Actual source code: matrix.c

petsc-master 2017-09-23
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  2: /*
  3:    This is where the abstract matrix operations are defined
  4: */

  6:  #include <petsc/private/matimpl.h>
  7:  #include <petsc/private/isimpl.h>
  8:  #include <petsc/private/vecimpl.h>

 10: /* Logging support */
 11: PetscClassId MAT_CLASSID;
 12: PetscClassId MAT_COLORING_CLASSID;
 13: PetscClassId MAT_FDCOLORING_CLASSID;
 14: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;

 16: PetscLogEvent MAT_Mult, MAT_Mults, MAT_MultConstrained, MAT_MultAdd, MAT_MultTranspose;
 17: PetscLogEvent MAT_MultTransposeConstrained, MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve;
 18: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
 19: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
 20: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
 21: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
 22: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
 23: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply,MAT_Transpose,MAT_FDColoringFunction, MAT_CreateSubMat;
 24: PetscLogEvent MAT_TransposeColoringCreate;
 25: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
 26: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric,MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
 27: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
 28: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
 29: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
 30: PetscLogEvent MAT_MultHermitianTranspose,MAT_MultHermitianTransposeAdd;
 31: PetscLogEvent MAT_Getsymtranspose, MAT_Getsymtransreduced, MAT_Transpose_SeqAIJ, MAT_GetBrowsOfAcols;
 32: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
 33: PetscLogEvent MAT_Applypapt, MAT_Applypapt_numeric, MAT_Applypapt_symbolic, MAT_GetSequentialNonzeroStructure;
 34: PetscLogEvent MAT_GetMultiProcBlock;
 35: PetscLogEvent MAT_CUSPCopyToGPU, MAT_CUSPARSECopyToGPU, MAT_SetValuesBatch, MAT_SetValuesBatchI, MAT_SetValuesBatchII, MAT_SetValuesBatchIII, MAT_SetValuesBatchIV;
 36: PetscLogEvent MAT_ViennaCLCopyToGPU;
 37: PetscLogEvent MAT_Merge,MAT_Residual,MAT_SetRandom;
 38: PetscLogEvent MATCOLORING_Apply,MATCOLORING_Comm,MATCOLORING_Local,MATCOLORING_ISCreate,MATCOLORING_SetUp,MATCOLORING_Weights;

 40: const char *const MatFactorTypes[] = {"NONE","LU","CHOLESKY","ILU","ICC","ILUDT","MatFactorType","MAT_FACTOR_",0};

 42: /*@
 43:    MatSetRandom - Sets all components of a matrix to random numbers. For sparse matrices that have been preallocated it randomly selects appropriate locations

 45:    Logically Collective on Vec

 47:    Input Parameters:
 48: +  x  - the vector
 49: -  rctx - the random number context, formed by PetscRandomCreate(), or NULL and
 50:           it will create one internally.

 52:    Output Parameter:
 53: .  x  - the vector

 55:    Example of Usage:
 56: .vb
 57:      PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
 58:      MatSetRandom(x,rctx);
 59:      PetscRandomDestroy(rctx);
 60: .ve

 62:    Level: intermediate

 64:    Concepts: matrix^setting to random
 65:    Concepts: random^matrix

 67: .seealso: MatZeroEntries(), MatSetValues(), PetscRandomCreate(), PetscRandomDestroy()
 68: @*/
 69: PetscErrorCode MatSetRandom(Mat x,PetscRandom rctx)
 70: {
 72:   PetscRandom    randObj = NULL;


 79:   if (!rctx) {
 80:     MPI_Comm comm;
 81:     PetscObjectGetComm((PetscObject)x,&comm);
 82:     PetscRandomCreate(comm,&randObj);
 83:     PetscRandomSetFromOptions(randObj);
 84:     rctx = randObj;
 85:   }

 87:   PetscLogEventBegin(MAT_SetRandom,x,rctx,0,0);
 88:   (*x->ops->setrandom)(x,rctx);
 89:   PetscLogEventEnd(MAT_SetRandom,x,rctx,0,0);

 91:   x->assembled = PETSC_TRUE;
 92:   PetscRandomDestroy(&randObj);
 93:   return(0);
 94: }

 96: /*@
 97:    MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

 99:    Logically Collective on Mat

101:    Input Parameters:
102: .  mat - the factored matrix

104:    Output Parameter:
105: +  pivot - the pivot value computed
106: -  row - the row that the zero pivot occurred. Note that this row must be interpreted carefully due to row reorderings and which processes
107:          the share the matrix

109:    Level: advanced

111:    Notes: This routine does not work for factorizations done with external packages.
112:    This routine should only be called if MatGetFactorError() returns a value of MAT_FACTOR_NUMERIC_ZEROPIVOT

114:    This can be called on non-factored matrices that come from, for example, matrices used in SOR.

116: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
117: @*/
118: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat,PetscReal *pivot,PetscInt *row)
119: {
122:   *pivot = mat->factorerror_zeropivot_value;
123:   *row   = mat->factorerror_zeropivot_row;
124:   return(0);
125: }

127: /*@
128:    MatFactorGetError - gets the error code from a factorization

130:    Logically Collective on Mat

132:    Input Parameters:
133: .  mat - the factored matrix

135:    Output Parameter:
136: .  err  - the error code

138:    Level: advanced

140:    Notes:    This can be called on non-factored matrices that come from, for example, matrices used in SOR.

142: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
143: @*/
144: PetscErrorCode MatFactorGetError(Mat mat,MatFactorError *err)
145: {
148:   *err = mat->factorerrortype;
149:   return(0);
150: }

152: /*@
153:    MatFactorClearError - clears the error code in a factorization

155:    Logically Collective on Mat

157:    Input Parameter:
158: .  mat - the factored matrix

160:    Level: developer

162:    Notes: This can be called on non-factored matrices that come from, for example, matrices used in SOR.

164: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorGetError(), MatFactorGetErrorZeroPivot()
165: @*/
166: PetscErrorCode MatFactorClearError(Mat mat)
167: {
170:   mat->factorerrortype             = MAT_FACTOR_NOERROR;
171:   mat->factorerror_zeropivot_value = 0.0;
172:   mat->factorerror_zeropivot_row   = 0;
173:   return(0);
174: }


177: /*@
178:       MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

180:   Input Parameter:
181: .    A  - the matrix

183:   Output Parameter:
184: .    keptrows - the rows that are not completely zero

186:   Notes: keptrows is set to NULL if all rows are nonzero.

188:   Level: intermediate

190:  @*/
191: PetscErrorCode MatFindNonzeroRows(Mat mat,IS *keptrows)
192: {

197:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
198:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
199:   if (!mat->ops->findnonzerorows) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not coded for this matrix type");
200:   (*mat->ops->findnonzerorows)(mat,keptrows);
201:   return(0);
202: }

204: /*@
205:       MatFindZeroRows - Locate all rows that are completely zero in the matrix

207:   Input Parameter:
208: .    A  - the matrix

210:   Output Parameter:
211: .    zerorows - the rows that are completely zero

213:   Notes: zerorows is set to NULL if no rows are zero.

215:   Level: intermediate

217:  @*/
218: PetscErrorCode MatFindZeroRows(Mat mat,IS *zerorows)
219: {
221:   IS keptrows;
222:   PetscInt m, n;


227:   MatFindNonzeroRows(mat, &keptrows);
228:   /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
229:      In keeping with this convention, we set zerorows to NULL if there are no zero
230:      rows. */
231:   if (keptrows == NULL) {
232:     *zerorows = NULL;
233:   } else {
234:     MatGetOwnershipRange(mat,&m,&n);
235:     ISComplement(keptrows,m,n,zerorows);
236:     ISDestroy(&keptrows);
237:   }
238:   return(0);
239: }

241: /*@
242:    MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

244:    Not Collective

246:    Input Parameters:
247: .   A - the matrix

249:    Output Parameters:
250: .   a - the diagonal part (which is a SEQUENTIAL matrix)

252:    Notes: see the manual page for MatCreateAIJ() for more information on the "diagonal part" of the matrix.
253:           Use caution, as the reference count on the returned matrix is not incremented and it is used as
254:           part of the containing MPI Mat's normal operation.

256:    Level: advanced

258: @*/
259: PetscErrorCode MatGetDiagonalBlock(Mat A,Mat *a)
260: {

267:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
268:   if (!A->ops->getdiagonalblock) {
269:     PetscMPIInt size;
270:     MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
271:     if (size == 1) {
272:       *a = A;
273:       return(0);
274:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not coded for this matrix type");
275:   }
276:   (*A->ops->getdiagonalblock)(A,a);
277:   return(0);
278: }

280: /*@
281:    MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

283:    Collective on Mat

285:    Input Parameters:
286: .  mat - the matrix

288:    Output Parameter:
289: .   trace - the sum of the diagonal entries

291:    Level: advanced

293: @*/
294: PetscErrorCode MatGetTrace(Mat mat,PetscScalar *trace)
295: {
297:   Vec            diag;

300:   MatCreateVecs(mat,&diag,NULL);
301:   MatGetDiagonal(mat,diag);
302:   VecSum(diag,trace);
303:   VecDestroy(&diag);
304:   return(0);
305: }

307: /*@
308:    MatRealPart - Zeros out the imaginary part of the matrix

310:    Logically Collective on Mat

312:    Input Parameters:
313: .  mat - the matrix

315:    Level: advanced


318: .seealso: MatImaginaryPart()
319: @*/
320: PetscErrorCode MatRealPart(Mat mat)
321: {

327:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
328:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
329:   if (!mat->ops->realpart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
330:   MatCheckPreallocated(mat,1);
331:   (*mat->ops->realpart)(mat);
332: #if defined(PETSC_HAVE_CUSP)
333:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
334:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
335:   }
336: #elif defined(PETSC_HAVE_VIENNACL)
337:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
338:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
339:   }
340: #elif defined(PETSC_HAVE_VECCUDA)
341:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
342:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
343:   }
344: #endif
345:   return(0);
346: }

348: /*@C
349:    MatGetGhosts - Get the global index of all ghost nodes defined by the sparse matrix

351:    Collective on Mat

353:    Input Parameter:
354: .  mat - the matrix

356:    Output Parameters:
357: +   nghosts - number of ghosts (note for BAIJ matrices there is one ghost for each block)
358: -   ghosts - the global indices of the ghost points

360:    Notes: the nghosts and ghosts are suitable to pass into VecCreateGhost()

362:    Level: advanced

364: @*/
365: PetscErrorCode MatGetGhosts(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
366: {

372:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
373:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
374:   if (!mat->ops->getghosts) {
375:     if (nghosts) *nghosts = 0;
376:     if (ghosts) *ghosts = 0;
377:   } else {
378:     (*mat->ops->getghosts)(mat,nghosts,ghosts);
379:   }
380:   return(0);
381: }


384: /*@
385:    MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

387:    Logically Collective on Mat

389:    Input Parameters:
390: .  mat - the matrix

392:    Level: advanced


395: .seealso: MatRealPart()
396: @*/
397: PetscErrorCode MatImaginaryPart(Mat mat)
398: {

404:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
405:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
406:   if (!mat->ops->imaginarypart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
407:   MatCheckPreallocated(mat,1);
408:   (*mat->ops->imaginarypart)(mat);
409: #if defined(PETSC_HAVE_CUSP)
410:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
411:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
412:   }
413: #elif defined(PETSC_HAVE_VIENNACL)
414:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
415:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
416:   }
417: #elif defined(PETSC_HAVE_VECCUDA)
418:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
419:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
420:   }
421: #endif
422:   return(0);
423: }

425: /*@
426:    MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for BAIJ matrices)

428:    Not Collective

430:    Input Parameter:
431: .  mat - the matrix

433:    Output Parameters:
434: +  missing - is any diagonal missing
435: -  dd - first diagonal entry that is missing (optional) on this process

437:    Level: advanced


440: .seealso: MatRealPart()
441: @*/
442: PetscErrorCode MatMissingDiagonal(Mat mat,PetscBool *missing,PetscInt *dd)
443: {

449:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
450:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
451:   if (!mat->ops->missingdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
452:   (*mat->ops->missingdiagonal)(mat,missing,dd);
453:   return(0);
454: }

456: /*@C
457:    MatGetRow - Gets a row of a matrix.  You MUST call MatRestoreRow()
458:    for each row that you get to ensure that your application does
459:    not bleed memory.

461:    Not Collective

463:    Input Parameters:
464: +  mat - the matrix
465: -  row - the row to get

467:    Output Parameters:
468: +  ncols -  if not NULL, the number of nonzeros in the row
469: .  cols - if not NULL, the column numbers
470: -  vals - if not NULL, the values

472:    Notes:
473:    This routine is provided for people who need to have direct access
474:    to the structure of a matrix.  We hope that we provide enough
475:    high-level matrix routines that few users will need it.

477:    MatGetRow() always returns 0-based column indices, regardless of
478:    whether the internal representation is 0-based (default) or 1-based.

480:    For better efficiency, set cols and/or vals to NULL if you do
481:    not wish to extract these quantities.

483:    The user can only examine the values extracted with MatGetRow();
484:    the values cannot be altered.  To change the matrix entries, one
485:    must use MatSetValues().

487:    You can only have one call to MatGetRow() outstanding for a particular
488:    matrix at a time, per processor. MatGetRow() can only obtain rows
489:    associated with the given processor, it cannot get rows from the
490:    other processors; for that we suggest using MatCreateSubMatrices(), then
491:    MatGetRow() on the submatrix. The row index passed to MatGetRows()
492:    is in the global number of rows.

494:    Fortran Notes:
495:    The calling sequence from Fortran is
496: .vb
497:    MatGetRow(matrix,row,ncols,cols,values,ierr)
498:          Mat     matrix (input)
499:          integer row    (input)
500:          integer ncols  (output)
501:          integer cols(maxcols) (output)
502:          double precision (or double complex) values(maxcols) output
503: .ve
504:    where maxcols >= maximum nonzeros in any row of the matrix.


507:    Caution:
508:    Do not try to change the contents of the output arrays (cols and vals).
509:    In some cases, this may corrupt the matrix.

511:    Level: advanced

513:    Concepts: matrices^row access

515: .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatCreateSubMatrices(), MatGetDiagonal()
516: @*/
517: PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
518: {
520:   PetscInt       incols;

525:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
526:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
527:   if (!mat->ops->getrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
528:   MatCheckPreallocated(mat,1);
529:   PetscLogEventBegin(MAT_GetRow,mat,0,0,0);
530:   (*mat->ops->getrow)(mat,row,&incols,(PetscInt**)cols,(PetscScalar**)vals);
531:   if (ncols) *ncols = incols;
532:   PetscLogEventEnd(MAT_GetRow,mat,0,0,0);
533:   return(0);
534: }

536: /*@
537:    MatConjugate - replaces the matrix values with their complex conjugates

539:    Logically Collective on Mat

541:    Input Parameters:
542: .  mat - the matrix

544:    Level: advanced

546: .seealso:  VecConjugate()
547: @*/
548: PetscErrorCode MatConjugate(Mat mat)
549: {
550: #if defined(PETSC_USE_COMPLEX)

555:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
556:   if (!mat->ops->conjugate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not provided for this matrix format, send email to petsc-maint@mcs.anl.gov");
557:   (*mat->ops->conjugate)(mat);
558: #if defined(PETSC_HAVE_CUSP)
559:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
560:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
561:   }
562: #elif defined(PETSC_HAVE_VIENNACL)
563:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
564:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
565:   }
566: #elif defined(PETSC_HAVE_VECCUDA)
567:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
568:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
569:   }
570: #endif
571:   return(0);
572: #else
573:   return 0;
574: #endif
575: }

577: /*@C
578:    MatRestoreRow - Frees any temporary space allocated by MatGetRow().

580:    Not Collective

582:    Input Parameters:
583: +  mat - the matrix
584: .  row - the row to get
585: .  ncols, cols - the number of nonzeros and their columns
586: -  vals - if nonzero the column values

588:    Notes:
589:    This routine should be called after you have finished examining the entries.

591:    This routine zeros out ncols, cols, and vals. This is to prevent accidental
592:    us of the array after it has been restored. If you pass NULL, it will
593:    not zero the pointers.  Use of cols or vals after MatRestoreRow is invalid.

595:    Fortran Notes:
596:    The calling sequence from Fortran is
597: .vb
598:    MatRestoreRow(matrix,row,ncols,cols,values,ierr)
599:       Mat     matrix (input)
600:       integer row    (input)
601:       integer ncols  (output)
602:       integer cols(maxcols) (output)
603:       double precision (or double complex) values(maxcols) output
604: .ve
605:    Where maxcols >= maximum nonzeros in any row of the matrix.

