383: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9384: @*/
9385: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9386: {
9387: PetscFunctionBegin;
9389: PetscAssertPointer(set, 2);
9390: PetscAssertPointer(flg, 3);
9391: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9392: *set = PETSC_TRUE;
9393: *flg = PetscBool3ToBool(A->symmetric);
9394: } else {
9395: *set = PETSC_FALSE;
9396: }
9397: PetscFunctionReturn(PETSC_SUCCESS);
9398: }
9400: /*@
9401: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9403: Not Collective
9405: Input Parameter:
9406: . A - the matrix to check
9408: Output Parameters:
9409: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9410: - flg - the result (only valid if set is `PETSC_TRUE`)
9412: Level: advanced
9414: Notes:
9415: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9417: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9418: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9420: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9421: @*/
9422: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9423: {
9424: PetscFunctionBegin;
9426: PetscAssertPointer(set, 2);
9427: PetscAssertPointer(flg, 3);
9428: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9429: *set = PETSC_TRUE;
9430: *flg = PetscBool3ToBool(A->spd);
9431: } else {
9432: *set = PETSC_FALSE;
9433: }
9434: PetscFunctionReturn(PETSC_SUCCESS);
9435: }
9437: /*@
9438: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9440: Not Collective
9442: Input Parameter:
9443: . A - the matrix to check
9445: Output Parameters:
9446: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9447: - flg - the result (only valid if set is `PETSC_TRUE`)
9449: Level: advanced
9451: Notes:
9452: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9453: if you want it explicitly checked
9455: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9456: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9458: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9459: @*/
9460: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9461: {
9462: PetscFunctionBegin;
9464: PetscAssertPointer(set, 2);
9465: PetscAssertPointer(flg, 3);
9466: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9467: *set = PETSC_TRUE;
9468: *flg = PetscBool3ToBool(A->hermitian);
9469: } else {
9470: *set = PETSC_FALSE;
9471: }
9472: PetscFunctionReturn(PETSC_SUCCESS);
9473: }
9475: /*@
9476: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9478: Collective
9480: Input Parameter:
9481: . A - the matrix to test
9483: Output Parameter:
9484: . flg - the result
9486: Level: intermediate
9488: Notes:
9489: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9491: 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
9492: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9494: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9495: @*/
9496: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9497: {
9498: PetscFunctionBegin;
9500: PetscAssertPointer(flg, 2);
9501: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9502: *flg = PetscBool3ToBool(A->structurally_symmetric);
9503: } else {
9504: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9505: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9506: }
9507: PetscFunctionReturn(PETSC_SUCCESS);
9508: }
9510: /*@
9511: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9513: Not Collective
9515: Input Parameter:
9516: . A - the matrix to check
9518: Output Parameters:
9519: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9520: - flg - the result (only valid if set is PETSC_TRUE)
9522: Level: advanced
9524: Notes:
9525: 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
9526: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9528: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9530: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9531: @*/
9532: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9533: {
9534: PetscFunctionBegin;
9536: PetscAssertPointer(set, 2);
9537: PetscAssertPointer(flg, 3);
9538: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9539: *set = PETSC_TRUE;
9540: *flg = PetscBool3ToBool(A->structurally_symmetric);
9541: } else {
9542: *set = PETSC_FALSE;
9543: }
9544: PetscFunctionReturn(PETSC_SUCCESS);
9545: }
9547: /*@
9548: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9549: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9551: Not Collective
9553: Input Parameter:
9554: . mat - the matrix
9556: Output Parameters:
9557: + nstash - the size of the stash
9558: . reallocs - the number of additional mallocs incurred.
9559: . bnstash - the size of the block stash
9560: - breallocs - the number of additional mallocs incurred.in the block stash
9562: Level: advanced
9564: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9565: @*/
9566: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9567: {
9568: PetscFunctionBegin;
9569: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9570: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9571: PetscFunctionReturn(PETSC_SUCCESS);
9572: }
9574: /*@C
9575: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9576: parallel layout, `PetscLayout` for rows and columns
9578: Collective
9580: Input Parameter:
9581: . mat - the matrix
9583: Output Parameters:
9584: + right - (optional) vector that the matrix can be multiplied against
9585: - left - (optional) vector that the matrix vector product can be stored in
9587: Level: advanced
9589: Notes:
9590: 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()`.
9592: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9594: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9595: @*/
9596: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9597: {
9598: PetscFunctionBegin;
9601: if (mat->ops->getvecs) {
9602: PetscUseTypeMethod(mat, getvecs, right, left);
9603: } else {
9604: if (right) {
9605: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9606: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9607: PetscCall(VecSetType(*right, mat->defaultvectype));
9608: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9609: if (mat->boundtocpu && mat->bindingpropagates) {
9610: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9611: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9612: }
9613: #endif
9614: }
9615: if (left) {
9616: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9617: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9618: PetscCall(VecSetType(*left, mat->defaultvectype));
9619: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9620: if (mat->boundtocpu && mat->bindingpropagates) {
9621: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9622: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9623: }
9624: #endif
9625: }
9626: }
9627: PetscFunctionReturn(PETSC_SUCCESS);
9628: }
9630: /*@C
9631: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9632: with default values.