607:    In Fortran MatRestoreRow() MUST be called after MatGetRow()
608:    before another call to MatGetRow() can be made.

610:    Level: advanced

612: .seealso:  MatGetRow()
613: @*/
614: PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
615: {

621:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
622:   if (!mat->ops->restorerow) return(0);
623:   (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);
624:   if (ncols) *ncols = 0;
625:   if (cols)  *cols = NULL;
626:   if (vals)  *vals = NULL;
627:   return(0);
628: }

630: /*@
631:    MatGetRowUpperTriangular - Sets a flag to enable calls to MatGetRow() for matrix in MATSBAIJ format.
632:    You should call MatRestoreRowUpperTriangular() after calling MatGetRow/MatRestoreRow() to disable the flag.

634:    Not Collective

636:    Input Parameters:
637: +  mat - the matrix

639:    Notes:
640:    The flag is to ensure that users are aware of MatGetRow() only provides the upper trianglular part of the row for the matrices in MATSBAIJ format.

642:    Level: advanced

644:    Concepts: matrices^row access

646: .seealso: MatRestoreRowRowUpperTriangular()
647: @*/
648: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
649: {

655:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
656:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
657:   if (!mat->ops->getrowuppertriangular) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
658:   MatCheckPreallocated(mat,1);
659:   (*mat->ops->getrowuppertriangular)(mat);
660:   return(0);
661: }

663: /*@
664:    MatRestoreRowUpperTriangular - Disable calls to MatGetRow() for matrix in MATSBAIJ format.

666:    Not Collective

668:    Input Parameters:
669: +  mat - the matrix

671:    Notes:
672:    This routine should be called after you have finished MatGetRow/MatRestoreRow().


675:    Level: advanced

677: .seealso:  MatGetRowUpperTriangular()
678: @*/
679: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
680: {

685:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
686:   if (!mat->ops->restorerowuppertriangular) return(0);
687:   (*mat->ops->restorerowuppertriangular)(mat);
688:   return(0);
689: }

691: /*@C
692:    MatSetOptionsPrefix - Sets the prefix used for searching for all
693:    Mat options in the database.

695:    Logically Collective on Mat

697:    Input Parameter:
698: +  A - the Mat context
699: -  prefix - the prefix to prepend to all option names

701:    Notes:
702:    A hyphen (-) must NOT be given at the beginning of the prefix name.
703:    The first character of all runtime options is AUTOMATICALLY the hyphen.

705:    Level: advanced

707: .keywords: Mat, set, options, prefix, database

709: .seealso: MatSetFromOptions()
710: @*/
711: PetscErrorCode MatSetOptionsPrefix(Mat A,const char prefix[])
712: {

717:   PetscObjectSetOptionsPrefix((PetscObject)A,prefix);
718:   return(0);
719: }

721: /*@C
722:    MatAppendOptionsPrefix - Appends to the prefix used for searching for all
723:    Mat options in the database.

725:    Logically Collective on Mat

727:    Input Parameters:
728: +  A - the Mat context
729: -  prefix - the prefix to prepend to all option names

731:    Notes:
732:    A hyphen (-) must NOT be given at the beginning of the prefix name.
733:    The first character of all runtime options is AUTOMATICALLY the hyphen.

735:    Level: advanced

737: .keywords: Mat, append, options, prefix, database

739: .seealso: MatGetOptionsPrefix()
740: @*/
741: PetscErrorCode MatAppendOptionsPrefix(Mat A,const char prefix[])
742: {

747:   PetscObjectAppendOptionsPrefix((PetscObject)A,prefix);
748:   return(0);
749: }

751: /*@C
752:    MatGetOptionsPrefix - Sets the prefix used for searching for all
753:    Mat options in the database.

755:    Not Collective

757:    Input Parameter:
758: .  A - the Mat context

760:    Output Parameter:
761: .  prefix - pointer to the prefix string used

763:    Notes: On the fortran side, the user should pass in a string 'prefix' of
764:    sufficient length to hold the prefix.

766:    Level: advanced

768: .keywords: Mat, get, options, prefix, database

770: .seealso: MatAppendOptionsPrefix()
771: @*/
772: PetscErrorCode MatGetOptionsPrefix(Mat A,const char *prefix[])
773: {

778:   PetscObjectGetOptionsPrefix((PetscObject)A,prefix);
779:   return(0);
780: }

782: /*@
783:    MatSetUp - Sets up the internal matrix data structures for the later use.

785:    Collective on Mat

787:    Input Parameters:
788: .  A - the Mat context

790:    Notes:
791:    If the user has not set preallocation for this matrix then a default preallocation that is likely to be inefficient is used.

793:    If a suitable preallocation routine is used, this function does not need to be called.

795:    See the Performance chapter of the PETSc users manual for how to preallocate matrices

797:    Level: beginner

799: .keywords: Mat, setup

801: .seealso: MatCreate(), MatDestroy()
802: @*/
803: PetscErrorCode MatSetUp(Mat A)
804: {
805:   PetscMPIInt    size;

810:   if (!((PetscObject)A)->type_name) {
811:     MPI_Comm_size(PetscObjectComm((PetscObject)A), &size);
812:     if (size == 1) {
813:       MatSetType(A, MATSEQAIJ);
814:     } else {
815:       MatSetType(A, MATMPIAIJ);
816:     }
817:   }
818:   if (!A->preallocated && A->ops->setup) {
819:     PetscInfo(A,"Warning not preallocating matrix storage\n");
820:     (*A->ops->setup)(A);
821:   }
822:   if (A->rmap->n < 0 || A->rmap->N < 0) {
823:     PetscLayoutSetUp(A->rmap);
824:   }
825:   if (A->cmap->n < 0 || A->cmap->N < 0) {
826:     PetscLayoutSetUp(A->cmap);
827:   }
828:   A->preallocated = PETSC_TRUE;
829:   return(0);
830: }

832: #if defined(PETSC_HAVE_SAWS)
833:  #include <petscviewersaws.h>
834: #endif
835: /*@C
836:    MatView - Visualizes a matrix object.

838:    Collective on Mat

840:    Input Parameters:
841: +  mat - the matrix
842: -  viewer - visualization context

844:   Notes:
845:   The available visualization contexts include
846: +    PETSC_VIEWER_STDOUT_SELF - for sequential matrices
847: .    PETSC_VIEWER_STDOUT_WORLD - for parallel matrices created on PETSC_COMM_WORLD
848: .    PETSC_VIEWER_STDOUT_(comm) - for matrices created on MPI communicator comm
849: -     PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure

851:    The user can open alternative visualization contexts with
852: +    PetscViewerASCIIOpen() - Outputs matrix to a specified file
853: .    PetscViewerBinaryOpen() - Outputs matrix in binary to a
854:          specified file; corresponding input uses MatLoad()
855: .    PetscViewerDrawOpen() - Outputs nonzero matrix structure to
856:          an X window display
857: -    PetscViewerSocketOpen() - Outputs matrix to Socket viewer.
858:          Currently only the sequential dense and AIJ
859:          matrix types support the Socket viewer.

861:    The user can call PetscViewerPushFormat() to specify the output
862:    format of ASCII printed objects (when using PETSC_VIEWER_STDOUT_SELF,
863:    PETSC_VIEWER_STDOUT_WORLD and PetscViewerASCIIOpen).  Available formats include
864: +    PETSC_VIEWER_DEFAULT - default, prints matrix contents
865: .    PETSC_VIEWER_ASCII_MATLAB - prints matrix contents in Matlab format
866: .    PETSC_VIEWER_ASCII_DENSE - prints entire matrix including zeros
867: .    PETSC_VIEWER_ASCII_COMMON - prints matrix contents, using a sparse
868:          format common among all matrix types
869: .    PETSC_VIEWER_ASCII_IMPL - prints matrix contents, using an implementation-specific
870:          format (which is in many cases the same as the default)
871: .    PETSC_VIEWER_ASCII_INFO - prints basic information about the matrix
872:          size and structure (not the matrix entries)
873: .    PETSC_VIEWER_ASCII_INFO_DETAIL - prints more detailed information about
874:          the matrix structure

876:    Options Database Keys:
877: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of MatAssemblyEnd()
878: .  -mat_view ::ascii_info_detail - Prints more detailed info
879: .  -mat_view - Prints matrix in ASCII format
880: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
881: .  -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX().
882: .  -display <name> - Sets display name (default is host)
883: .  -draw_pause <sec> - Sets number of seconds to pause after display
884: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (see Users-Manual: Chapter 12 Using MATLAB with PETSc for details)
885: .  -viewer_socket_machine <machine> -
886: .  -viewer_socket_port <port> -
887: .  -mat_view binary - save matrix to file in binary format
888: -  -viewer_binary_filename <name> -
889:    Level: beginner

891:    Notes: see the manual page for MatLoad() for the exact format of the binary file when the binary
892:       viewer is used.

894:       See share/petsc/matlab/PetscBinaryRead.m for a Matlab code that can read in the binary file when the binary
895:       viewer is used.

897:       One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure.
898:       And then use the following mouse functions:
899:           left mouse: zoom in
900:           middle mouse: zoom out
901:           right mouse: continue with the simulation

903:    Concepts: matrices^viewing
904:    Concepts: matrices^plotting
905:    Concepts: matrices^printing

907: .seealso: PetscViewerPushFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(),
908:           PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad()
909: @*/
910: PetscErrorCode MatView(Mat mat,PetscViewer viewer)
911: {
912:   PetscErrorCode    ierr;
913:   PetscInt          rows,cols,rbs,cbs;
914:   PetscBool         iascii,ibinary;
915:   PetscViewerFormat format;
916: #if defined(PETSC_HAVE_SAWS)
917:   PetscBool         issaws;
918: #endif

923:   if (!viewer) {
924:     PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat),&viewer);
925:   }
928:   MatCheckPreallocated(mat,1);
929:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&ibinary);
930:   if (ibinary) {
931:     PetscBool mpiio;
932:     PetscViewerBinaryGetUseMPIIO(viewer,&mpiio);
933:     if (mpiio) SETERRQ(PetscObjectComm((PetscObject)viewer),PETSC_ERR_SUP,"PETSc matrix viewers do not support using MPI-IO, turn off that flag");
934:   }

936:   PetscLogEventBegin(MAT_View,mat,viewer,0,0);
937:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
938:   PetscViewerGetFormat(viewer,&format);
939:   if ((!iascii || (format != PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) && mat->factortype) {
940:     SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"No viewers for factored matrix except ASCII info or info_detailed");
941:   }

943: #if defined(PETSC_HAVE_SAWS)
944:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSAWS,&issaws);
945: #endif
946:   if (iascii) {
947:     if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix");
948:     PetscObjectPrintClassNamePrefixType((PetscObject)mat,viewer);
949:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
950:       PetscViewerASCIIPushTab(viewer);
951:       MatGetSize(mat,&rows,&cols);
952:       MatGetBlockSizes(mat,&rbs,&cbs);
953:       if (rbs != 1 || cbs != 1) {
954:         if (rbs != cbs) {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, rbs=%D, cbs = %D\n",rows,cols,rbs,cbs);}
955:         else            {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, bs=%D\n",rows,cols,rbs);}
956:       } else {
957:         PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D\n",rows,cols);
958:       }
959:       if (mat->factortype) {
960:         const MatSolverPackage solver;
961:         MatFactorGetSolverPackage(mat,&solver);
962:         PetscViewerASCIIPrintf(viewer,"package used to perform factorization: %s\n",solver);
963:       }
964:       if (mat->ops->getinfo) {
965:         MatInfo info;
966:         MatGetInfo(mat,MAT_GLOBAL_SUM,&info);
967:         PetscViewerASCIIPrintf(viewer,"total: nonzeros=%.f, allocated nonzeros=%.f\n",info.nz_used,info.nz_allocated);
968:         PetscViewerASCIIPrintf(viewer,"total number of mallocs used during MatSetValues calls =%D\n",(PetscInt)info.mallocs);
969:       }
970:       if (mat->nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached null space\n");}
971:       if (mat->nearnullsp) {PetscViewerASCIIPrintf(viewer,"  has attached near null space\n");}
972:     }
973: #if defined(PETSC_HAVE_SAWS)
974:   } else if (issaws) {
975:     PetscMPIInt rank;

977:     PetscObjectName((PetscObject)mat);
978:     MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
979:     if (!((PetscObject)mat)->amsmem && !rank) {
980:       PetscObjectViewSAWs((PetscObject)mat,viewer);
981:     }
982: #endif
983:   }
984:   if (mat->ops->view) {
985:     PetscViewerASCIIPushTab(viewer);
986:     (*mat->ops->view)(mat,viewer);
987:     PetscViewerASCIIPopTab(viewer);
988:   }
989:   if (iascii) {
990:     if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix");
991:     PetscViewerGetFormat(viewer,&format);
992:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
993:       PetscViewerASCIIPopTab(viewer);
994:     }
995:   }
996:   PetscLogEventEnd(MAT_View,mat,viewer,0,0);
997:   return(0);
998: }

1000: #if defined(PETSC_USE_DEBUG)
1001: #include <../src/sys/totalview/tv_data_display.h>
1002: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1003: {
1004:   TV_add_row("Local rows", "int", &mat->rmap->n);
1005:   TV_add_row("Local columns", "int", &mat->cmap->n);
1006:   TV_add_row("Global rows", "int", &mat->rmap->N);
1007:   TV_add_row("Global columns", "int", &mat->cmap->N);
1008:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1009:   return TV_format_OK;
1010: }
1011: #endif

1013: /*@C
1014:    MatLoad - Loads a matrix that has been stored in binary format
1015:    with MatView().  The matrix format is determined from the options database.
1016:    Generates a parallel MPI matrix if the communicator has more than one
1017:    processor.  The default matrix type is AIJ.

1019:    Collective on PetscViewer

1021:    Input Parameters:
1022: +  newmat - the newly loaded matrix, this needs to have been created with MatCreate()
1023:             or some related function before a call to MatLoad()
1024: -  viewer - binary file viewer, created with PetscViewerBinaryOpen()

1026:    Options Database Keys:
1027:    Used with block matrix formats (MATSEQBAIJ,  ...) to specify
1028:    block size
1029: .    -matload_block_size <bs>

1031:    Level: beginner

1033:    Notes:
1034:    If the Mat type has not yet been given then MATAIJ is used, call MatSetFromOptions() on the
1035:    Mat before calling this routine if you wish to set it from the options database.

1037:    MatLoad() automatically loads into the options database any options
1038:    given in the file filename.info where filename is the name of the file
1039:    that was passed to the PetscViewerBinaryOpen(). The options in the info
1040:    file will be ignored if you use the -viewer_binary_skip_info option.

1042:    If the type or size of newmat is not set before a call to MatLoad, PETSc
1043:    sets the default matrix type AIJ and sets the local and global sizes.
1044:    If type and/or size is already set, then the same are used.

1046:    In parallel, each processor can load a subset of rows (or the
1047:    entire matrix).  This routine is especially useful when a large
1048:    matrix is stored on disk and only part of it is desired on each
1049:    processor.  For example, a parallel solver may access only some of
1050:    the rows from each processor.  The algorithm used here reads
1051:    relatively small blocks of data rather than reading the entire
1052:    matrix and then subsetting it.

1054:    Notes for advanced users:
1055:    Most users should not need to know the details of the binary storage
1056:    format, since MatLoad() and MatView() completely hide these details.
1057:    But for anyone who's interested, the standard binary matrix storage
1058:    format is

1060: $    int    MAT_FILE_CLASSID
1061: $    int    number of rows
1062: $    int    number of columns
1063: $    int    total number of nonzeros
1064: $    int    *number nonzeros in each row
1065: $    int    *column indices of all nonzeros (starting index is zero)
1066: $    PetscScalar *values of all nonzeros

1068:    PETSc automatically does the byte swapping for
1069: machines that store the bytes reversed, e.g.  DEC alpha, freebsd,
1070: linux, Windows and the paragon; thus if you write your own binary
1071: read/write routines you have to swap the bytes; see PetscBinaryRead()
1072: and PetscBinaryWrite() to see how this may be done.