9634: Not Collective
9636: Input Parameter:
9637: . info - the `MatFactorInfo` data structure
9639: Level: developer
9641: Notes:
9642: The solvers are generally used through the `KSP` and `PC` objects, for example
9643: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9645: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9647: Developer Note:
9648: The Fortran interface is not autogenerated as the
9649: interface definition cannot be generated correctly [due to `MatFactorInfo`]
9651: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9652: @*/
9653: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9654: {
9655: PetscFunctionBegin;
9656: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9657: PetscFunctionReturn(PETSC_SUCCESS);
9658: }
9660: /*@
9661: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9663: Collective
9665: Input Parameters:
9666: + mat - the factored matrix
9667: - is - the index set defining the Schur indices (0-based)
9669: Level: advanced
9671: Notes:
9672: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9674: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9676: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9678: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9679: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9680: @*/
9681: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9682: {
9683: PetscErrorCode (*f)(Mat, IS);
9685: PetscFunctionBegin;
9690: PetscCheckSameComm(mat, 1, is, 2);
9691: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9692: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9693: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9694: PetscCall(MatDestroy(&mat->schur));
9695: PetscCall((*f)(mat, is));
9696: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9697: PetscFunctionReturn(PETSC_SUCCESS);
9698: }
9700: /*@
9701: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9703: Logically Collective
9705: Input Parameters:
9706: + F - the factored matrix obtained by calling `MatGetFactor()`
9707: . S - location where to return the Schur complement, can be `NULL`
9708: - status - the status of the Schur complement matrix, can be `NULL`
9710: Level: advanced
9712: Notes:
9713: You must call `MatFactorSetSchurIS()` before calling this routine.
9715: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9717: The routine provides a copy of the Schur matrix stored within the solver data structures.
9718: The caller must destroy the object when it is no longer needed.
9719: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9721: 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)
9723: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9725: Developer Note:
9726: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9727: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9729: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9730: @*/
9731: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9732: {
9733: PetscFunctionBegin;
9735: if (S) PetscAssertPointer(S, 2);
9736: if (status) PetscAssertPointer(status, 3);
9737: if (S) {
9738: PetscErrorCode (*f)(Mat, Mat *);
9740: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9741: if (f) {
9742: PetscCall((*f)(F, S));
9743: } else {
9744: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9745: }
9746: }
9747: if (status) *status = F->schur_status;
9748: PetscFunctionReturn(PETSC_SUCCESS);
9749: }
9751: /*@
9752: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9754: Logically Collective
9756: Input Parameters:
9757: + F - the factored matrix obtained by calling `MatGetFactor()`
9758: . S - location where to return the Schur complement, can be `NULL`
9759: - status - the status of the Schur complement matrix, can be `NULL`
9761: Level: advanced
9763: Notes:
9764: You must call `MatFactorSetSchurIS()` before calling this routine.
9766: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9768: The routine returns a the Schur Complement stored within the data structures of the solver.
9770: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9772: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9774: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9776: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9778: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9779: @*/
9780: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9781: {
9782: PetscFunctionBegin;
9784: if (S) {
9785: PetscAssertPointer(S, 2);
9786: *S = F->schur;
9787: }
9788: if (status) {
9789: PetscAssertPointer(status, 3);
9790: *status = F->schur_status;
9791: }
9792: PetscFunctionReturn(PETSC_SUCCESS);
9793: }
9795: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9796: {
9797: Mat S = F->schur;
9799: PetscFunctionBegin;
9800: switch (F->schur_status) {
9801: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9802: case MAT_FACTOR_SCHUR_INVERTED:
9803: if (S) {
9804: S->ops->solve = NULL;
9805: S->ops->matsolve = NULL;
9806: S->ops->solvetranspose = NULL;
9807: S->ops->matsolvetranspose = NULL;
9808: S->ops->solveadd = NULL;
9809: S->ops->solvetransposeadd = NULL;
9810: S->factortype = MAT_FACTOR_NONE;
9811: PetscCall(PetscFree(S->solvertype));
9812: }
9813: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9814: break;
9815: default:
9816: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9817: }
9818: PetscFunctionReturn(PETSC_SUCCESS);
9819: }
9821: /*@
9822: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9824: Logically Collective
9826: Input Parameters:
9827: + F - the factored matrix obtained by calling `MatGetFactor()`
9828: . S - location where the Schur complement is stored
9829: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9831: Level: advanced
9833: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9834: @*/
9835: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9836: {
9837: PetscFunctionBegin;
9839: if (S) {
9841: *S = NULL;
9842: }
9843: F->schur_status = status;
9844: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9845: PetscFunctionReturn(PETSC_SUCCESS);
9846: }
9848: /*@
9849: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9851: Logically Collective
9853: Input Parameters:
9854: + F - the factored matrix obtained by calling `MatGetFactor()`
9855: . rhs - location where the right hand side of the Schur complement system is stored
9856: - sol - location where the solution of the Schur complement system has to be returned
9858: Level: advanced
9860: Notes:
9861: The sizes of the vectors should match the size of the Schur complement
9863: Must be called after `MatFactorSetSchurIS()`
9865: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9866: @*/
9867: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9868: {
9869: PetscFunctionBegin;
9876: PetscCheckSameComm(F, 1, rhs, 2);
9877: PetscCheckSameComm(F, 1, sol, 3);
9878: PetscCall(MatFactorFactorizeSchurComplement(F));
9879: switch (F->schur_status) {
9880: case MAT_FACTOR_SCHUR_FACTORED:
9881: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9882: break;
9883: case MAT_FACTOR_SCHUR_INVERTED:
9884: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9885: break;
9886: default:
9887: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9888: }
9889: PetscFunctionReturn(PETSC_SUCCESS);
9890: }
9892: /*@
9893: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9895: Logically Collective
9897: Input Parameters:
9898: + F - the factored matrix obtained by calling `MatGetFactor()`
9899: . rhs - location where the right hand side of the Schur complement system is stored
9900: - sol - location where the solution of the Schur complement system has to be returned
9902: Level: advanced
9904: Notes:
9905: The sizes of the vectors should match the size of the Schur complement
9907: Must be called after `MatFactorSetSchurIS()`
9909: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9910: @*/
9911: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9912: {
9913: PetscFunctionBegin;
9920: PetscCheckSameComm(F, 1, rhs, 2);
9921: PetscCheckSameComm(F, 1, sol, 3);
9922: PetscCall(MatFactorFactorizeSchurComplement(F));
9923: switch (F->schur_status) {
9924: case MAT_FACTOR_SCHUR_FACTORED:
9925: PetscCall(MatSolve(F->schur, rhs, sol));
9926: break;
9927: case MAT_FACTOR_SCHUR_INVERTED:
9928: PetscCall(MatMult(F->schur, rhs, sol));
9929: break;
9930: default:
9931: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9932: }
9933: PetscFunctionReturn(PETSC_SUCCESS);
9934: }
9936: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9937: #if PetscDefined(HAVE_CUDA)
9938: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9939: #endif
9941: /* Schur status updated in the interface */
9942: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9943: {
9944: Mat S = F->schur;
9946: PetscFunctionBegin;
9947: if (S) {
9948: PetscMPIInt size;
9949: PetscBool isdense, isdensecuda;
9951: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9952: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9953: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9954: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9955: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9956: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9957: if (isdense) {
9958: PetscCall(MatSeqDenseInvertFactors_Private(S));
9959: } else if (isdensecuda) {
9960: #if defined(PETSC_HAVE_CUDA)
9961: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9962: #endif
9963: }
9964: // HIP??????????????
9965: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9966: }
9967: PetscFunctionReturn(PETSC_SUCCESS);
9968: }
9970: /*@
9971: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
9973: Logically Collective
9975: Input Parameter:
9976: . F - the factored matrix obtained by calling `MatGetFactor()`
9978: Level: advanced
9980: Notes:
9981: Must be called after `MatFactorSetSchurIS()`.
9983: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
9985: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9986: @*/
9987: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9988: {
9989: PetscFunctionBegin;
9992: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9993: PetscCall(MatFactorFactorizeSchurComplement(F));
9994: PetscCall(MatFactorInvertSchurComplement_Private(F));
9995: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9996: PetscFunctionReturn(PETSC_SUCCESS);
9997: }
9999: /*@
10000: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10002: Logically Collective
10004: Input Parameter:
10005: . F - the factored matrix obtained by calling `MatGetFactor()`
10007: Level: advanced
10009: Note:
10010: Must be called after `MatFactorSetSchurIS()`
10012: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10013: @*/
10014: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10015: {
10016: MatFactorInfo info;
10018: PetscFunctionBegin;
10021: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10022: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10023: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10024: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10025: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10026: } else {
10027: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10028: }
10029: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10030: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10031: PetscFunctionReturn(PETSC_SUCCESS);
10032: }
10034: /*@
10035: MatPtAP - Creates the matrix product $C = P^T * A * P$
10037: Neighbor-wise Collective
10039: Input Parameters:
10040: + A - the matrix
10041: . P - the projection matrix
10042: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10043: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DEFAULT` if you do not have a good estimate
10044: if the result is a dense matrix this is irrelevant
10046: Output Parameter:
10047: . C - the product matrix
10049: Level: intermediate
10051: Notes:
10052: C will be created and must be destroyed by the user with `MatDestroy()`.
10054: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10056: Developer Note:
10057: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10059: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10060: @*/
10061: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10062: {
10063: PetscFunctionBegin;
10064: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10065: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10067: if (scall == MAT_INITIAL_MATRIX) {
10068: PetscCall(MatProductCreate(A, P, NULL, C));
10069: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10070: PetscCall(MatProductSetAlgorithm(*C, "default"));
10071: PetscCall(MatProductSetFill(*C, fill));
10073: (*C)->product->api_user = PETSC_TRUE;
10074: PetscCall(MatProductSetFromOptions(*C));
10075: 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);
10076: PetscCall(MatProductSymbolic(*C));
10077: } else { /* scall == MAT_REUSE_MATRIX */
10078: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10079: }
10081: PetscCall(MatProductNumeric(*C));
10082: (*C)->symmetric = A->symmetric;
10083: (*C)->spd = A->spd;
10084: PetscFunctionReturn(PETSC_SUCCESS);
10085: }
10087: /*@
10088: MatRARt - Creates the matrix product $C = R * A * R^T$
10090: Neighbor-wise Collective
10092: Input Parameters:
10093: + A - the matrix
10094: . R - the projection matrix
10095: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10096: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DEFAULT` if you do not have a good estimate
10097: if the result is a dense matrix this is irrelevant
10099: Output Parameter:
10100: . C - the product matrix
10102: Level: intermediate
10104: Notes:
10105: C will be created and must be destroyed by the user with `MatDestroy()`.
10107: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10109: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10110: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10111: parallel MatRARt is implemented via explicit transpose of R, which could be very expensive.
10112: We recommend using MatPtAP().