1074: .keywords: matrix, load, binary, input

1076: .seealso: PetscViewerBinaryOpen(), MatView(), VecLoad()

1078:  @*/
1079: PetscErrorCode MatLoad(Mat newmat,PetscViewer viewer)
1080: {
1082:   PetscBool      isbinary,flg;

1087:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1088:   if (!isbinary) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid viewer; open viewer with PetscViewerBinaryOpen()");

1090:   if (!((PetscObject)newmat)->type_name) {
1091:     MatSetType(newmat,MATAIJ);
1092:   }

1094:   if (!newmat->ops->load) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatLoad is not supported for type");
1095:   PetscLogEventBegin(MAT_Load,viewer,0,0,0);
1096:   (*newmat->ops->load)(newmat,viewer);
1097:   PetscLogEventEnd(MAT_Load,viewer,0,0,0);

1099:   flg  = PETSC_FALSE;
1100:   PetscOptionsGetBool(((PetscObject)newmat)->options,((PetscObject)newmat)->prefix,"-matload_symmetric",&flg,NULL);
1101:   if (flg) {
1102:     MatSetOption(newmat,MAT_SYMMETRIC,PETSC_TRUE);
1103:     MatSetOption(newmat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE);
1104:   }
1105:   flg  = PETSC_FALSE;
1106:   PetscOptionsGetBool(((PetscObject)newmat)->options,((PetscObject)newmat)->prefix,"-matload_spd",&flg,NULL);
1107:   if (flg) {
1108:     MatSetOption(newmat,MAT_SPD,PETSC_TRUE);
1109:   }
1110:   return(0);
1111: }

1113: PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1114: {
1116:   Mat_Redundant  *redund = *redundant;
1117:   PetscInt       i;

1120:   if (redund){
1121:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1122:       ISDestroy(&redund->isrow);
1123:       ISDestroy(&redund->iscol);
1124:       MatDestroySubMatrices(1,&redund->matseq);
1125:     } else {
1126:       PetscFree2(redund->send_rank,redund->recv_rank);
1127:       PetscFree(redund->sbuf_j);
1128:       PetscFree(redund->sbuf_a);
1129:       for (i=0; i<redund->nrecvs; i++) {
1130:         PetscFree(redund->rbuf_j[i]);
1131:         PetscFree(redund->rbuf_a[i]);
1132:       }
1133:       PetscFree4(redund->sbuf_nz,redund->rbuf_nz,redund->rbuf_j,redund->rbuf_a);
1134:     }

1136:     if (redund->subcomm) {
1137:       PetscCommDestroy(&redund->subcomm);
1138:     }
1139:     PetscFree(redund);
1140:   }
1141:   return(0);
1142: }

1144: /*@
1145:    MatDestroy - Frees space taken by a matrix.

1147:    Collective on Mat

1149:    Input Parameter:
1150: .  A - the matrix

1152:    Level: beginner

1154: @*/
1155: PetscErrorCode MatDestroy(Mat *A)
1156: {

1160:   if (!*A) return(0);
1162:   if (--((PetscObject)(*A))->refct > 0) {*A = NULL; return(0);}

1164:   /* if memory was published with SAWs then destroy it */
1165:   PetscObjectSAWsViewOff((PetscObject)*A);
1166:   if ((*A)->ops->destroy) {
1167:     (*(*A)->ops->destroy)(*A);
1168:   }

1170:   PetscFree((*A)->solvertype);
1171:   MatDestroy_Redundant(&(*A)->redundant);
1172:   MatNullSpaceDestroy(&(*A)->nullsp);
1173:   MatNullSpaceDestroy(&(*A)->transnullsp);
1174:   MatNullSpaceDestroy(&(*A)->nearnullsp);
1175:   MatDestroy(&(*A)->schur);
1176:   PetscLayoutDestroy(&(*A)->rmap);
1177:   PetscLayoutDestroy(&(*A)->cmap);
1178:   PetscHeaderDestroy(A);
1179:   return(0);
1180: }

1182: /*@C
1183:    MatSetValues - Inserts or adds a block of values into a matrix.
1184:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
1185:    MUST be called after all calls to MatSetValues() have been completed.

1187:    Not Collective

1189:    Input Parameters:
1190: +  mat - the matrix
1191: .  v - a logically two-dimensional array of values
1192: .  m, idxm - the number of rows and their global indices
1193: .  n, idxn - the number of columns and their global indices
1194: -  addv - either ADD_VALUES or INSERT_VALUES, where
1195:    ADD_VALUES adds values to any existing entries, and
1196:    INSERT_VALUES replaces existing entries with new values

1198:    Notes:
1199:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
1200:       MatSetUp() before using this routine

1202:    By default the values, v, are row-oriented. See MatSetOption() for other options.

1204:    Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES
1205:    options cannot be mixed without intervening calls to the assembly
1206:    routines.

1208:    MatSetValues() uses 0-based row and column numbers in Fortran
1209:    as well as in C.

1211:    Negative indices may be passed in idxm and idxn, these rows and columns are
1212:    simply ignored. This allows easily inserting element stiffness matrices
1213:    with homogeneous Dirchlet boundary conditions that you don't want represented
1214:    in the matrix.

1216:    Efficiency Alert:
1217:    The routine MatSetValuesBlocked() may offer much better efficiency
1218:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1220:    Level: beginner

1222:    Developer Notes: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1223:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1225:    Concepts: matrices^putting entries in

1227: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1228:           InsertMode, INSERT_VALUES, ADD_VALUES
1229: @*/
1230: PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1231: {
1233: #if defined(PETSC_USE_DEBUG)
1234:   PetscInt       i,j;
1235: #endif

1240:   if (!m || !n) return(0); /* no values to insert */
1244:   MatCheckPreallocated(mat,1);
1245:   if (mat->insertmode == NOT_SET_VALUES) {
1246:     mat->insertmode = addv;
1247:   }
1248: #if defined(PETSC_USE_DEBUG)
1249:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1250:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1251:   if (!mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);

1253:   for (i=0; i<m; i++) {
1254:     for (j=0; j<n; j++) {
1255:       if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i*n+j]))
1256: #if defined(PETSC_USE_COMPLEX)
1257:         SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %g+ig at matrix entry (%D,%D)",(double)PetscRealPart(v[i*n+j]),(double)PetscImaginaryPart(v[i*n+j]),idxm[i],idxn[j]);
1258: #else
1259:         SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %g at matrix entry (%D,%D)",(double)v[i*n+j],idxm[i],idxn[j]);
1260: #endif
1261:     }
1262:   }
1263: #endif

1265:   if (mat->assembled) {
1266:     mat->was_assembled = PETSC_TRUE;
1267:     mat->assembled     = PETSC_FALSE;
1268:   }
1269:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1270:   (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);
1271:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1272: #if defined(PETSC_HAVE_CUSP)
1273:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
1274:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
1275:   }
1276: #elif defined(PETSC_HAVE_VIENNACL)
1277:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
1278:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
1279:   }
1280: #elif defined(PETSC_HAVE_VECCUDA)
1281:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
1282:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
1283:   }
1284: #endif
1285:   return(0);
1286: }


1289: /*@
1290:    MatSetValuesRowLocal - Inserts a row (block row for BAIJ matrices) of nonzero
1291:         values into a matrix

1293:    Not Collective

1295:    Input Parameters:
1296: +  mat - the matrix
1297: .  row - the (block) row to set
1298: -  v - a logically two-dimensional array of values

1300:    Notes:
1301:    By the values, v, are column-oriented (for the block version) and sorted

1303:    All the nonzeros in the row must be provided

1305:    The matrix must have previously had its column indices set

1307:    The row must belong to this process

1309:    Level: intermediate

1311:    Concepts: matrices^putting entries in

1313: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1314:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues(), MatSetValuesRow(), MatSetLocalToGlobalMapping()
1315: @*/
1316: PetscErrorCode MatSetValuesRowLocal(Mat mat,PetscInt row,const PetscScalar v[])
1317: {
1319:   PetscInt       globalrow;

1325:   ISLocalToGlobalMappingApply(mat->rmap->mapping,1,&row,&globalrow);
1326:   MatSetValuesRow(mat,globalrow,v);
1327: #if defined(PETSC_HAVE_CUSP)
1328:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
1329:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
1330:   }
1331: #elif defined(PETSC_HAVE_VIENNACL)
1332:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
1333:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
1334:   }
1335: #elif defined(PETSC_HAVE_VECCUDA)
1336:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
1337:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
1338:   }
1339: #endif
1340:   return(0);
1341: }

1343: /*@
1344:    MatSetValuesRow - Inserts a row (block row for BAIJ matrices) of nonzero
1345:         values into a matrix

1347:    Not Collective

1349:    Input Parameters:
1350: +  mat - the matrix
1351: .  row - the (block) row to set
1352: -  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

1354:    Notes:
1355:    The values, v, are column-oriented for the block version.

1357:    All the nonzeros in the row must be provided

1359:    THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually MatSetValues() is used.

1361:    The row must belong to this process

1363:    Level: advanced

1365:    Concepts: matrices^putting entries in

1367: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1368:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
1369: @*/
1370: PetscErrorCode MatSetValuesRow(Mat mat,PetscInt row,const PetscScalar v[])
1371: {

1377:   MatCheckPreallocated(mat,1);
1379: #if defined(PETSC_USE_DEBUG)
1380:   if (mat->insertmode == ADD_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add and insert values");
1381:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1382: #endif
1383:   mat->insertmode = INSERT_VALUES;

1385:   if (mat->assembled) {
1386:     mat->was_assembled = PETSC_TRUE;
1387:     mat->assembled     = PETSC_FALSE;
1388:   }
1389:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1390:   if (!mat->ops->setvaluesrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1391:   (*mat->ops->setvaluesrow)(mat,row,v);
1392:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1393: #if defined(PETSC_HAVE_CUSP)
1394:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
1395:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
1396:   }
1397: #elif defined(PETSC_HAVE_VIENNACL)
1398:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
1399:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
1400:   }
1401: #elif defined(PETSC_HAVE_VECCUDA)
1402:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
1403:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
1404:   }
1405: #endif
1406:   return(0);
1407: }

1409: /*@
1410:    MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1411:      Using structured grid indexing

1413:    Not Collective

1415:    Input Parameters:
1416: +  mat - the matrix
1417: .  m - number of rows being entered
1418: .  idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1419: .  n - number of columns being entered
1420: .  idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1421: .  v - a logically two-dimensional array of values
1422: -  addv - either ADD_VALUES or INSERT_VALUES, where
1423:    ADD_VALUES adds values to any existing entries, and
1424:    INSERT_VALUES replaces existing entries with new values

1426:    Notes:
1427:    By default the values, v, are row-oriented.  See MatSetOption() for other options.

1429:    Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES
1430:    options cannot be mixed without intervening calls to the assembly
1431:    routines.

1433:    The grid coordinates are across the entire grid, not just the local portion

1435:    MatSetValuesStencil() uses 0-based row and column numbers in Fortran
1436:    as well as in C.

1438:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1440:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1441:    or call MatSetLocalToGlobalMapping() and MatSetStencil() first.

1443:    The columns and rows in the stencil passed in MUST be contained within the
1444:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1445:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1446:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1447:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1449:    In Fortran idxm and idxn should be declared as
1450: $     MatStencil idxm(4,m),idxn(4,n)
1451:    and the values inserted using
1452: $    idxm(MatStencil_i,1) = i
1453: $    idxm(MatStencil_j,1) = j
1454: $    idxm(MatStencil_k,1) = k
1455: $    idxm(MatStencil_c,1) = c
1456:    etc

1458:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1459:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1460:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1461:    DM_BOUNDARY_PERIODIC boundary type.

1463:    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
1464:    a single value per point) you can skip filling those indices.

1466:    Inspired by the structured grid interface to the HYPRE package
1467:    (http://www.llnl.gov/CASC/hypre)

1469:    Efficiency Alert:
1470:    The routine MatSetValuesBlockedStencil() may offer much better efficiency
1471:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1473:    Level: beginner

1475:    Concepts: matrices^putting entries in

1477: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1478:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil
1479: @*/
1480: PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1481: {
1483:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1484:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1485:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1488:   if (!m || !n) return(0); /* no values to insert */

1495:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1496:     jdxm = buf; jdxn = buf+m;
1497:   } else {
1498:     PetscMalloc2(m,&bufm,n,&bufn);
1499:     jdxm = bufm; jdxn = bufn;
1500:   }
1501:   for (i=0; i<m; i++) {
1502:     for (j=0; j<3-sdim; j++) dxm++;
1503:     tmp = *dxm++ - starts[0];
1504:     for (j=0; j<dim-1; j++) {
1505:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1506:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1507:     }
1508:     if (mat->stencil.noc) dxm++;
1509:     jdxm[i] = tmp;
1510:   }
1511:   for (i=0; i<n; i++) {
1512:     for (j=0; j<3-sdim; j++) dxn++;
1513:     tmp = *dxn++ - starts[0];
1514:     for (j=0; j<dim-1; j++) {
1515:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1516:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1517:     }
1518:     if (mat->stencil.noc) dxn++;
1519:     jdxn[i] = tmp;
1520:   }
1521:   MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);
1522:   PetscFree2(bufm,bufn);
1523:   return(0);
1524: }

1526: /*@
1527:    MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1528:      Using structured grid indexing

1530:    Not Collective

1532:    Input Parameters:
1533: +  mat - the matrix
1534: .  m - number of rows being entered
1535: .  idxm - grid coordinates for matrix rows being entered
1536: .  n - number of columns being entered
1537: .  idxn - grid coordinates for matrix columns being entered
1538: .  v - a logically two-dimensional array of values
1539: -  addv - either ADD_VALUES or INSERT_VALUES, where
1540:    ADD_VALUES adds values to any existing entries, and
1541:    INSERT_VALUES replaces existing entries with new values

1543:    Notes:
1544:    By default the values, v, are row-oriented and unsorted.
1545:    See MatSetOption() for other options.

1547:    Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES
1548:    options cannot be mixed without intervening calls to the assembly
1549:    routines.

1551:    The grid coordinates are across the entire grid, not just the local portion

1553:    MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran
1554:    as well as in C.

1556:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1558:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1559:    or call MatSetBlockSize(), MatSetLocalToGlobalMapping() and MatSetStencil() first.

1561:    The columns and rows in the stencil passed in MUST be contained within the
1562:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1563:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1564:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1565:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1567:    In Fortran idxm and idxn should be declared as
1568: $     MatStencil idxm(4,m),idxn(4,n)
1569:    and the values inserted using
1570: $    idxm(MatStencil_i,1) = i
1571: $    idxm(MatStencil_j,1) = j
1572: $    idxm(MatStencil_k,1) = k
1573:    etc

1575:    Negative indices may be passed in idxm and idxn, these rows and columns are
1576:    simply ignored. This allows easily inserting element stiffness matrices
1577:    with homogeneous Dirchlet boundary conditions that you don't want represented
1578:    in the matrix.

1580:    Inspired by the structured grid interface to the HYPRE package
1581:    (http://www.llnl.gov/CASC/hypre)

1583:    Level: beginner

1585:    Concepts: matrices^putting entries in

1587: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1588:           MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil,
1589:           MatSetBlockSize(), MatSetLocalToGlobalMapping()
1590: @*/
1591: PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1592: {
1594:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1595:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1596:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1599:   if (!m || !n) return(0); /* no values to insert */

1606:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1607:     jdxm = buf; jdxn = buf+m;
1608:   } else {
1609:     PetscMalloc2(m,&bufm,n,&bufn);
1610:     jdxm = bufm; jdxn = bufn;
1611:   }
1612:   for (i=0; i<m; i++) {
1613:     for (j=0; j<3-sdim; j++) dxm++;
1614:     tmp = *dxm++ - starts[0];
1615:     for (j=0; j<sdim-1; j++) {
1616:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1617:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1618:     }
1619:     dxm++;
1620:     jdxm[i] = tmp;
1621:   }
1622:   for (i=0; i<n; i++) {
1623:     for (j=0; j<3-sdim; j++) dxn++;
1624:     tmp = *dxn++ - starts[0];
1625:     for (j=0; j<sdim-1; j++) {
1626:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1627:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1628:     }
1629:     dxn++;
1630:     jdxn[i] = tmp;
1631:   }
1632:   MatSetValuesBlockedLocal(mat,m,jdxm,n,jdxn,v,addv);
1633:   PetscFree2(bufm,bufn);
1634: #if defined(PETSC_HAVE_CUSP)
1635:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
1636:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
1637:   }
1638: #elif defined(PETSC_HAVE_VIENNACL)
1639:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
1640:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
1641:   }
1642: #elif defined(PETSC_HAVE_VECCUDA)
1643:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
1644:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
1645:   }
1646: #endif
1647:   return(0);
1648: }

1650: /*@
1651:    MatSetStencil - Sets the grid information for setting values into a matrix via
1652:         MatSetValuesStencil()

1654:    Not Collective

1656:    Input Parameters:
1657: +  mat - the matrix
1658: .  dim - dimension of the grid 1, 2, or 3
1659: .  dims - number of grid points in x, y, and z direction, including ghost points on your processor
1660: .  starts - starting point of ghost nodes on your processor in x, y, and z direction
1661: -  dof - number of degrees of freedom per node


1664:    Inspired by the structured grid interface to the HYPRE package
1665:    (www.llnl.gov/CASC/hyper)

1667:    For matrices generated with DMCreateMatrix() this routine is automatically called and so not needed by the
1668:    user.