10114: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10115: @*/
10116: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10117: {
10118: PetscFunctionBegin;
10119: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10120: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10122: if (scall == MAT_INITIAL_MATRIX) {
10123: PetscCall(MatProductCreate(A, R, NULL, C));
10124: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10125: PetscCall(MatProductSetAlgorithm(*C, "default"));
10126: PetscCall(MatProductSetFill(*C, fill));
10128: (*C)->product->api_user = PETSC_TRUE;
10129: PetscCall(MatProductSetFromOptions(*C));
10130: 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);
10131: PetscCall(MatProductSymbolic(*C));
10132: } else { /* scall == MAT_REUSE_MATRIX */
10133: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10134: }
10136: PetscCall(MatProductNumeric(*C));
10137: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10138: PetscFunctionReturn(PETSC_SUCCESS);
10139: }
10141: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10142: {
10143: PetscBool flg = PETSC_TRUE;
10145: PetscFunctionBegin;
10146: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10147: if (scall == MAT_INITIAL_MATRIX) {
10148: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10149: PetscCall(MatProductCreate(A, B, NULL, C));
10150: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10151: PetscCall(MatProductSetFill(*C, fill));
10152: } else { /* scall == MAT_REUSE_MATRIX */
10153: Mat_Product *product = (*C)->product;
10155: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10156: if (flg && product && product->type != ptype) {
10157: PetscCall(MatProductClear(*C));
10158: product = NULL;
10159: }
10160: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10161: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10162: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10163: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10164: product = (*C)->product;
10165: product->fill = fill;
10166: product->clear = PETSC_TRUE;
10167: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10168: flg = PETSC_FALSE;
10169: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10170: }
10171: }
10172: if (flg) {
10173: (*C)->product->api_user = PETSC_TRUE;
10174: PetscCall(MatProductSetType(*C, ptype));
10175: PetscCall(MatProductSetFromOptions(*C));
10176: PetscCall(MatProductSymbolic(*C));
10177: }
10178: PetscCall(MatProductNumeric(*C));
10179: PetscFunctionReturn(PETSC_SUCCESS);
10180: }
10182: /*@
10183: MatMatMult - Performs matrix-matrix multiplication C=A*B.
10185: Neighbor-wise Collective
10187: Input Parameters:
10188: + A - the left matrix
10189: . B - the right matrix
10190: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10191: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if you do not have a good estimate
10192: if the result is a dense matrix this is irrelevant
10194: Output Parameter:
10195: . C - the product matrix
10197: Notes:
10198: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10200: `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
10201: call to this function with `MAT_INITIAL_MATRIX`.
10203: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed.
10205: 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`,
10206: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix C is sparse.
10208: Example of Usage:
10209: .vb
10210: MatProductCreate(A,B,NULL,&C);
10211: MatProductSetType(C,MATPRODUCT_AB);
10212: MatProductSymbolic(C);
10213: MatProductNumeric(C); // compute C=A * B
10214: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10215: MatProductNumeric(C);
10216: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10217: MatProductNumeric(C);
10218: .ve
10220: Level: intermediate
10222: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10223: @*/
10224: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10225: {
10226: PetscFunctionBegin;
10227: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10228: PetscFunctionReturn(PETSC_SUCCESS);
10229: }
10231: /*@
10232: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10234: Neighbor-wise Collective
10236: Input Parameters:
10237: + A - the left matrix
10238: . B - the right matrix
10239: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10240: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known
10242: Output Parameter:
10243: . C - the product matrix
10245: Options Database Key:
10246: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10247: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10248: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10250: Level: intermediate
10252: Notes:
10253: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10255: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10257: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10258: actually needed.
10260: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10261: and for pairs of `MATMPIDENSE` matrices.
10263: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`
10265: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10266: @*/
10267: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10268: {
10269: PetscFunctionBegin;
10270: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10271: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10272: PetscFunctionReturn(PETSC_SUCCESS);
10273: }
10275: /*@
10276: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10278: Neighbor-wise Collective
10280: Input Parameters:
10281: + A - the left matrix
10282: . B - the right matrix
10283: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10284: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known
10286: Output Parameter:
10287: . C - the product matrix
10289: Level: intermediate
10291: Notes:
10292: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10294: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.
10296: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`
10298: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10299: actually needed.
10301: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10302: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10304: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10305: @*/
10306: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10307: {
10308: PetscFunctionBegin;
10309: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10310: PetscFunctionReturn(PETSC_SUCCESS);
10311: }
10313: /*@
10314: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10316: Neighbor-wise Collective
10318: Input Parameters:
10319: + A - the left matrix
10320: . B - the middle matrix
10321: . C - the right matrix
10322: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10323: - fill - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DEFAULT` if you do not have a good estimate
10324: if the result is a dense matrix this is irrelevant
10326: Output Parameter:
10327: . D - the product matrix
10329: Level: intermediate
10331: Notes:
10332: Unless `scall` is `MAT_REUSE_MATRIX` D will be created.
10334: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10336: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`
10338: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10339: actually needed.
10341: If you have many matrices with the same non-zero structure to multiply, you
10342: should use `MAT_REUSE_MATRIX` in all calls but the first
10344: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10345: @*/
10346: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10347: {
10348: PetscFunctionBegin;
10349: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10350: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10352: if (scall == MAT_INITIAL_MATRIX) {
10353: PetscCall(MatProductCreate(A, B, C, D));
10354: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10355: PetscCall(MatProductSetAlgorithm(*D, "default"));
10356: PetscCall(MatProductSetFill(*D, fill));
10358: (*D)->product->api_user = PETSC_TRUE;
10359: PetscCall(MatProductSetFromOptions(*D));
10360: 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,
10361: ((PetscObject)C)->type_name);
10362: PetscCall(MatProductSymbolic(*D));
10363: } else { /* user may change input matrices when REUSE */
10364: PetscCall(MatProductReplaceMats(A, B, C, *D));
10365: }
10366: PetscCall(MatProductNumeric(*D));
10367: PetscFunctionReturn(PETSC_SUCCESS);
10368: }
10370: /*@
10371: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10373: Collective
10375: Input Parameters:
10376: + mat - the matrix
10377: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10378: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10379: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10381: Output Parameter:
10382: . matredundant - redundant matrix
10384: Level: advanced
10386: Notes:
10387: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10388: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10390: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10391: calling it.
10393: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10395: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10396: @*/
10397: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10398: {
10399: MPI_Comm comm;
10400: PetscMPIInt size;
10401: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10402: Mat_Redundant *redund = NULL;
10403: PetscSubcomm psubcomm = NULL;
10404: MPI_Comm subcomm_in = subcomm;
10405: Mat *matseq;
10406: IS isrow, iscol;
10407: PetscBool newsubcomm = PETSC_FALSE;
10409: PetscFunctionBegin;
10411: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10412: PetscAssertPointer(*matredundant, 5);
10414: }
10416: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10417: if (size == 1 || nsubcomm == 1) {
10418: if (reuse == MAT_INITIAL_MATRIX) {
10419: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10420: } else {
10421: 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");
10422: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10423: }
10424: PetscFunctionReturn(PETSC_SUCCESS);
10425: }
10427: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10428: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10429: MatCheckPreallocated(mat, 1);
10431: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10432: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10433: /* create psubcomm, then get subcomm */
10434: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10435: PetscCallMPI(MPI_Comm_size(comm, &size));
10436: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10438: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10439: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10440: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10441: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10442: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10443: newsubcomm = PETSC_TRUE;
10444: PetscCall(PetscSubcommDestroy(&psubcomm));
10445: }
10447: /* get isrow, iscol and a local sequential matrix matseq[0] */
10448: if (reuse == MAT_INITIAL_MATRIX) {
10449: mloc_sub = PETSC_DECIDE;
10450: nloc_sub = PETSC_DECIDE;
10451: if (bs < 1) {
10452: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10453: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10454: } else {
10455: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10456: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10457: }
10458: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10459: rstart = rend - mloc_sub;
10460: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10461: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10462: PetscCall(ISSetIdentity(iscol));
10463: } else { /* reuse == MAT_REUSE_MATRIX */
10464: 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");
10465: /* retrieve subcomm */
10466: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10467: redund = (*matredundant)->redundant;
10468: isrow = redund->isrow;
10469: iscol = redund->iscol;
10470: matseq = redund->matseq;
10471: }
10472: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10474: /* get matredundant over subcomm */
10475: if (reuse == MAT_INITIAL_MATRIX) {
10476: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10478: /* create a supporting struct and attach it to C for reuse */
10479: PetscCall(PetscNew(&redund));
10480: (*matredundant)->redundant = redund;
10481: redund->isrow = isrow;
10482: redund->iscol = iscol;
10483: redund->matseq = matseq;
10484: if (newsubcomm) {
10485: redund->subcomm = subcomm;
10486: } else {
10487: redund->subcomm = MPI_COMM_NULL;
10488: }
10489: } else {
10490: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10491: }
10492: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10493: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10494: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10495: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10496: }
10497: #endif
10498: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10499: PetscFunctionReturn(PETSC_SUCCESS);
10500: }
10502: /*@C
10503: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10504: a given `Mat`. Each submatrix can span multiple procs.
10506: Collective
10508: Input Parameters:
10509: + mat - the matrix
10510: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10511: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10513: Output Parameter:
10514: . subMat - parallel sub-matrices each spanning a given `subcomm`
10516: Level: advanced
10518: Notes:
10519: The submatrix partition across processors is dictated by `subComm` a
10520: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10521: is not restricted to be grouped with consecutive original MPI processes.
10523: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10524: map directly to the layout of the original matrix [wrt the local
10525: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10526: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10527: the `subMat`. However the offDiagMat looses some columns - and this is
10528: reconstructed with `MatSetValues()`
10530: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10532: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10533: @*/
10534: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10535: {
10536: PetscMPIInt commsize, subCommSize;
10538: PetscFunctionBegin;
10539: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10540: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10541: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10543: 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");
10544: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10545: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10546: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10547: PetscFunctionReturn(PETSC_SUCCESS);
10548: }
10550: /*@
10551: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10553: Not Collective
10555: Input Parameters:
10556: + mat - matrix to extract local submatrix from
10557: . isrow - local row indices for submatrix
10558: - iscol - local column indices for submatrix
10560: Output Parameter:
10561: . submat - the submatrix
10563: Level: intermediate
10565: Notes:
10566: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10568: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10569: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10571: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10572: `MatSetValuesBlockedLocal()` will also be implemented.