1670:    Level: beginner

1672:    Concepts: matrices^putting entries in

1674: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1675:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil()
1676: @*/
1677: PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof)
1678: {
1679:   PetscInt i;


1686:   mat->stencil.dim = dim + (dof > 1);
1687:   for (i=0; i<dim; i++) {
1688:     mat->stencil.dims[i]   = dims[dim-i-1];      /* copy the values in backwards */
1689:     mat->stencil.starts[i] = starts[dim-i-1];
1690:   }
1691:   mat->stencil.dims[dim]   = dof;
1692:   mat->stencil.starts[dim] = 0;
1693:   mat->stencil.noc         = (PetscBool)(dof == 1);
1694:   return(0);
1695: }

1697: /*@C
1698:    MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1700:    Not Collective

1702:    Input Parameters:
1703: +  mat - the matrix
1704: .  v - a logically two-dimensional array of values
1705: .  m, idxm - the number of block rows and their global block indices
1706: .  n, idxn - the number of block columns and their global block indices
1707: -  addv - either ADD_VALUES or INSERT_VALUES, where
1708:    ADD_VALUES adds values to any existing entries, and
1709:    INSERT_VALUES replaces existing entries with new values

1711:    Notes:
1712:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call
1713:    MatXXXXSetPreallocation() or MatSetUp() before using this routine.

1715:    The m and n count the NUMBER of blocks in the row direction and column direction,
1716:    NOT the total number of rows/columns; for example, if the block size is 2 and
1717:    you are passing in values for rows 2,3,4,5  then m would be 2 (not 4).
1718:    The values in idxm would be 1 2; that is the first index for each block divided by
1719:    the block size.

1721:    Note that you must call MatSetBlockSize() when constructing this matrix (before
1722:    preallocating it).

1724:    By default the values, v, are row-oriented, so the layout of
1725:    v is the same as for MatSetValues(). See MatSetOption() for other options.

1727:    Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES
1728:    options cannot be mixed without intervening calls to the assembly
1729:    routines.

1731:    MatSetValuesBlocked() uses 0-based row and column numbers in Fortran
1732:    as well as in C.

1734:    Negative indices may be passed in idxm and idxn, these rows and columns are
1735:    simply ignored. This allows easily inserting element stiffness matrices
1736:    with homogeneous Dirchlet boundary conditions that you don't want represented
1737:    in the matrix.

1739:    Each time an entry is set within a sparse matrix via MatSetValues(),
1740:    internal searching must be done to determine where to place the
1741:    data in the matrix storage space.  By instead inserting blocks of
1742:    entries via MatSetValuesBlocked(), the overhead of matrix assembly is
1743:    reduced.

1745:    Example:
1746: $   Suppose m=n=2 and block size(bs) = 2 The array is
1747: $
1748: $   1  2  | 3  4
1749: $   5  6  | 7  8
1750: $   - - - | - - -
1751: $   9  10 | 11 12
1752: $   13 14 | 15 16
1753: $
1754: $   v[] should be passed in like
1755: $   v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
1756: $
1757: $  If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1758: $   v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]

1760:    Level: intermediate

1762:    Concepts: matrices^putting entries in blocked

1764: .seealso: MatSetBlockSize(), MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal()
1765: @*/
1766: PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1767: {

1773:   if (!m || !n) return(0); /* no values to insert */
1777:   MatCheckPreallocated(mat,1);
1778:   if (mat->insertmode == NOT_SET_VALUES) {
1779:     mat->insertmode = addv;
1780:   }
1781: #if defined(PETSC_USE_DEBUG)
1782:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1783:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1784:   if (!mat->ops->setvaluesblocked && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1785: #endif

1787:   if (mat->assembled) {
1788:     mat->was_assembled = PETSC_TRUE;
1789:     mat->assembled     = PETSC_FALSE;
1790:   }
1791:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1792:   if (mat->ops->setvaluesblocked) {
1793:     (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);
1794:   } else {
1795:     PetscInt buf[8192],*bufr=0,*bufc=0,*iidxm,*iidxn;
1796:     PetscInt i,j,bs,cbs;
1797:     MatGetBlockSizes(mat,&bs,&cbs);
1798:     if (m*bs+n*cbs <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1799:       iidxm = buf; iidxn = buf + m*bs;
1800:     } else {
1801:       PetscMalloc2(m*bs,&bufr,n*cbs,&bufc);
1802:       iidxm = bufr; iidxn = bufc;
1803:     }
1804:     for (i=0; i<m; i++) {
1805:       for (j=0; j<bs; j++) {
1806:         iidxm[i*bs+j] = bs*idxm[i] + j;
1807:       }
1808:     }
1809:     for (i=0; i<n; i++) {
1810:       for (j=0; j<cbs; j++) {
1811:         iidxn[i*cbs+j] = cbs*idxn[i] + j;
1812:       }
1813:     }
1814:     MatSetValues(mat,m*bs,iidxm,n*cbs,iidxn,v,addv);
1815:     PetscFree2(bufr,bufc);
1816:   }
1817:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1818: #if defined(PETSC_HAVE_CUSP)
1819:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
1820:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
1821:   }
1822: #elif defined(PETSC_HAVE_VIENNACL)
1823:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
1824:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
1825:   }
1826: #elif defined(PETSC_HAVE_VECCUDA)
1827:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
1828:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
1829:   }
1830: #endif
1831:   return(0);
1832: }

1834: /*@
1835:    MatGetValues - Gets a block of values from a matrix.

1837:    Not Collective; currently only returns a local block

1839:    Input Parameters:
1840: +  mat - the matrix
1841: .  v - a logically two-dimensional array for storing the values
1842: .  m, idxm - the number of rows and their global indices
1843: -  n, idxn - the number of columns and their global indices

1845:    Notes:
1846:    The user must allocate space (m*n PetscScalars) for the values, v.
1847:    The values, v, are then returned in a row-oriented format,
1848:    analogous to that used by default in MatSetValues().

1850:    MatGetValues() uses 0-based row and column numbers in
1851:    Fortran as well as in C.

1853:    MatGetValues() requires that the matrix has been assembled
1854:    with MatAssemblyBegin()/MatAssemblyEnd().  Thus, calls to
1855:    MatSetValues() and MatGetValues() CANNOT be made in succession
1856:    without intermediate matrix assembly.

1858:    Negative row or column indices will be ignored and those locations in v[] will be
1859:    left unchanged.

1861:    Level: advanced

1863:    Concepts: matrices^accessing values

1865: .seealso: MatGetRow(), MatCreateSubMatrices(), MatSetValues()
1866: @*/
1867: PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
1868: {

1874:   if (!m || !n) return(0);
1878:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
1879:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1880:   if (!mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1881:   MatCheckPreallocated(mat,1);

1883:   PetscLogEventBegin(MAT_GetValues,mat,0,0,0);
1884:   (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);
1885:   PetscLogEventEnd(MAT_GetValues,mat,0,0,0);
1886:   return(0);
1887: }

1889: /*@
1890:   MatSetValuesBatch - Adds (ADD_VALUES) many blocks of values into a matrix at once. The blocks must all be square and
1891:   the same size. Currently, this can only be called once and creates the given matrix.

1893:   Not Collective

1895:   Input Parameters:
1896: + mat - the matrix
1897: . nb - the number of blocks
1898: . bs - the number of rows (and columns) in each block
1899: . rows - a concatenation of the rows for each block
1900: - v - a concatenation of logically two-dimensional arrays of values

1902:   Notes:
1903:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

1905:   Level: advanced

1907:   Concepts: matrices^putting entries in

1909: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1910:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
1911: @*/
1912: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
1913: {

1921: #if defined(PETSC_USE_DEBUG)
1922:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1923: #endif

1925:   PetscLogEventBegin(MAT_SetValuesBatch,mat,0,0,0);
1926:   if (mat->ops->setvaluesbatch) {
1927:     (*mat->ops->setvaluesbatch)(mat,nb,bs,rows,v);
1928:   } else {
1929:     PetscInt b;
1930:     for (b = 0; b < nb; ++b) {
1931:       MatSetValues(mat, bs, &rows[b*bs], bs, &rows[b*bs], &v[b*bs*bs], ADD_VALUES);
1932:     }
1933:   }
1934:   PetscLogEventEnd(MAT_SetValuesBatch,mat,0,0,0);
1935:   return(0);
1936: }

1938: /*@
1939:    MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
1940:    the routine MatSetValuesLocal() to allow users to insert matrix entries
1941:    using a local (per-processor) numbering.

1943:    Not Collective

1945:    Input Parameters:
1946: +  x - the matrix
1947: .  rmapping - row mapping created with ISLocalToGlobalMappingCreate()   or ISLocalToGlobalMappingCreateIS()
1948: - cmapping - column mapping

1950:    Level: intermediate

1952:    Concepts: matrices^local to global mapping
1953:    Concepts: local to global mapping^for matrices

1955: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal()
1956: @*/
1957: PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping)
1958: {


1967:   if (x->ops->setlocaltoglobalmapping) {
1968:     (*x->ops->setlocaltoglobalmapping)(x,rmapping,cmapping);
1969:   } else {
1970:     PetscLayoutSetISLocalToGlobalMapping(x->rmap,rmapping);
1971:     PetscLayoutSetISLocalToGlobalMapping(x->cmap,cmapping);
1972:   }
1973:   return(0);
1974: }


1977: /*@
1978:    MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by MatSetLocalToGlobalMapping()

1980:    Not Collective

1982:    Input Parameters:
1983: .  A - the matrix

1985:    Output Parameters:
1986: + rmapping - row mapping
1987: - cmapping - column mapping

1989:    Level: advanced

1991:    Concepts: matrices^local to global mapping
1992:    Concepts: local to global mapping^for matrices

1994: .seealso:  MatSetValuesLocal()
1995: @*/
1996: PetscErrorCode MatGetLocalToGlobalMapping(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping)
1997: {
2003:   if (rmapping) *rmapping = A->rmap->mapping;
2004:   if (cmapping) *cmapping = A->cmap->mapping;
2005:   return(0);
2006: }

2008: /*@
2009:    MatGetLayouts - Gets the PetscLayout objects for rows and columns

2011:    Not Collective

2013:    Input Parameters:
2014: .  A - the matrix

2016:    Output Parameters:
2017: + rmap - row layout
2018: - cmap - column layout

2020:    Level: advanced

2022: .seealso:  MatCreateVecs(), MatGetLocalToGlobalMapping()
2023: @*/
2024: PetscErrorCode MatGetLayouts(Mat A,PetscLayout *rmap,PetscLayout *cmap)
2025: {
2031:   if (rmap) *rmap = A->rmap;
2032:   if (cmap) *cmap = A->cmap;
2033:   return(0);
2034: }

2036: /*@C
2037:    MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2038:    using a local ordering of the nodes.

2040:    Not Collective

2042:    Input Parameters:
2043: +  mat - the matrix
2044: .  nrow, irow - number of rows and their local indices
2045: .  ncol, icol - number of columns and their local indices
2046: .  y -  a logically two-dimensional array of values
2047: -  addv - either INSERT_VALUES or ADD_VALUES, where
2048:    ADD_VALUES adds values to any existing entries, and
2049:    INSERT_VALUES replaces existing entries with new values

2051:    Notes:
2052:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2053:       MatSetUp() before using this routine

2055:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine

2057:    Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES
2058:    options cannot be mixed without intervening calls to the assembly
2059:    routines.

2061:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2062:    MUST be called after all calls to MatSetValuesLocal() have been completed.

2064:    Level: intermediate

2066:    Concepts: matrices^putting entries in with local numbering

2068:    Developer Notes: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2069:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2071: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(),
2072:            MatSetValueLocal()
2073: @*/
2074: PetscErrorCode MatSetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv)
2075: {

2081:   MatCheckPreallocated(mat,1);
2082:   if (!nrow || !ncol) return(0); /* no values to insert */
2086:   if (mat->insertmode == NOT_SET_VALUES) {
2087:     mat->insertmode = addv;
2088:   }
2089: #if defined(PETSC_USE_DEBUG)
2090:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2091:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2092:   if (!mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2093: #endif

2095:   if (mat->assembled) {
2096:     mat->was_assembled = PETSC_TRUE;
2097:     mat->assembled     = PETSC_FALSE;
2098:   }
2099:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2100:   if (mat->ops->setvalueslocal) {
2101:     (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);
2102:   } else {
2103:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2104:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2105:       irowm = buf; icolm = buf+nrow;
2106:     } else {
2107:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2108:       irowm = bufr; icolm = bufc;
2109:     }
2110:     ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);
2111:     ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);
2112:     MatSetValues(mat,nrow,irowm,ncol,icolm,y,addv);
2113:     PetscFree2(bufr,bufc);
2114:   }
2115:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2116: #if defined(PETSC_HAVE_CUSP)
2117:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
2118:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
2119:   }
2120: #elif defined(PETSC_HAVE_VIENNACL)
2121:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
2122:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
2123:   }
2124: #elif defined(PETSC_HAVE_VECCUDA)
2125:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
2126:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
2127:   }
2128: #endif
2129:   return(0);
2130: }

2132: /*@C
2133:    MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2134:    using a local ordering of the nodes a block at a time.

2136:    Not Collective

2138:    Input Parameters:
2139: +  x - the matrix
2140: .  nrow, irow - number of rows and their local indices
2141: .  ncol, icol - number of columns and their local indices
2142: .  y -  a logically two-dimensional array of values
2143: -  addv - either INSERT_VALUES or ADD_VALUES, where
2144:    ADD_VALUES adds values to any existing entries, and
2145:    INSERT_VALUES replaces existing entries with new values

2147:    Notes:
2148:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2149:       MatSetUp() before using this routine

2151:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetBlockSize() and MatSetLocalToGlobalMapping()
2152:       before using this routineBefore calling MatSetValuesLocal(), the user must first set the

2154:    Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES
2155:    options cannot be mixed without intervening calls to the assembly
2156:    routines.

2158:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2159:    MUST be called after all calls to MatSetValuesBlockedLocal() have been completed.

2161:    Level: intermediate

2163:    Developer Notes: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2164:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2166:    Concepts: matrices^putting blocked values in with local numbering

2168: .seealso:  MatSetBlockSize(), MatSetLocalToGlobalMapping(), MatAssemblyBegin(), MatAssemblyEnd(),
2169:            MatSetValuesLocal(),  MatSetValuesBlocked()
2170: @*/
2171: PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv)
2172: {

2178:   MatCheckPreallocated(mat,1);
2179:   if (!nrow || !ncol) return(0); /* no values to insert */
2183:   if (mat->insertmode == NOT_SET_VALUES) {
2184:     mat->insertmode = addv;
2185:   }
2186: #if defined(PETSC_USE_DEBUG)
2187:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2188:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2189:   if (!mat->ops->setvaluesblockedlocal && !mat->ops->setvaluesblocked && !mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2190: #endif

2192:   if (mat->assembled) {
2193:     mat->was_assembled = PETSC_TRUE;
2194:     mat->assembled     = PETSC_FALSE;
2195:   }
2196:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2197:   if (mat->ops->setvaluesblockedlocal) {
2198:     (*mat->ops->setvaluesblockedlocal)(mat,nrow,irow,ncol,icol,y,addv);
2199:   } else {
2200:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2201:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2202:       irowm = buf; icolm = buf + nrow;
2203:     } else {
2204:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2205:       irowm = bufr; icolm = bufc;
2206:     }
2207:     ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping,nrow,irow,irowm);
2208:     ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping,ncol,icol,icolm);
2209:     MatSetValuesBlocked(mat,nrow,irowm,ncol,icolm,y,addv);
2210:     PetscFree2(bufr,bufc);
2211:   }
2212:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2213: #if defined(PETSC_HAVE_CUSP)
2214:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
2215:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
2216:   }
2217: #elif defined(PETSC_HAVE_VIENNACL)
2218:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
2219:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
2220:   }
2221: #elif defined(PETSC_HAVE_VECCUDA)
2222:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
2223:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
2224:   }
2225: #endif
2226:   return(0);
2227: }

2229: /*@
2230:    MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal

2232:    Collective on Mat and Vec

2234:    Input Parameters:
2235: +  mat - the matrix
2236: -  x   - the vector to be multiplied

2238:    Output Parameters:
2239: .  y - the result

2241:    Notes:
2242:    The vectors x and y cannot be the same.  I.e., one cannot
2243:    call MatMult(A,y,y).

2245:    Level: developer

2247:    Concepts: matrix-vector product

2249: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2250: @*/
2251: PetscErrorCode MatMultDiagonalBlock(Mat mat,Vec x,Vec y)
2252: {


2261:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2262:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2263:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2264:   MatCheckPreallocated(mat,1);

2266:   if (!mat->ops->multdiagonalblock) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined");
2267:   (*mat->ops->multdiagonalblock)(mat,x,y);
2268:   PetscObjectStateIncrease((PetscObject)y);
2269:   return(0);
2270: }

2272: /* --------------------------------------------------------*/
2273: /*@
2274:    MatMult - Computes the matrix-vector product, y = Ax.

2276:    Neighbor-wise Collective on Mat and Vec

2278:    Input Parameters:
2279: +  mat - the matrix
2280: -  x   - the vector to be multiplied

2282:    Output Parameters:
2283: .  y - the result

2285:    Notes:
2286:    The vectors x and y cannot be the same.  I.e., one cannot
2287:    call MatMult(A,y,y).

2289:    Level: beginner

2291:    Concepts: matrix-vector product

2293: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2294: @*/
2295: PetscErrorCode MatMult(Mat mat,Vec x,Vec y)
2296: {

2304:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2305:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2306:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2307: #if !defined(PETSC_HAVE_CONSTRAINTS)
2308:   if (mat->cmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
2309:   if (mat->rmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N);
2310:   if (mat->rmap->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->rmap->n,y->map->n);
2311: #endif
2312:   VecLocked(y,3);
2313:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2314:   MatCheckPreallocated(mat,1);

2316:   VecLockPush(x);
2317:   if (!mat->ops->mult) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined");
2318:   PetscLogEventBegin(MAT_Mult,mat,x,y,0);
2319:   (*mat->ops->mult)(mat,x,y);
2320:   PetscLogEventEnd(MAT_Mult,mat,x,y,0);
2321:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2322:   VecLockPop(x);
2323:   return(0);
2324: }

2326: /*@
2327:    MatMultTranspose - Computes matrix transpose times a vector.

2329:    Neighbor-wise Collective on Mat and Vec

2331:    Input Parameters:
2332: +  mat - the matrix
2333: -  x   - the vector to be multilplied

2335:    Output Parameters:
2336: .  y - the result

2338:    Notes:
2339:    The vectors x and y cannot be the same.  I.e., one cannot
2340:    call MatMultTranspose(A,y,y).

2342:    For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2343:    use MatMultHermitianTranspose()

2345:    Level: beginner

2347:    Concepts: matrix vector product^transpose

2349: .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd(), MatMultHermitianTranspose(), MatTranspose()
2350: @*/
2351: PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y)
2352: {


2361:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2362:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2363:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2364: #if !defined(PETSC_HAVE_CONSTRAINTS)
2365:   if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N);
2366:   if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N);
2367: #endif
2368:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2369:   MatCheckPreallocated(mat,1);

2371:   if (!mat->ops->multtranspose) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply tranpose defined");
2372:   PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);
2373:   VecLockPush(x);
2374:   (*mat->ops->multtranspose)(mat,x,y);
2375:   VecLockPop(x);
2376:   PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);
2377:   PetscObjectStateIncrease((PetscObject)y);
2378:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2379:   return(0);
2380: }

2382: /*@
2383:    MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector.

2385:    Neighbor-wise Collective on Mat and Vec

2387:    Input Parameters:
2388: +  mat - the matrix
2389: -  x   - the vector to be multilplied

2391:    Output Parameters:
2392: .  y - the result

2394:    Notes:
2395:    The vectors x and y cannot be the same.  I.e., one cannot
2396:    call MatMultHermitianTranspose(A,y,y).

2398:    Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2400:    For real numbers MatMultTranspose() and MatMultHermitianTranspose() are identical.

2402:    Level: beginner

2404:    Concepts: matrix vector product^transpose

2406: .seealso: MatMult(), MatMultAdd(), MatMultHermitianTransposeAdd(), MatMultTranspose()
2407: @*/
2408: PetscErrorCode MatMultHermitianTranspose(Mat mat,Vec x,Vec y)
2409: {
2411:   Vec            w;


2419:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2420:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2421:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2422: #if !defined(PETSC_HAVE_CONSTRAINTS)
2423:   if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N);
2424:   if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N);
2425: #endif
2426:   MatCheckPreallocated(mat,1);

2428:   PetscLogEventBegin(MAT_MultHermitianTranspose,mat,x,y,0);
2429:   if (mat->ops->multhermitiantranspose) {
2430:     VecLockPush(x);
2431:     (*mat->ops->multhermitiantranspose)(mat,x,y);
2432:     VecLockPop(x);
2433:   } else {
2434:     VecDuplicate(x,&w);
2435:     VecCopy(x,w);
2436:     VecConjugate(w);
2437:     MatMultTranspose(mat,w,y);
2438:     VecDestroy(&w);
2439:     VecConjugate(y);
2440:   }
2441:   PetscLogEventEnd(MAT_MultHermitianTranspose,mat,x,y,0);
2442:   PetscObjectStateIncrease((PetscObject)y);
2443:   return(0);
2444: }

2446: /*@
2447:     MatMultAdd -  Computes v3 = v2 + A * v1.

2449:     Neighbor-wise Collective on Mat and Vec

2451:     Input Parameters:
2452: +   mat - the matrix
2453: -   v1, v2 - the vectors

2455:     Output Parameters:
2456: .   v3 - the result

2458:     Notes:
2459:     The vectors v1 and v3 cannot be the same.  I.e., one cannot
2460:     call MatMultAdd(A,v1,v2,v1).

2462:     Level: beginner

2464:     Concepts: matrix vector product^addition

2466: .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd()
2467: @*/
2468: PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2469: {


2479:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2480:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2481:   if (mat->cmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->cmap->N,v1->map->N);
2482:   /* if (mat->rmap->N != v2->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->rmap->N,v2->map->N);
2483:      if (mat->rmap->N != v3->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->rmap->N,v3->map->N); */
2484:   if (mat->rmap->n != v3->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: local dim %D %D",mat->rmap->n,v3->map->n);
2485:   if (mat->rmap->n != v2->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: local dim %D %D",mat->rmap->n,v2->map->n);
2486:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2487:   MatCheckPreallocated(mat,1);

2489:   if (!mat->ops->multadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No MatMultAdd() for matrix type '%s'",((PetscObject)mat)->type_name);
2490:   PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);
2491:   VecLockPush(v1);
2492:   (*mat->ops->multadd)(mat,v1,v2,v3);
2493:   VecLockPop(v1);
2494:   PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);
2495:   PetscObjectStateIncrease((PetscObject)v3);
2496:   return(0);
2497: }

2499: /*@
2500:    MatMultTransposeAdd - Computes v3 = v2 + A' * v1.

2502:    Neighbor-wise Collective on Mat and Vec

2504:    Input Parameters:
2505: +  mat - the matrix
2506: -  v1, v2 - the vectors

2508:    Output Parameters:
2509: .  v3 - the result

2511:    Notes:
2512:    The vectors v1 and v3 cannot be the same.  I.e., one cannot
2513:    call MatMultTransposeAdd(A,v1,v2,v1).

2515:    Level: beginner

2517:    Concepts: matrix vector product^transpose and addition

2519: .seealso: MatMultTranspose(), MatMultAdd(), MatMult()
2520: @*/
2521: PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2522: {


2532:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2533:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2534:   if (!mat->ops->multtransposeadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2535:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2536:   if (mat->rmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N);
2537:   if (mat->cmap->N != v2->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N);
2538:   if (mat->cmap->N != v3->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N);
2539:   MatCheckPreallocated(mat,1);

2541:   PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);
2542:   VecLockPush(v1);
2543:   (*mat->ops->multtransposeadd)(mat,v1,v2,v3);
2544:   VecLockPop(v1);
2545:   PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);
2546:   PetscObjectStateIncrease((PetscObject)v3);
2547:   return(0);
2548: }

2550: /*@
2551:    MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1.

2553:    Neighbor-wise Collective on Mat and Vec

2555:    Input Parameters:
2556: +  mat - the matrix
2557: -  v1, v2 - the vectors

2559:    Output Parameters:
2560: .  v3 - the result

2562:    Notes:
2563:    The vectors v1 and v3 cannot be the same.  I.e., one cannot
2564:    call MatMultHermitianTransposeAdd(A,v1,v2,v1).

2566:    Level: beginner

2568:    Concepts: matrix vector product^transpose and addition

2570: .seealso: MatMultHermitianTranspose(), MatMultTranspose(), MatMultAdd(), MatMult()
2571: @*/
2572: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2573: {


2583:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2584:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2585:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2586:   if (mat->rmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N);
2587:   if (mat->cmap->N != v2->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N);
2588:   if (mat->cmap->N != v3->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N);
2589:   MatCheckPreallocated(mat,1);

2591:   PetscLogEventBegin(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2592:   VecLockPush(v1);
2593:   if (mat->ops->multhermitiantransposeadd) {
2594:     (*mat->ops->multhermitiantransposeadd)(mat,v1,v2,v3);
2595:    } else {
2596:     Vec w,z;
2597:     VecDuplicate(v1,&w);
2598:     VecCopy(v1,w);
2599:     VecConjugate(w);
2600:     VecDuplicate(v3,&z);
2601:     MatMultTranspose(mat,w,z);
2602:     VecDestroy(&w);
2603:     VecConjugate(z);
2604:     VecWAXPY(v3,1.0,v2,z);
2605:     VecDestroy(&z);
2606:   }
2607:   VecLockPop(v1);
2608:   PetscLogEventEnd(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2609:   PetscObjectStateIncrease((PetscObject)v3);
2610:   return(0);
2611: }

2613: /*@
2614:    MatMultConstrained - The inner multiplication routine for a
2615:    constrained matrix P^T A P.

2617:    Neighbor-wise Collective on Mat and Vec

2619:    Input Parameters:
2620: +  mat - the matrix
2621: -  x   - the vector to be multilplied

2623:    Output Parameters:
2624: .  y - the result

2626:    Notes:
2627:    The vectors x and y cannot be the same.  I.e., one cannot
2628:    call MatMult(A,y,y).

2630:    Level: beginner

2632: .keywords: matrix, multiply, matrix-vector product, constraint
2633: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2634: @*/
2635: PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y)
2636: {

2643:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2644:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2645:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2646:   if (mat->cmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
2647:   if (mat->rmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N);
2648:   if (mat->rmap->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->rmap->n,y->map->n);

2650:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2651:   VecLockPush(x);
2652:   (*mat->ops->multconstrained)(mat,x,y);
2653:   VecLockPop(x);
2654:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2655:   PetscObjectStateIncrease((PetscObject)y);
2656:   return(0);
2657: }

2659: /*@
2660:    MatMultTransposeConstrained - The inner multiplication routine for a
2661:    constrained matrix P^T A^T P.

2663:    Neighbor-wise Collective on Mat and Vec

2665:    Input Parameters:
2666: +  mat - the matrix
2667: -  x   - the vector to be multilplied

2669:    Output Parameters:
2670: .  y - the result

2672:    Notes:
2673:    The vectors x and y cannot be the same.  I.e., one cannot
2674:    call MatMult(A,y,y).

2676:    Level: beginner

2678: .keywords: matrix, multiply, matrix-vector product, constraint
2679: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2680: @*/
2681: PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y)
2682: {

2689:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2690:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2691:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2692:   if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
2693:   if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N);

2695:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2696:   (*mat->ops->multtransposeconstrained)(mat,x,y);
2697:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2698:   PetscObjectStateIncrease((PetscObject)y);
2699:   return(0);
2700: }

2702: /*@C
2703:    MatGetFactorType - gets the type of factorization it is

2705:    Note Collective
2706:    as the flag

2708:    Input Parameters:
2709: .  mat - the matrix

2711:    Output Parameters:
2712: .  t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT

2714:     Level: intermediate

2716: .seealso:    MatFactorType, MatGetFactor()
2717: @*/
2718: PetscErrorCode MatGetFactorType(Mat mat,MatFactorType *t)
2719: {
2723:   *t = mat->factortype;
2724:   return(0);
2725: }

2727: /* ------------------------------------------------------------*/
2728: /*@C
2729:    MatGetInfo - Returns information about matrix storage (number of
2730:    nonzeros, memory, etc.).

2732:    Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used as the flag

2734:    Input Parameters:
2735: .  mat - the matrix

2737:    Output Parameters:
2738: +  flag - flag indicating the type of parameters to be returned
2739:    (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors,
2740:    MAT_GLOBAL_SUM - sum over all processors)
2741: -  info - matrix information context

2743:    Notes:
2744:    The MatInfo context contains a variety of matrix data, including
2745:    number of nonzeros allocated and used, number of mallocs during
2746:    matrix assembly, etc.  Additional information for factored matrices
2747:    is provided (such as the fill ratio, number of mallocs during
2748:    factorization, etc.).  Much of this info is printed to PETSC_STDOUT
2749:    when using the runtime options
2750: $       -info -mat_view ::ascii_info

2752:    Example for C/C++ Users:
2753:    See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2754:    data within the MatInfo context.  For example,
2755: .vb
2756:       MatInfo info;
2757:       Mat     A;
2758:       double  mal, nz_a, nz_u;

2760:       MatGetInfo(A,MAT_LOCAL,&info);
2761:       mal  = info.mallocs;
2762:       nz_a = info.nz_allocated;
2763: .ve

2765:    Example for Fortran Users:
2766:    Fortran users should declare info as a double precision
2767:    array of dimension MAT_INFO_SIZE, and then extract the parameters
2768:    of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2769:    a complete list of parameter names.
2770: .vb
2771:       double  precision info(MAT_INFO_SIZE)
2772:       double  precision mal, nz_a
2773:       Mat     A
2774:       integer ierr

2776:       call MatGetInfo(A,MAT_LOCAL,info,ierr)
2777:       mal = info(MAT_INFO_MALLOCS)
2778:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
2779: .ve

2781:     Level: intermediate

2783:     Concepts: matrices^getting information on

2785:     Developer Note: fortran interface is not autogenerated as the f90
2786:     interface defintion cannot be generated correctly [due to MatInfo]

2788: .seealso: MatStashGetInfo()

2790: @*/
2791: PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info)
2792: {

2799:   if (!mat->ops->getinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2800:   MatCheckPreallocated(mat,1);
2801:   (*mat->ops->getinfo)(mat,flag,info);
2802:   return(0);
2803: }

2805: /*
2806:    This is used by external packages where it is not easy to get the info from the actual
2807:    matrix factorization.
2808: */
2809: PetscErrorCode MatGetInfo_External(Mat A,MatInfoType flag,MatInfo *info)
2810: {

2814:   PetscMemzero(info,sizeof(MatInfo));
2815:   return(0);
2816: }

2818: /* ----------------------------------------------------------*/

2820: /*@C
2821:    MatLUFactor - Performs in-place LU factorization of matrix.

2823:    Collective on Mat

2825:    Input Parameters:
2826: +  mat - the matrix
2827: .  row - row permutation
2828: .  col - column permutation
2829: -  info - options for factorization, includes
2830: $          fill - expected fill as ratio of original fill.
2831: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
2832: $                   Run with the option -info to determine an optimal value to use

2834:    Notes:
2835:    Most users should employ the simplified KSP interface for linear solvers
2836:    instead of working directly with matrix algebra routines such as this.
2837:    See, e.g., KSPCreate().

2839:    This changes the state of the matrix to a factored matrix; it cannot be used
2840:    for example with MatSetValues() unless one first calls MatSetUnfactored().