10574: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10575: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10577: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10578: @*/
10579: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10580: {
10581: PetscFunctionBegin;
10585: PetscCheckSameComm(isrow, 2, iscol, 3);
10586: PetscAssertPointer(submat, 4);
10587: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10589: if (mat->ops->getlocalsubmatrix) {
10590: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10591: } else {
10592: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10593: }
10594: PetscFunctionReturn(PETSC_SUCCESS);
10595: }
10597: /*@
10598: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10600: Not Collective
10602: Input Parameters:
10603: + mat - matrix to extract local submatrix from
10604: . isrow - local row indices for submatrix
10605: . iscol - local column indices for submatrix
10606: - submat - the submatrix
10608: Level: intermediate
10610: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10611: @*/
10612: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10613: {
10614: PetscFunctionBegin;
10618: PetscCheckSameComm(isrow, 2, iscol, 3);
10619: PetscAssertPointer(submat, 4);
10622: if (mat->ops->restorelocalsubmatrix) {
10623: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10624: } else {
10625: PetscCall(MatDestroy(submat));
10626: }
10627: *submat = NULL;
10628: PetscFunctionReturn(PETSC_SUCCESS);
10629: }
10631: /*@
10632: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10634: Collective
10636: Input Parameter:
10637: . mat - the matrix
10639: Output Parameter:
10640: . is - if any rows have zero diagonals this contains the list of them
10642: Level: developer
10644: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10645: @*/
10646: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10647: {
10648: PetscFunctionBegin;
10651: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10652: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10654: if (!mat->ops->findzerodiagonals) {
10655: Vec diag;
10656: const PetscScalar *a;
10657: PetscInt *rows;
10658: PetscInt rStart, rEnd, r, nrow = 0;
10660: PetscCall(MatCreateVecs(mat, &diag, NULL));
10661: PetscCall(MatGetDiagonal(mat, diag));
10662: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10663: PetscCall(VecGetArrayRead(diag, &a));
10664: for (r = 0; r < rEnd - rStart; ++r)
10665: if (a[r] == 0.0) ++nrow;
10666: PetscCall(PetscMalloc1(nrow, &rows));
10667: nrow = 0;
10668: for (r = 0; r < rEnd - rStart; ++r)
10669: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10670: PetscCall(VecRestoreArrayRead(diag, &a));
10671: PetscCall(VecDestroy(&diag));
10672: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10673: } else {
10674: PetscUseTypeMethod(mat, findzerodiagonals, is);
10675: }
10676: PetscFunctionReturn(PETSC_SUCCESS);
10677: }
10679: /*@
10680: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10682: Collective
10684: Input Parameter:
10685: . mat - the matrix
10687: Output Parameter:
10688: . is - contains the list of rows with off block diagonal entries
10690: Level: developer
10692: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10693: @*/
10694: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10695: {
10696: PetscFunctionBegin;
10699: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10700: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10702: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10703: PetscFunctionReturn(PETSC_SUCCESS);
10704: }
10706: /*@C
10707: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10709: Collective; No Fortran Support
10711: Input Parameter:
10712: . mat - the matrix
10714: Output Parameter:
10715: . values - the block inverses in column major order (FORTRAN-like)
10717: Level: advanced
10719: Notes:
10720: The size of the blocks is determined by the block size of the matrix.
10722: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10724: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10726: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10727: @*/
10728: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar **values)
10729: {
10730: PetscFunctionBegin;
10732: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10733: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10734: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10735: PetscFunctionReturn(PETSC_SUCCESS);
10736: }
10738: /*@C
10739: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10741: Collective; No Fortran Support
10743: Input Parameters:
10744: + mat - the matrix
10745: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10746: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10748: Output Parameter:
10749: . values - the block inverses in column major order (FORTRAN-like)
10751: Level: advanced
10753: Notes:
10754: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10756: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10758: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10759: @*/
10760: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *values)
10761: {
10762: PetscFunctionBegin;
10764: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10765: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10766: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10767: PetscFunctionReturn(PETSC_SUCCESS);
10768: }
10770: /*@
10771: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10773: Collective
10775: Input Parameters:
10776: + A - the matrix
10777: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10779: Level: advanced
10781: Note:
10782: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10784: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10785: @*/
10786: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10787: {
10788: const PetscScalar *vals;
10789: PetscInt *dnnz;
10790: PetscInt m, rstart, rend, bs, i, j;
10792: PetscFunctionBegin;
10793: PetscCall(MatInvertBlockDiagonal(A, &vals));
10794: PetscCall(MatGetBlockSize(A, &bs));
10795: PetscCall(MatGetLocalSize(A, &m, NULL));
10796: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10797: PetscCall(PetscMalloc1(m / bs, &dnnz));
10798: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10799: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10800: PetscCall(PetscFree(dnnz));
10801: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10802: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10803: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10804: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10805: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10806: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10807: PetscFunctionReturn(PETSC_SUCCESS);
10808: }
10810: /*@C
10811: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10812: via `MatTransposeColoringCreate()`.
10814: Collective
10816: Input Parameter:
10817: . c - coloring context
10819: Level: intermediate
10821: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10822: @*/
10823: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10824: {
10825: MatTransposeColoring matcolor = *c;
10827: PetscFunctionBegin;
10828: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10829: if (--((PetscObject)matcolor)->refct > 0) {
10830: matcolor = NULL;
10831: PetscFunctionReturn(PETSC_SUCCESS);
10832: }
10834: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10835: PetscCall(PetscFree(matcolor->rows));
10836: PetscCall(PetscFree(matcolor->den2sp));
10837: PetscCall(PetscFree(matcolor->colorforcol));
10838: PetscCall(PetscFree(matcolor->columns));
10839: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10840: PetscCall(PetscHeaderDestroy(c));
10841: PetscFunctionReturn(PETSC_SUCCESS);
10842: }
10844: /*@C
10845: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10846: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10847: `MatTransposeColoring` to sparse `B`.
10849: Collective
10851: Input Parameters:
10852: + coloring - coloring context created with `MatTransposeColoringCreate()`
10853: - B - sparse matrix
10855: Output Parameter:
10856: . Btdense - dense matrix $B^T$
10858: Level: developer
10860: Note:
10861: These are used internally for some implementations of `MatRARt()`
10863: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10864: @*/
10865: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10866: {
10867: PetscFunctionBegin;
10872: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10873: PetscFunctionReturn(PETSC_SUCCESS);
10874: }
10876: /*@C
10877: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10878: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10879: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10880: $C_{sp}$ from $C_{den}$.