2842:    Level: developer

2844:    Concepts: matrices^LU factorization

2846: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(),
2847:           MatGetOrdering(), MatSetUnfactored(), MatFactorInfo, MatGetFactor()

2849:     Developer Note: fortran interface is not autogenerated as the f90
2850:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2852: @*/
2853: PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
2854: {
2856:   MatFactorInfo  tinfo;

2864:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2865:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2866:   if (!mat->ops->lufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2867:   MatCheckPreallocated(mat,1);
2868:   if (!info) {
2869:     MatFactorInfoInitialize(&tinfo);
2870:     info = &tinfo;
2871:   }

2873:   PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);
2874:   (*mat->ops->lufactor)(mat,row,col,info);
2875:   PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);
2876:   PetscObjectStateIncrease((PetscObject)mat);
2877:   return(0);
2878: }

2880: /*@C
2881:    MatILUFactor - Performs in-place ILU factorization of matrix.

2883:    Collective on Mat

2885:    Input Parameters:
2886: +  mat - the matrix
2887: .  row - row permutation
2888: .  col - column permutation
2889: -  info - structure containing
2890: $      levels - number of levels of fill.
2891: $      expected fill - as ratio of original fill.
2892: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
2893:                 missing diagonal entries)

2895:    Notes:
2896:    Probably really in-place only when level of fill is zero, otherwise allocates
2897:    new space to store factored matrix and deletes previous memory.

2899:    Most users should employ the simplified KSP interface for linear solvers
2900:    instead of working directly with matrix algebra routines such as this.
2901:    See, e.g., KSPCreate().

2903:    Level: developer

2905:    Concepts: matrices^ILU factorization

2907: .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

2909:     Developer Note: fortran interface is not autogenerated as the f90
2910:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2912: @*/
2913: PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
2914: {

2923:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
2924:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2925:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2926:   if (!mat->ops->ilufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2927:   MatCheckPreallocated(mat,1);

2929:   PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);
2930:   (*mat->ops->ilufactor)(mat,row,col,info);
2931:   PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);
2932:   PetscObjectStateIncrease((PetscObject)mat);
2933:   return(0);
2934: }

2936: /*@C
2937:    MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
2938:    Call this routine before calling MatLUFactorNumeric().

2940:    Collective on Mat

2942:    Input Parameters:
2943: +  fact - the factor matrix obtained with MatGetFactor()
2944: .  mat - the matrix
2945: .  row, col - row and column permutations
2946: -  info - options for factorization, includes
2947: $          fill - expected fill as ratio of original fill.
2948: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
2949: $                   Run with the option -info to determine an optimal value to use


2952:    Notes: See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

2954:    Most users should employ the simplified KSP interface for linear solvers
2955:    instead of working directly with matrix algebra routines such as this.
2956:    See, e.g., KSPCreate().

2958:    Level: developer

2960:    Concepts: matrices^LU symbolic factorization

2962: .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo, MatFactorInfoInitialize()

2964:     Developer Note: fortran interface is not autogenerated as the f90
2965:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2967: @*/
2968: PetscErrorCode MatLUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
2969: {

2979:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2980:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2981:   if (!(fact)->ops->lufactorsymbolic) {
2982:     const MatSolverPackage spackage;
2983:     MatFactorGetSolverPackage(fact,&spackage);
2984:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic LU using solver package %s",((PetscObject)mat)->type_name,spackage);
2985:   }
2986:   MatCheckPreallocated(mat,2);

2988:   PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);
2989:   (fact->ops->lufactorsymbolic)(fact,mat,row,col,info);
2990:   PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);
2991:   PetscObjectStateIncrease((PetscObject)fact);
2992:   return(0);
2993: }

2995: /*@C
2996:    MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
2997:    Call this routine after first calling MatLUFactorSymbolic().

2999:    Collective on Mat

3001:    Input Parameters:
3002: +  fact - the factor matrix obtained with MatGetFactor()
3003: .  mat - the matrix
3004: -  info - options for factorization

3006:    Notes:
3007:    See MatLUFactor() for in-place factorization.  See
3008:    MatCholeskyFactorNumeric() for the symmetric, positive definite case.

3010:    Most users should employ the simplified KSP interface for linear solvers
3011:    instead of working directly with matrix algebra routines such as this.
3012:    See, e.g., KSPCreate().

3014:    Level: developer

3016:    Concepts: matrices^LU numeric factorization

3018: .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor()

3020:     Developer Note: fortran interface is not autogenerated as the f90
3021:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3023: @*/
3024: PetscErrorCode MatLUFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3025: {

3033:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3034:   if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Mat fact: global dimensions are different %D should = %D %D should = %D",mat->rmap->N,(fact)->rmap->N,mat->cmap->N,(fact)->cmap->N);

3036:   if (!(fact)->ops->lufactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric LU",((PetscObject)mat)->type_name);
3037:   MatCheckPreallocated(mat,2);
3038:   PetscLogEventBegin(MAT_LUFactorNumeric,mat,fact,0,0);
3039:   (fact->ops->lufactornumeric)(fact,mat,info);
3040:   PetscLogEventEnd(MAT_LUFactorNumeric,mat,fact,0,0);
3041:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3042:   PetscObjectStateIncrease((PetscObject)fact);
3043:   return(0);
3044: }

3046: /*@C
3047:    MatCholeskyFactor - Performs in-place Cholesky factorization of a
3048:    symmetric matrix.

3050:    Collective on Mat

3052:    Input Parameters:
3053: +  mat - the matrix
3054: .  perm - row and column permutations
3055: -  f - expected fill as ratio of original fill

3057:    Notes:
3058:    See MatLUFactor() for the nonsymmetric case.  See also
3059:    MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric().

3061:    Most users should employ the simplified KSP interface for linear solvers
3062:    instead of working directly with matrix algebra routines such as this.
3063:    See, e.g., KSPCreate().

3065:    Level: developer

3067:    Concepts: matrices^Cholesky factorization

3069: .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric()
3070:           MatGetOrdering()

3072:     Developer Note: fortran interface is not autogenerated as the f90
3073:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3075: @*/
3076: PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,const MatFactorInfo *info)
3077: {

3085:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3086:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3087:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3088:   if (!mat->ops->choleskyfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"In-place factorization for Mat type %s is not supported, try out-of-place factorization. See MatCholeskyFactorSymbolic/Numeric",((PetscObject)mat)->type_name);
3089:   MatCheckPreallocated(mat,1);

3091:   PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);
3092:   (*mat->ops->choleskyfactor)(mat,perm,info);
3093:   PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);
3094:   PetscObjectStateIncrease((PetscObject)mat);
3095:   return(0);
3096: }

3098: /*@C
3099:    MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3100:    of a symmetric matrix.

3102:    Collective on Mat

3104:    Input Parameters:
3105: +  fact - the factor matrix obtained with MatGetFactor()
3106: .  mat - the matrix
3107: .  perm - row and column permutations
3108: -  info - options for factorization, includes
3109: $          fill - expected fill as ratio of original fill.
3110: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3111: $                   Run with the option -info to determine an optimal value to use

3113:    Notes:
3114:    See MatLUFactorSymbolic() for the nonsymmetric case.  See also
3115:    MatCholeskyFactor() and MatCholeskyFactorNumeric().

3117:    Most users should employ the simplified KSP interface for linear solvers
3118:    instead of working directly with matrix algebra routines such as this.
3119:    See, e.g., KSPCreate().

3121:    Level: developer

3123:    Concepts: matrices^Cholesky symbolic factorization

3125: .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric()
3126:           MatGetOrdering()

3128:     Developer Note: fortran interface is not autogenerated as the f90
3129:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3131: @*/
3132: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
3133: {

3142:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3143:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3144:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3145:   if (!(fact)->ops->choleskyfactorsymbolic) {
3146:     const MatSolverPackage spackage;
3147:     MatFactorGetSolverPackage(fact,&spackage);
3148:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s symbolic factor Cholesky using solver package %s",((PetscObject)mat)->type_name,spackage);
3149:   }
3150:   MatCheckPreallocated(mat,2);

3152:   PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3153:   (fact->ops->choleskyfactorsymbolic)(fact,mat,perm,info);
3154:   PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3155:   PetscObjectStateIncrease((PetscObject)fact);
3156:   return(0);
3157: }

3159: /*@C
3160:    MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3161:    of a symmetric matrix. Call this routine after first calling
3162:    MatCholeskyFactorSymbolic().

3164:    Collective on Mat

3166:    Input Parameters:
3167: +  fact - the factor matrix obtained with MatGetFactor()
3168: .  mat - the initial matrix
3169: .  info - options for factorization
3170: -  fact - the symbolic factor of mat


3173:    Notes:
3174:    Most users should employ the simplified KSP interface for linear solvers
3175:    instead of working directly with matrix algebra routines such as this.
3176:    See, e.g., KSPCreate().

3178:    Level: developer

3180:    Concepts: matrices^Cholesky numeric factorization

3182: .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric()

3184:     Developer Note: fortran interface is not autogenerated as the f90
3185:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3187: @*/
3188: PetscErrorCode MatCholeskyFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3189: {

3197:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3198:   if (!(fact)->ops->choleskyfactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric factor Cholesky",((PetscObject)mat)->type_name);
3199:   if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Mat fact: global dim %D should = %D %D should = %D",mat->rmap->N,(fact)->rmap->N,mat->cmap->N,(fact)->cmap->N);
3200:   MatCheckPreallocated(mat,2);

3202:   PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3203:   (fact->ops->choleskyfactornumeric)(fact,mat,info);
3204:   PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3205:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3206:   PetscObjectStateIncrease((PetscObject)fact);
3207:   return(0);
3208: }

3210: /* ----------------------------------------------------------------*/
3211: /*@
3212:    MatSolve - Solves A x = b, given a factored matrix.

3214:    Neighbor-wise Collective on Mat and Vec

3216:    Input Parameters:
3217: +  mat - the factored matrix
3218: -  b - the right-hand-side vector

3220:    Output Parameter:
3221: .  x - the result vector

3223:    Notes:
3224:    The vectors b and x cannot be the same.  I.e., one cannot
3225:    call MatSolve(A,x,x).

3227:    Notes:
3228:    Most users should employ the simplified KSP interface for linear solvers
3229:    instead of working directly with matrix algebra routines such as this.
3230:    See, e.g., KSPCreate().

3232:    Level: developer

3234:    Concepts: matrices^triangular solves

3236: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd()
3237: @*/
3238: PetscErrorCode MatSolve(Mat mat,Vec b,Vec x)
3239: {

3249:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3250:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3251:   if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
3252:   if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N);
3253:   if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n);
3254:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3255:   if (!mat->ops->solve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3256:   MatCheckPreallocated(mat,1);

3258:   PetscLogEventBegin(MAT_Solve,mat,b,x,0);
3259:   if (mat->factorerrortype) {
3260:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3261:     VecSetInf(x);
3262:   } else {
3263:     (*mat->ops->solve)(mat,b,x);
3264:   }
3265:   PetscLogEventEnd(MAT_Solve,mat,b,x,0);
3266:   PetscObjectStateIncrease((PetscObject)x);
3267:   return(0);
3268: }

3270: static PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X, PetscBool trans)
3271: {
3273:   Vec            b,x;
3274:   PetscInt       m,N,i;
3275:   PetscScalar    *bb,*xx;
3276:   PetscBool      flg;

3279:   PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
3280:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
3281:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
3282:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");

3284:   MatDenseGetArray(B,&bb);
3285:   MatDenseGetArray(X,&xx);
3286:   MatGetLocalSize(B,&m,NULL);  /* number local rows */
3287:   MatGetSize(B,NULL,&N);       /* total columns in dense matrix */
3288:   MatCreateVecs(A,&x,&b);
3289:   for (i=0; i<N; i++) {
3290:     VecPlaceArray(b,bb + i*m);
3291:     VecPlaceArray(x,xx + i*m);
3292:     if (trans) {
3293:       MatSolveTranspose(A,b,x);
3294:     } else {
3295:       MatSolve(A,b,x);
3296:     }
3297:     VecResetArray(x);
3298:     VecResetArray(b);
3299:   }
3300:   VecDestroy(&b);
3301:   VecDestroy(&x);
3302:   MatDenseRestoreArray(B,&bb);
3303:   MatDenseRestoreArray(X,&xx);
3304:   return(0);
3305: }

3307: /*@
3308:    MatMatSolve - Solves A X = B, given a factored matrix.

3310:    Neighbor-wise Collective on Mat

3312:    Input Parameters:
3313: +  A - the factored matrix
3314: -  B - the right-hand-side matrix  (dense matrix)

3316:    Output Parameter:
3317: .  X - the result matrix (dense matrix)

3319:    Notes:
3320:    The matrices b and x cannot be the same.  I.e., one cannot
3321:    call MatMatSolve(A,x,x).

3323:    Notes:
3324:    Most users should usually employ the simplified KSP interface for linear solvers
3325:    instead of working directly with matrix algebra routines such as this.
3326:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3327:    at a time.

3329:    When using SuperLU_Dist as a parallel solver PETSc will use the SuperLU_Dist functionality to solve multiple right hand sides simultaneously. For MUMPS
3330:    it calls a separate solve for each right hand side since MUMPS does not yet support distributed right hand sides.

3332:    Since the resulting matrix X must always be dense we do not support sparse representation of the matrix B.

3334:    Level: developer

3336:    Concepts: matrices^triangular solves

3338: .seealso: MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3339: @*/
3340: PetscErrorCode MatMatSolve(Mat A,Mat B,Mat X)
3341: {

3351:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3352:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3353:   if (A->cmap->N != X->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat X: global dim %D %D",A->cmap->N,X->rmap->N);
3354:   if (A->rmap->N != B->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D",A->rmap->N,B->rmap->N);
3355:   if (A->rmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat A,Mat B: local dim %D %D",A->rmap->n,B->rmap->n);
3356:   if (X->cmap->N < B->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Solution matrix must have same number of columns as rhs matrix");
3357:   if (!A->rmap->N && !A->cmap->N) return(0);
3358:   MatCheckPreallocated(A,1);

3360:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3361:   if (!A->ops->matsolve) {
3362:     PetscInfo1(A,"Mat type %s using basic MatMatSolve\n",((PetscObject)A)->type_name);
3363:     MatMatSolve_Basic(A,B,X,PETSC_FALSE);
3364:   } else {
3365:     (*A->ops->matsolve)(A,B,X);
3366:   }
3367:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3368:   PetscObjectStateIncrease((PetscObject)X);
3369:   return(0);
3370: }

3372: /*@
3373:    MatMatSolveTranspose - Solves A^T X = B, given a factored matrix.

3375:    Neighbor-wise Collective on Mat

3377:    Input Parameters:
3378: +  A - the factored matrix
3379: -  B - the right-hand-side matrix  (dense matrix)

3381:    Output Parameter:
3382: .  X - the result matrix (dense matrix)

3384:    Notes:
3385:    The matrices b and x cannot be the same.  I.e., one cannot
3386:    call MatMatSolveTranspose(A,x,x).

3388:    Notes:
3389:    Most users should usually employ the simplified KSP interface for linear solvers
3390:    instead of working directly with matrix algebra routines such as this.
3391:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3392:    at a time.

3394:    When using SuperLU_Dist as a parallel solver PETSc will use the SuperLU_Dist functionality to solve multiple right hand sides simultaneously. For MUMPS
3395:    it calls a separate solve for each right hand side since MUMPS does not yet support distributed right hand sides.

3397:    Since the resulting matrix X must always be dense we do not support sparse representation of the matrix B.

3399:    Level: developer

3401:    Concepts: matrices^triangular solves

3403: .seealso: MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3404: @*/
3405: PetscErrorCode MatMatSolveTranspose(Mat A,Mat B,Mat X)
3406: {

3416:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3417:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3418:   if (A->cmap->N != X->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat X: global dim %D %D",A->cmap->N,X->rmap->N);
3419:   if (A->rmap->N != B->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D",A->rmap->N,B->rmap->N);
3420:   if (A->rmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat A,Mat B: local dim %D %D",A->rmap->n,B->rmap->n);
3421:   if (X->cmap->N < B->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Solution matrix must have same number of columns as rhs matrix");
3422:   if (!A->rmap->N && !A->cmap->N) return(0);
3423:   MatCheckPreallocated(A,1);

3425:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3426:   if (!A->ops->matsolvetranspose) {
3427:     PetscInfo1(A,"Mat type %s using basic MatMatSolveTranspose\n",((PetscObject)A)->type_name);
3428:     MatMatSolve_Basic(A,B,X,PETSC_TRUE);
3429:   } else {
3430:     (*A->ops->matsolvetranspose)(A,B,X);
3431:   }
3432:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3433:   PetscObjectStateIncrease((PetscObject)X);
3434:   return(0);
3435: }

3437: /*@
3438:    MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3439:                             U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3441:    Neighbor-wise Collective on Mat and Vec

3443:    Input Parameters:
3444: +  mat - the factored matrix
3445: -  b - the right-hand-side vector

3447:    Output Parameter:
3448: .  x - the result vector

3450:    Notes:
3451:    MatSolve() should be used for most applications, as it performs
3452:    a forward solve followed by a backward solve.

3454:    The vectors b and x cannot be the same,  i.e., one cannot
3455:    call MatForwardSolve(A,x,x).

3457:    For matrix in seqsbaij format with block size larger than 1,
3458:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3459:    MatForwardSolve() solves U^T*D y = b, and
3460:    MatBackwardSolve() solves U x = y.
3461:    Thus they do not provide a symmetric preconditioner.

3463:    Most users should employ the simplified KSP interface for linear solvers
3464:    instead of working directly with matrix algebra routines such as this.
3465:    See, e.g., KSPCreate().

3467:    Level: developer

3469:    Concepts: matrices^forward solves

3471: .seealso: MatSolve(), MatBackwardSolve()
3472: @*/
3473: PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x)
3474: {

3484:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3485:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3486:   if (!mat->ops->forwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3487:   if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
3488:   if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N);
3489:   if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n);
3490:   MatCheckPreallocated(mat,1);
3491:   PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);
3492:   (*mat->ops->forwardsolve)(mat,b,x);
3493:   PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);
3494:   PetscObjectStateIncrease((PetscObject)x);
3495:   return(0);
3496: }

3498: /*@
3499:    MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3500:                              D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3502:    Neighbor-wise Collective on Mat and Vec

3504:    Input Parameters:
3505: +  mat - the factored matrix
3506: -  b - the right-hand-side vector

3508:    Output Parameter:
3509: .  x - the result vector

3511:    Notes:
3512:    MatSolve() should be used for most applications, as it performs
3513:    a forward solve followed by a backward solve.

3515:    The vectors b and x cannot be the same.  I.e., one cannot
3516:    call MatBackwardSolve(A,x,x).

3518:    For matrix in seqsbaij format with block size larger than 1,
3519:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3520:    MatForwardSolve() solves U^T*D y = b, and
3521:    MatBackwardSolve() solves U x = y.
3522:    Thus they do not provide a symmetric preconditioner.

3524:    Most users should employ the simplified KSP interface for linear solvers
3525:    instead of working directly with matrix algebra routines such as this.
3526:    See, e.g., KSPCreate().

3528:    Level: developer

3530:    Concepts: matrices^backward solves

3532: .seealso: MatSolve(), MatForwardSolve()
3533: @*/
3534: PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x)
3535: {

3545:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3546:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3547:   if (!mat->ops->backwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3548:   if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
3549:   if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N);
3550:   if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n);
3551:   MatCheckPreallocated(mat,1);

3553:   PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);
3554:   (*mat->ops->backwardsolve)(mat,b,x);
3555:   PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);
3556:   PetscObjectStateIncrease((PetscObject)x);
3557:   return(0);
3558: }

3560: /*@
3561:    MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3563:    Neighbor-wise Collective on Mat and Vec

3565:    Input Parameters:
3566: +  mat - the factored matrix
3567: .  b - the right-hand-side vector
3568: -  y - the vector to be added to

3570:    Output Parameter:
3571: .  x - the result vector

3573:    Notes:
3574:    The vectors b and x cannot be the same.  I.e., one cannot
3575:    call MatSolveAdd(A,x,y,x).

3577:    Most users should employ the simplified KSP interface for linear solvers
3578:    instead of working directly with matrix algebra routines such as this.
3579:    See, e.g., KSPCreate().

3581:    Level: developer

3583:    Concepts: matrices^triangular solves

3585: .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd()
3586: @*/
3587: PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x)
3588: {
3589:   PetscScalar    one = 1.0;
3590:   Vec            tmp;

3602:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3603:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3604:   if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
3605:   if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N);
3606:   if (mat->rmap->N != y->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N);
3607:   if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n);
3608:   if (x->map->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->map->n,y->map->n);
3609:   MatCheckPreallocated(mat,1);

3611:   PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);
3612:   if (mat->ops->solveadd) {
3613:     (*mat->ops->solveadd)(mat,b,y,x);
3614:   } else {
3615:     /* do the solve then the add manually */
3616:     if (x != y) {
3617:       MatSolve(mat,b,x);
3618:       VecAXPY(x,one,y);
3619:     } else {
3620:       VecDuplicate(x,&tmp);
3621:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3622:       VecCopy(x,tmp);
3623:       MatSolve(mat,b,x);
3624:       VecAXPY(x,one,tmp);
3625:       VecDestroy(&tmp);
3626:     }
3627:   }
3628:   PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);
3629:   PetscObjectStateIncrease((PetscObject)x);
3630:   return(0);
3631: }

3633: /*@
3634:    MatSolveTranspose - Solves A' x = b, given a factored matrix.

3636:    Neighbor-wise Collective on Mat and Vec

3638:    Input Parameters:
3639: +  mat - the factored matrix
3640: -  b - the right-hand-side vector

3642:    Output Parameter:
3643: .  x - the result vector

3645:    Notes:
3646:    The vectors b and x cannot be the same.  I.e., one cannot
3647:    call MatSolveTranspose(A,x,x).

3649:    Most users should employ the simplified KSP interface for linear solvers
3650:    instead of working directly with matrix algebra routines such as this.
3651:    See, e.g., KSPCreate().

3653:    Level: developer

3655:    Concepts: matrices^triangular solves

3657: .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd()
3658: @*/
3659: PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x)
3660: {

3670:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3671:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3672:   if (!mat->ops->solvetranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name);
3673:   if (mat->rmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N);
3674:   if (mat->cmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N);
3675:   MatCheckPreallocated(mat,1);
3676:   PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);
3677:   if (mat->factorerrortype) {
3678:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3679:     VecSetInf(x);
3680:   } else {
3681:     (*mat->ops->solvetranspose)(mat,b,x);
3682:   }
3683:   PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);
3684:   PetscObjectStateIncrease((PetscObject)x);
3685:   return(0);
3686: }

3688: /*@
3689:    MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
3690:                       factored matrix.

3692:    Neighbor-wise Collective on Mat and Vec

3694:    Input Parameters:
3695: +  mat - the factored matrix
3696: .  b - the right-hand-side vector
3697: -  y - the vector to be added to

3699:    Output Parameter:
3700: .  x - the result vector

3702:    Notes:
3703:    The vectors b and x cannot be the same.  I.e., one cannot
3704:    call MatSolveTransposeAdd(A,x,y,x).

3706:    Most users should employ the simplified KSP interface for linear solvers
3707:    instead of working directly with matrix algebra routines such as this.
3708:    See, e.g., KSPCreate().

3710:    Level: developer

3712:    Concepts: matrices^triangular solves

3714: .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose()
3715: @*/
3716: PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x)
3717: {
3718:   PetscScalar    one = 1.0;
3720:   Vec            tmp;

3731:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3732:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3733:   if (mat->rmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N);
3734:   if (mat->cmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N);
3735:   if (mat->cmap->N != y->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N);
3736:   if (x->map->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->map->n,y->map->n);
3737:   MatCheckPreallocated(mat,1);

3739:   PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);
3740:   if (mat->ops->solvetransposeadd) {
3741:     if (mat->factorerrortype) {
3742:       PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3743:       VecSetInf(x);
3744:     } else {
3745:       (*mat->ops->solvetransposeadd)(mat,b,y,x);
3746:     }
3747:   } else {
3748:     /* do the solve then the add manually */
3749:     if (x != y) {
3750:       MatSolveTranspose(mat,b,x);
3751:       VecAXPY(x,one,y);
3752:     } else {
3753:       VecDuplicate(x,&tmp);
3754:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3755:       VecCopy(x,tmp);
3756:       MatSolveTranspose(mat,b,x);
3757:       VecAXPY(x,one,tmp);
3758:       VecDestroy(&tmp);
3759:     }
3760:   }
3761:   PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);
3762:   PetscObjectStateIncrease((PetscObject)x);
3763:   return(0);
3764: }
3765: /* ----------------------------------------------------------------*/

3767: /*@
3768:    MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

3770:    Neighbor-wise Collective on Mat and Vec

3772:    Input Parameters:
3773: +  mat - the matrix
3774: .  b - the right hand side
3775: .  omega - the relaxation factor
3776: .  flag - flag indicating the type of SOR (see below)
3777: .  shift -  diagonal shift
3778: .  its - the number of iterations
3779: -  lits - the number of local iterations

3781:    Output Parameters:
3782: .  x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess)

3784:    SOR Flags:
3785: .     SOR_FORWARD_SWEEP - forward SOR
3786: .     SOR_BACKWARD_SWEEP - backward SOR
3787: .     SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR)
3788: .     SOR_LOCAL_FORWARD_SWEEP - local forward SOR
3789: .     SOR_LOCAL_BACKWARD_SWEEP - local forward SOR
3790: .     SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR
3791: .     SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies
3792:          upper/lower triangular part of matrix to
3793:          vector (with omega)
3794: .     SOR_ZERO_INITIAL_GUESS - zero initial guess

3796:    Notes:
3797:    SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and
3798:    SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings
3799:    on each processor.

3801:    Application programmers will not generally use MatSOR() directly,
3802:    but instead will employ the KSP/PC interface.

3804:    Notes: for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

3806:    Notes for Advanced Users:
3807:    The flags are implemented as bitwise inclusive or operations.
3808:    For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP)
3809:    to specify a zero initial guess for SSOR.

3811:    Most users should employ the simplified KSP interface for linear solvers
3812:    instead of working directly with matrix algebra routines such as this.
3813:    See, e.g., KSPCreate().

3815:    Vectors x and b CANNOT be the same

3817:    Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes

3819:    Level: developer

3821:    Concepts: matrices^relaxation
3822:    Concepts: matrices^SOR
3823:    Concepts: matrices^Gauss-Seidel

3825: @*/
3826: PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x)
3827: {

3837:   if (!mat->ops->sor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3838:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3839:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3840:   if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N);
3841:   if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N);
3842:   if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n);
3843:   if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its);
3844:   if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits);
3845:   if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same");

3847:   MatCheckPreallocated(mat,1);
3848:   PetscLogEventBegin(MAT_SOR,mat,b,x,0);
3849:   ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);
3850:   PetscLogEventEnd(MAT_SOR,mat,b,x,0);
3851:   PetscObjectStateIncrease((PetscObject)x);
3852:   return(0);
3853: }

3855: /*
3856:       Default matrix copy routine.
3857: */
3858: PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str)
3859: {
3860:   PetscErrorCode    ierr;
3861:   PetscInt          i,rstart = 0,rend = 0,nz;
3862:   const PetscInt    *cwork;
3863:   const PetscScalar *vwork;

3866:   if (B->assembled) {
3867:     MatZeroEntries(B);
3868:   }
3869:   MatGetOwnershipRange(A,&rstart,&rend);
3870:   for (i=rstart; i<rend; i++) {
3871:     MatGetRow(A,i,&nz,&cwork,&vwork);
3872:     MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);
3873:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
3874:   }
3875:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3876:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3877:   return(0);
3878: }

3880: /*@
3881:    MatCopy - Copys a matrix to another matrix.

3883:    Collective on Mat

3885:    Input Parameters:
3886: +  A - the matrix
3887: -  str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN

3889:    Output Parameter:
3890: .  B - where the copy is put

3892:    Notes:
3893:    If you use SAME_NONZERO_PATTERN then the two matrices had better have the
3894:    same nonzero pattern or the routine will crash.

3896:    MatCopy() copies the matrix entries of a matrix to another existing
3897:    matrix (after first zeroing the second matrix).  A related routine is
3898:    MatConvert(), which first creates a new matrix and then copies the data.

3900:    Level: intermediate

3902:    Concepts: matrices^copying

3904: .seealso: MatConvert(), MatDuplicate()

3906: @*/
3907: PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str)
3908: {
3910:   PetscInt       i;

3918:   MatCheckPreallocated(B,2);
3919:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3920:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3921:   if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim (%D,%D) (%D,%D)",A->rmap->N,B->rmap->N,A->cmap->N,B->cmap->N);
3922:   MatCheckPreallocated(A,1);

3924:   PetscLogEventBegin(MAT_Copy,A,B,0,0);
3925:   if (A->ops->copy) {
3926:     (*A->ops->copy)(A,B,str);
3927:   } else { /* generic conversion */
3928:     MatCopy_Basic(A,B,str);
3929:   }

3931:   B->stencil.dim = A->stencil.dim;
3932:   B->stencil.noc = A->stencil.noc;
3933:   for (i=0; i<=A->stencil.dim; i++) {
3934:     B->stencil.dims[i]   = A->stencil.dims[i];
3935:     B->stencil.starts[i] = A->stencil.starts[i];
3936:   }

3938:   PetscLogEventEnd(MAT_Copy,A,B,0,0);
3939:   PetscObjectStateIncrease((PetscObject)B);
3940:   return(0);
3941: }

3943: /*@C
3944:    MatConvert - Converts a matrix to another matrix, either of the same
3945:    or different type.

3947:    Collective on Mat

3949:    Input Parameters:
3950: +  mat - the matrix
3951: .  newtype - new matrix type.  Use MATSAME to create a new matrix of the
3952:    same type as the original matrix.
3953: -  reuse - denotes if the destination matrix is to be created or reused.
3954:    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
3955:    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).

3957:    Output Parameter:
3958: .  M - pointer to place new matrix

3960:    Notes:
3961:    MatConvert() first creates a new matrix and then copies the data from
3962:    the first matrix.  A related routine is MatCopy(), which copies the matrix
3963:    entries of one matrix to another already existing matrix context.

3965:    Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
3966:    the MPI communicator of the generated matrix is always the same as the communicator
3967:    of the input matrix.

3969:    Level: intermediate

3971:    Concepts: matrices^converting between storage formats

3973: .seealso: MatCopy(), MatDuplicate()
3974: @*/
3975: PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M)
3976: {
3978:   PetscBool      sametype,issame,flg;
3979:   char           convname[256],mtype[256];
3980:   Mat            B;

3986:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3987:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3988:   MatCheckPreallocated(mat,1);
3989:   MatSetOption(mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);

3991:   PetscOptionsGetString(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);
3992:   if (flg) {
3993:     newtype = mtype;
3994:   }
3995:   PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);
3996:   PetscStrcmp(newtype,"same",&issame);
3997:   if ((reuse == MAT_INPLACE_MATRIX) && (mat != *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires same input and output matrix");
3998:   if ((reuse == MAT_REUSE_MATRIX) && (mat == *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");

4000:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) return(0);

4002:   if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4003:     (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);
4004:   } else {
4005:     PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=NULL;
4006:     const char     *prefix[3] = {"seq","mpi",""};
4007:     PetscInt       i;
4008:     /*
4009:        Order of precedence:
4010:        1) See if a specialized converter is known to the current matrix.
4011:        2) See if a specialized converter is known to the desired matrix class.
4012:        3) See if a good general converter is registered for the desired class
4013:           (as of 6/27/03 only MATMPIADJ falls into this category).
4014:        4) See if a good general converter is known for the current matrix.
4015:        5) Use a really basic converter.
4016:     */

4018:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4019:     for (i=0; i<3; i++) {
4020:       PetscStrcpy(convname,"MatConvert_");
4021:       PetscStrcat(convname,((PetscObject)mat)->type_name);
4022:       PetscStrcat(convname,"_");
4023:       PetscStrcat(convname,prefix[i]);
4024:       PetscStrcat(convname,issame ? ((PetscObject)mat)->type_name : newtype);
4025:       PetscStrcat(convname,"_C");
4026:       PetscObjectQueryFunction((PetscObject)mat,convname,&conv);
4027:       if (conv) goto foundconv;
4028:     }

4030:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4031:     MatCreate(PetscObjectComm((PetscObject)mat),&B);
4032:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);
4033:     MatSetType(B,newtype);
4034:     for (i=0; i<3; i++) {
4035:       PetscStrcpy(convname,"MatConvert_");
4036:       PetscStrcat(convname,((PetscObject)mat)->type_name);
4037:       PetscStrcat(convname,"_");
4038:       PetscStrcat(convname,prefix[i]);
4039:       PetscStrcat(convname,newtype);
4040:       PetscStrcat(convname,"_C");
4041:       PetscObjectQueryFunction((PetscObject)B,convname,&conv);
4042:       if (conv) {
4043:         MatDestroy(&B);
4044:         goto foundconv;
4045:       }
4046:     }

4048:     /* 3) See if a good general converter is registered for the desired class */
4049:     conv = B->ops->convertfrom;
4050:     MatDestroy(&B);
4051:     if (conv) goto foundconv;