10882: Collective
10884: Input Parameters:
10885: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10886: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10888: Output Parameter:
10889: . Csp - sparse matrix
10891: Level: developer
10893: Note:
10894: These are used internally for some implementations of `MatRARt()`
10896: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10897: @*/
10898: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10899: {
10900: PetscFunctionBegin;
10905: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10906: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10907: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10908: PetscFunctionReturn(PETSC_SUCCESS);
10909: }
10911: /*@C
10912: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10914: Collective
10916: Input Parameters:
10917: + mat - the matrix product C
10918: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10920: Output Parameter:
10921: . color - the new coloring context
10923: Level: intermediate
10925: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10926: `MatTransColoringApplyDenToSp()`
10927: @*/
10928: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10929: {
10930: MatTransposeColoring c;
10931: MPI_Comm comm;
10933: PetscFunctionBegin;
10934: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10935: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10936: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10938: c->ctype = iscoloring->ctype;
10939: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10941: *color = c;
10942: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10943: PetscFunctionReturn(PETSC_SUCCESS);
10944: }
10946: /*@
10947: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10948: matrix has had no new nonzero locations added to (or removed from) the matrix since the previous call then the value will be the
10949: same, otherwise it will be larger
10951: Not Collective
10953: Input Parameter:
10954: . mat - the matrix
10956: Output Parameter:
10957: . state - the current state
10959: Level: intermediate
10961: Notes:
10962: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10963: different matrices
10965: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
10967: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
10969: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10970: @*/
10971: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10972: {
10973: PetscFunctionBegin;
10975: *state = mat->nonzerostate;
10976: PetscFunctionReturn(PETSC_SUCCESS);
10977: }
10979: /*@
10980: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10981: matrices from each processor
10983: Collective
10985: Input Parameters:
10986: + comm - the communicators the parallel matrix will live on
10987: . seqmat - the input sequential matrices
10988: . n - number of local columns (or `PETSC_DECIDE`)
10989: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10991: Output Parameter:
10992: . mpimat - the parallel matrix generated
10994: Level: developer
10996: Note:
10997: The number of columns of the matrix in EACH processor MUST be the same.
10999: .seealso: [](ch_matrices), `Mat`
11000: @*/
11001: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11002: {
11003: PetscMPIInt size;
11005: PetscFunctionBegin;
11006: PetscCallMPI(MPI_Comm_size(comm, &size));
11007: if (size == 1) {
11008: if (reuse == MAT_INITIAL_MATRIX) {
11009: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11010: } else {
11011: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11012: }
11013: PetscFunctionReturn(PETSC_SUCCESS);
11014: }
11016: 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");
11018: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11019: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11020: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11021: PetscFunctionReturn(PETSC_SUCCESS);
11022: }
11024: /*@
11025: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11027: Collective
11029: Input Parameters:
11030: + A - the matrix to create subdomains from
11031: - N - requested number of subdomains
11033: Output Parameters:
11034: + n - number of subdomains resulting on this MPI process
11035: - iss - `IS` list with indices of subdomains on this MPI process
11037: Level: advanced
11039: Note:
11040: The number of subdomains must be smaller than the communicator size
11042: .seealso: [](ch_matrices), `Mat`, `IS`
11043: @*/
11044: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11045: {
11046: MPI_Comm comm, subcomm;
11047: PetscMPIInt size, rank, color;
11048: PetscInt rstart, rend, k;
11050: PetscFunctionBegin;
11051: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11052: PetscCallMPI(MPI_Comm_size(comm, &size));
11053: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11054: PetscCheck(N >= 1 && N < (PetscInt)size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
11055: *n = 1;
11056: k = ((PetscInt)size) / N + ((PetscInt)size % N > 0); /* There are up to k ranks to a color */
11057: color = rank / k;
11058: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11059: PetscCall(PetscMalloc1(1, iss));
11060: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11061: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11062: PetscCallMPI(MPI_Comm_free(&subcomm));
11063: PetscFunctionReturn(PETSC_SUCCESS);
11064: }
11066: /*@
11067: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11069: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11070: If they are not the same, uses `MatMatMatMult()`.
11072: Once the coarse grid problem is constructed, correct for interpolation operators
11073: that are not of full rank, which can legitimately happen in the case of non-nested
11074: geometric multigrid.
11076: Input Parameters:
11077: + restrct - restriction operator
11078: . dA - fine grid matrix
11079: . interpolate - interpolation operator
11080: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11081: - fill - expected fill, use `PETSC_DEFAULT` if you do not have a good estimate
11083: Output Parameter:
11084: . A - the Galerkin coarse matrix
11086: Options Database Key:
11087: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11089: Level: developer
11091: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11092: @*/
11093: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11094: {
11095: IS zerorows;
11096: Vec diag;
11098: PetscFunctionBegin;
11099: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11100: /* Construct the coarse grid matrix */
11101: if (interpolate == restrct) {
11102: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11103: } else {
11104: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11105: }
11107: /* If the interpolation matrix is not of full rank, A will have zero rows.
11108: This can legitimately happen in the case of non-nested geometric multigrid.
11109: In that event, we set the rows of the matrix to the rows of the identity,
11110: ignoring the equations (as the RHS will also be zero). */
11112: PetscCall(MatFindZeroRows(*A, &zerorows));
11114: if (zerorows != NULL) { /* if there are any zero rows */
11115: PetscCall(MatCreateVecs(*A, &diag, NULL));
11116: PetscCall(MatGetDiagonal(*A, diag));
11117: PetscCall(VecISSet(diag, zerorows, 1.0));
11118: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11119: PetscCall(VecDestroy(&diag));
11120: PetscCall(ISDestroy(&zerorows));
11121: }
11122: PetscFunctionReturn(PETSC_SUCCESS);
11123: }
11125: /*@C
11126: MatSetOperation - Allows user to set a matrix operation for any matrix type
11128: Logically Collective
11130: Input Parameters:
11131: + mat - the matrix
11132: . op - the name of the operation
11133: - f - the function that provides the operation
11135: Level: developer
11137: Example Usage:
11138: .vb
11139: extern PetscErrorCode usermult(Mat, Vec, Vec);
11141: PetscCall(MatCreateXXX(comm, ..., &A));
11142: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11143: .ve
11145: Notes:
11146: See the file `include/petscmat.h` for a complete list of matrix
11147: operations, which all have the form MATOP_<OPERATION>, where
11148: <OPERATION> is the name (in all capital letters) of the
11149: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11151: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11152: sequence as the usual matrix interface routines, since they
11153: are intended to be accessed via the usual matrix interface
11154: routines, e.g.,
11155: .vb
11156: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11157: .ve
11159: In particular each function MUST return `PETSC_SUCCESS` on success and
11160: nonzero on failure.
11162: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11164: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11165: @*/
11166: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11167: {
11168: PetscFunctionBegin;
11170: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11171: (((void (**)(void))mat->ops)[op]) = f;
11172: PetscFunctionReturn(PETSC_SUCCESS);
11173: }
11175: /*@C
11176: MatGetOperation - Gets a matrix operation for any matrix type.
11178: Not Collective
11180: Input Parameters:
11181: + mat - the matrix
11182: - op - the name of the operation
11184: Output Parameter:
11185: . f - the function that provides the operation
11187: Level: developer
11189: Example Usage:
11190: .vb
11191: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11193: MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11194: .ve
11196: Notes:
11197: See the file include/petscmat.h for a complete list of matrix
11198: operations, which all have the form MATOP_<OPERATION>, where
11199: <OPERATION> is the name (in all capital letters) of the
11200: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11202: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11204: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11205: @*/
11206: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11207: {
11208: PetscFunctionBegin;
11210: *f = (((void (**)(void))mat->ops)[op]);
11211: PetscFunctionReturn(PETSC_SUCCESS);
11212: }
11214: /*@
11215: MatHasOperation - Determines whether the given matrix supports the particular operation.
11217: Not Collective
11219: Input Parameters:
11220: + mat - the matrix
11221: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11223: Output Parameter:
11224: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11226: Level: advanced
11228: Note:
11229: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11231: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11232: @*/
11233: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11234: {
11235: PetscFunctionBegin;
11237: PetscAssertPointer(has, 3);
11238: if (mat->ops->hasoperation) {
11239: PetscUseTypeMethod(mat, hasoperation, op, has);
11240: } else {
11241: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11242: else {
11243: *has = PETSC_FALSE;
11244: if (op == MATOP_CREATE_SUBMATRIX) {
11245: PetscMPIInt size;
11247: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11248: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11249: }
11250: }
11251: }
11252: PetscFunctionReturn(PETSC_SUCCESS);
11253: }
11255: /*@
11256: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11258: Collective
11260: Input Parameter:
11261: . mat - the matrix
11263: Output Parameter:
11264: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11266: Level: beginner
11268: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11269: @*/
11270: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11271: {
11272: PetscFunctionBegin;
11275: PetscAssertPointer(cong, 2);
11276: if (!mat->rmap || !mat->cmap) {
11277: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11278: PetscFunctionReturn(PETSC_SUCCESS);
11279: }
11280: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11281: PetscCall(PetscLayoutSetUp(mat->rmap));
11282: PetscCall(PetscLayoutSetUp(mat->cmap));
11283: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11284: if (*cong) mat->congruentlayouts = 1;
11285: else mat->congruentlayouts = 0;
11286: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11287: PetscFunctionReturn(PETSC_SUCCESS);
11288: }
11290: PetscErrorCode MatSetInf(Mat A)
11291: {
11292: PetscFunctionBegin;
11293: PetscUseTypeMethod(A, setinf);
11294: PetscFunctionReturn(PETSC_SUCCESS);
11295: }
11297: /*@C
11298: 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
11299: and possibly removes small values from the graph structure.
11301: Collective
11303: Input Parameters:
11304: + A - the matrix
11305: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11306: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11307: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11308: . num_idx - size of 'index' array
11309: - index - array of block indices to use for graph strength of connection weight
11311: Output Parameter:
11312: . graph - the resulting graph
11314: Level: advanced
11316: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11317: @*/
11318: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11319: {
11320: PetscFunctionBegin;
11324: PetscAssertPointer(graph, 7);
11325: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11326: PetscFunctionReturn(PETSC_SUCCESS);
11327: }
11329: /*@
11330: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11331: meaning the same memory is used for the matrix, and no new memory is allocated.
11333: Collective
11335: Input Parameters:
11336: + A - the matrix
11337: - 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
11339: Level: intermediate
11341: Developer Note:
11342: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11343: of the arrays in the data structure are unneeded.
11345: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11346: @*/
11347: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11348: {
11349: PetscFunctionBegin;
11351: PetscUseTypeMethod(A, eliminatezeros, keep);
11352: PetscFunctionReturn(PETSC_SUCCESS);
11353: }