4053:     /* 4) See if a good general converter is known for the current matrix */
4054:     if (mat->ops->convert) {
4055:       conv = mat->ops->convert;
4056:     }
4057:     if (conv) goto foundconv;

4059:     /* 5) Use a really basic converter. */
4060:     conv = MatConvert_Basic;

4062: foundconv:
4063:     PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4064:     (*conv)(mat,newtype,reuse,M);
4065:     PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4066:   }
4067:   PetscObjectStateIncrease((PetscObject)*M);

4069:   /* Copy Mat options */
4070:   if (mat->symmetric) {MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);}
4071:   if (mat->hermitian) {MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);}
4072:   return(0);
4073: }

4075: /*@C
4076:    MatFactorGetSolverPackage - Returns name of the package providing the factorization routines

4078:    Not Collective

4080:    Input Parameter:
4081: .  mat - the matrix, must be a factored matrix

4083:    Output Parameter:
4084: .   type - the string name of the package (do not free this string)

4086:    Notes:
4087:       In Fortran you pass in a empty string and the package name will be copied into it.
4088:     (Make sure the string is long enough)

4090:    Level: intermediate

4092: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor()
4093: @*/
4094: PetscErrorCode MatFactorGetSolverPackage(Mat mat, const MatSolverPackage *type)
4095: {
4096:   PetscErrorCode ierr, (*conv)(Mat,const MatSolverPackage*);

4101:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
4102:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverPackage_C",&conv);
4103:   if (!conv) {
4104:     *type = MATSOLVERPETSC;
4105:   } else {
4106:     (*conv)(mat,type);
4107:   }
4108:   return(0);
4109: }

4111: typedef struct _MatSolverPackageForSpecifcType* MatSolverPackageForSpecifcType;
4112: struct _MatSolverPackageForSpecifcType {
4113:   MatType                        mtype;
4114:   PetscErrorCode                 (*getfactor[4])(Mat,MatFactorType,Mat*);
4115:   MatSolverPackageForSpecifcType next;
4116: };

4118: typedef struct _MatSolverPackageHolder* MatSolverPackageHolder;
4119: struct _MatSolverPackageHolder {
4120:   char                           *name;
4121:   MatSolverPackageForSpecifcType handlers;
4122:   MatSolverPackageHolder         next;
4123: };

4125: static MatSolverPackageHolder MatSolverPackageHolders = NULL;

4127: /*@C
4128:    MatSolvePackageRegister - Registers a MatSolverPackage that works for a particular matrix type

4130:    Input Parameters:
4131: +    package - name of the package, for example petsc or superlu
4132: .    mtype - the matrix type that works with this package
4133: .    ftype - the type of factorization supported by the package
4134: -    getfactor - routine that will create the factored matrix ready to be used

4136:     Level: intermediate

4138: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4139: @*/
4140: PetscErrorCode MatSolverPackageRegister(const MatSolverPackage package,const MatType mtype,MatFactorType ftype,PetscErrorCode (*getfactor)(Mat,MatFactorType,Mat*))
4141: {
4142:   PetscErrorCode                 ierr;
4143:   MatSolverPackageHolder         next = MatSolverPackageHolders,prev;
4144:   PetscBool                      flg;
4145:   MatSolverPackageForSpecifcType inext,iprev = NULL;

4148:   if (!next) {
4149:     PetscNew(&MatSolverPackageHolders);
4150:     PetscStrallocpy(package,&MatSolverPackageHolders->name);
4151:     PetscNew(&MatSolverPackageHolders->handlers);
4152:     PetscStrallocpy(mtype,(char **)&MatSolverPackageHolders->handlers->mtype);
4153:     MatSolverPackageHolders->handlers->getfactor[(int)ftype-1] = getfactor;
4154:     return(0);
4155:   }
4156:   while (next) {
4157:     PetscStrcasecmp(package,next->name,&flg);
4158:     if (flg) {
4159:       if (!next->handlers) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MatSolverPackageHolder is missing handlers");
4160:       inext = next->handlers;
4161:       while (inext) {
4162:         PetscStrcasecmp(mtype,inext->mtype,&flg);
4163:         if (flg) {
4164:           inext->getfactor[(int)ftype-1] = getfactor;
4165:           return(0);
4166:         }
4167:         iprev = inext;
4168:         inext = inext->next;
4169:       }
4170:       PetscNew(&iprev->next);
4171:       PetscStrallocpy(mtype,(char **)&iprev->next->mtype);
4172:       iprev->next->getfactor[(int)ftype-1] = getfactor;
4173:       return(0);
4174:     }
4175:     prev = next;
4176:     next = next->next;
4177:   }
4178:   PetscNew(&prev->next);
4179:   PetscStrallocpy(package,&prev->next->name);
4180:   PetscNew(&prev->next->handlers);
4181:   PetscStrallocpy(mtype,(char **)&prev->next->handlers->mtype);
4182:   prev->next->handlers->getfactor[(int)ftype-1] = getfactor;
4183:   return(0);
4184: }

4186: /*@C
4187:    MatSolvePackageGet - Get's the function that creates the factor matrix if it exist

4189:    Input Parameters:
4190: +    package - name of the package, for example petsc or superlu
4191: .    ftype - the type of factorization supported by the package
4192: -    mtype - the matrix type that works with this package

4194:    Output Parameters:
4195: +   foundpackage - PETSC_TRUE if the package was registered
4196: .   foundmtype - PETSC_TRUE if the package supports the requested mtype
4197: -   getfactor - routine that will create the factored matrix ready to be used or NULL if not found

4199:     Level: intermediate

4201: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4202: @*/
4203: PetscErrorCode MatSolverPackageGet(const MatSolverPackage package,const MatType mtype,MatFactorType ftype,PetscBool *foundpackage,PetscBool *foundmtype,PetscErrorCode (**getfactor)(Mat,MatFactorType,Mat*))
4204: {
4205:   PetscErrorCode                 ierr;
4206:   MatSolverPackageHolder         next = MatSolverPackageHolders;
4207:   PetscBool                      flg;
4208:   MatSolverPackageForSpecifcType inext;

4211:   if (foundpackage) *foundpackage = PETSC_FALSE;
4212:   if (foundmtype)   *foundmtype   = PETSC_FALSE;
4213:   if (getfactor)    *getfactor    = NULL;

4215:   if (package) {
4216:     while (next) {
4217:       PetscStrcasecmp(package,next->name,&flg);
4218:       if (flg) {
4219:         if (foundpackage) *foundpackage = PETSC_TRUE;
4220:         inext = next->handlers;
4221:         while (inext) {
4222:           PetscStrbeginswith(mtype,inext->mtype,&flg);
4223:           if (flg) {
4224:             if (foundmtype) *foundmtype = PETSC_TRUE;
4225:             if (getfactor)  *getfactor  = inext->getfactor[(int)ftype-1];
4226:             return(0);
4227:           }
4228:           inext = inext->next;
4229:         }
4230:       }
4231:       next = next->next;
4232:     }
4233:   } else {
4234:     while (next) {
4235:       inext = next->handlers;
4236:       while (inext) {
4237:         PetscStrbeginswith(mtype,inext->mtype,&flg);
4238:         if (flg && inext->getfactor[(int)ftype-1]) {
4239:           if (foundpackage) *foundpackage = PETSC_TRUE;
4240:           if (foundmtype)   *foundmtype   = PETSC_TRUE;
4241:           if (getfactor)    *getfactor    = inext->getfactor[(int)ftype-1];
4242:           return(0);
4243:         }
4244:         inext = inext->next;
4245:       }
4246:       next = next->next;
4247:     }
4248:   }
4249:   return(0);
4250: }

4252: PetscErrorCode MatSolverPackageDestroy(void)
4253: {
4254:   PetscErrorCode                 ierr;
4255:   MatSolverPackageHolder         next = MatSolverPackageHolders,prev;
4256:   MatSolverPackageForSpecifcType inext,iprev;

4259:   while (next) {
4260:     PetscFree(next->name);
4261:     inext = next->handlers;
4262:     while (inext) {
4263:       PetscFree(inext->mtype);
4264:       iprev = inext;
4265:       inext = inext->next;
4266:       PetscFree(iprev);
4267:     }
4268:     prev = next;
4269:     next = next->next;
4270:     PetscFree(prev);
4271:   }
4272:   MatSolverPackageHolders = NULL;
4273:   return(0);
4274: }

4276: /*@C
4277:    MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4279:    Collective on Mat

4281:    Input Parameters:
4282: +  mat - the matrix
4283: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4284: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4286:    Output Parameters:
4287: .  f - the factor matrix used with MatXXFactorSymbolic() calls

4289:    Notes:
4290:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4291:      such as pastix, superlu, mumps etc.

4293:       PETSc must have been ./configure to use the external solver, using the option --download-package

4295:    Level: intermediate

4297: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4298: @*/
4299: PetscErrorCode MatGetFactor(Mat mat, const MatSolverPackage type,MatFactorType ftype,Mat *f)
4300: {
4301:   PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*);
4302:   PetscBool      foundpackage,foundmtype;


4308:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4309:   MatCheckPreallocated(mat,1);

4311:   MatSolverPackageGet(type,((PetscObject)mat)->type_name,ftype,&foundpackage,&foundmtype,&conv);
4312:   if (!foundpackage) {
4313:     if (type) {
4314:       SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate solver package %s. Perhaps you must ./configure with --download-%s",type,type);
4315:     } else {
4316:       SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate a solver package. Perhaps you must ./configure with --download-<package>");
4317:     }
4318:   }

4320:   if (!foundmtype) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverPackage %s does not support matrix type %s",type,((PetscObject)mat)->type_name);
4321:   if (!conv) SETERRQ3(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverPackage %s does not support factorization type %s for  matrix type %s",type,MatFactorTypes[ftype],((PetscObject)mat)->type_name);

4323:   (*conv)(mat,ftype,f);
4324:   return(0);
4325: }

4327: /*@C
4328:    MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type

4330:    Not Collective

4332:    Input Parameters:
4333: +  mat - the matrix
4334: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4335: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4337:    Output Parameter:
4338: .    flg - PETSC_TRUE if the factorization is available

4340:    Notes:
4341:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4342:      such as pastix, superlu, mumps etc.

4344:       PETSc must have been ./configure to use the external solver, using the option --download-package

4346:    Level: intermediate

4348: .seealso: MatCopy(), MatDuplicate(), MatGetFactor()
4349: @*/
4350: PetscErrorCode MatGetFactorAvailable(Mat mat, const MatSolverPackage type,MatFactorType ftype,PetscBool  *flg)
4351: {
4352:   PetscErrorCode ierr, (*gconv)(Mat,MatFactorType,Mat*);


4358:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4359:   MatCheckPreallocated(mat,1);

4361:   *flg = PETSC_FALSE;
4362:   MatSolverPackageGet(type,((PetscObject)mat)->type_name,ftype,NULL,NULL,&gconv);
4363:   if (gconv) {
4364:     *flg = PETSC_TRUE;
4365:   }
4366:   return(0);
4367: }

4369:  #include <petscdmtypes.h>

4371: /*@
4372:    MatDuplicate - Duplicates a matrix including the non-zero structure.

4374:    Collective on Mat

4376:    Input Parameters:
4377: +  mat - the matrix
4378: -  op - One of MAT_DO_NOT_COPY_VALUES, MAT_COPY_VALUES, or MAT_SHARE_NONZERO_PATTERN.
4379:         See the manual page for MatDuplicateOption for an explanation of these options.

4381:    Output Parameter:
4382: .  M - pointer to place new matrix

4384:    Level: intermediate

4386:    Concepts: matrices^duplicating

4388:    Notes: You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN.

4390: .seealso: MatCopy(), MatConvert(), MatDuplicateOption
4391: @*/
4392: PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M)
4393: {
4395:   Mat            B;
4396:   PetscInt       i;
4397:   DM             dm;

4403:   if (op == MAT_COPY_VALUES && !mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MAT_COPY_VALUES not allowed for unassembled matrix");
4404:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4405:   MatCheckPreallocated(mat,1);

4407:   *M = 0;
4408:   if (!mat->ops->duplicate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not written for this matrix type");
4409:   PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4410:   (*mat->ops->duplicate)(mat,op,M);
4411:   B    = *M;

4413:   B->stencil.dim = mat->stencil.dim;
4414:   B->stencil.noc = mat->stencil.noc;
4415:   for (i=0; i<=mat->stencil.dim; i++) {
4416:     B->stencil.dims[i]   = mat->stencil.dims[i];
4417:     B->stencil.starts[i] = mat->stencil.starts[i];
4418:   }

4420:   B->nooffproczerorows = mat->nooffproczerorows;
4421:   B->nooffprocentries  = mat->nooffprocentries;

4423:   PetscObjectQuery((PetscObject) mat, "__PETSc_dm", (PetscObject*) &dm);
4424:   if (dm) {
4425:     PetscObjectCompose((PetscObject) B, "__PETSc_dm", (PetscObject) dm);
4426:   }
4427:   PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4428:   PetscObjectStateIncrease((PetscObject)B);
4429:   return(0);
4430: }

4432: /*@
4433:    MatGetDiagonal - Gets the diagonal of a matrix.

4435:    Logically Collective on Mat and Vec

4437:    Input Parameters:
4438: +  mat - the matrix
4439: -  v - the vector for storing the diagonal

4441:    Output Parameter:
4442: .  v - the diagonal of the matrix

4444:    Level: intermediate

4446:    Note:
4447:    Currently only correct in parallel for square matrices.

4449:    Concepts: matrices^accessing diagonals

4451: .seealso: MatGetRow(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs()
4452: @*/
4453: PetscErrorCode MatGetDiagonal(Mat mat,Vec v)
4454: {

4461:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4462:   if (!mat->ops->getdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4463:   MatCheckPreallocated(mat,1);

4465:   (*mat->ops->getdiagonal)(mat,v);
4466:   PetscObjectStateIncrease((PetscObject)v);
4467:   return(0);
4468: }

4470: /*@C
4471:    MatGetRowMin - Gets the minimum value (of the real part) of each
4472:         row of the matrix

4474:    Logically Collective on Mat and Vec

4476:    Input Parameters:
4477: .  mat - the matrix

4479:    Output Parameter:
4480: +  v - the vector for storing the maximums
4481: -  idx - the indices of the column found for each row (optional)

4483:    Level: intermediate

4485:    Notes: The result of this call are the same as if one converted the matrix to dense format
4486:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4488:     This code is only implemented for a couple of matrix formats.

4490:    Concepts: matrices^getting row maximums

4492: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs(),
4493:           MatGetRowMax()
4494: @*/
4495: PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[])
4496: {

4503:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4504:   if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4505:   MatCheckPreallocated(mat,1);

4507:   (*mat->ops->getrowmin)(mat,v,idx);
4508:   PetscObjectStateIncrease((PetscObject)v);
4509:   return(0);
4510: }

4512: /*@C
4513:    MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4514:         row of the matrix

4516:    Logically Collective on Mat and Vec

4518:    Input Parameters:
4519: .  mat - the matrix

4521:    Output Parameter:
4522: +  v - the vector for storing the minimums
4523: -  idx - the indices of the column found for each row (or NULL if not needed)

4525:    Level: intermediate

4527:    Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that
4528:     row is 0 (the first column).

4530:     This code is only implemented for a couple of matrix formats.

4532:    Concepts: matrices^getting row maximums

4534: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin()
4535: @*/
4536: PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[])
4537: {

4544:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4545:   if (!mat->ops->getrowminabs) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4546:   MatCheckPreallocated(mat,1);
4547:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4549:   (*mat->ops->getrowminabs)(mat,v,idx);
4550:   PetscObjectStateIncrease((PetscObject)v);
4551:   return(0);
4552: }

4554: /*@C
4555:    MatGetRowMax - Gets the maximum value (of the real part) of each
4556:         row of the matrix

4558:    Logically Collective on Mat and Vec

4560:    Input Parameters:
4561: .  mat - the matrix

4563:    Output Parameter:
4564: +  v - the vector for storing the maximums
4565: -  idx - the indices of the column found for each row (optional)

4567:    Level: intermediate

4569:    Notes: The result of this call are the same as if one converted the matrix to dense format
4570:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4572:     This code is only implemented for a couple of matrix formats.

4574:    Concepts: matrices^getting row maximums

4576: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs(), MatGetRowMin()
4577: @*/
4578: PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[])
4579: {

4586:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4587:   if (!mat->ops->getrowmax) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4588:   MatCheckPreallocated(mat,1);

4590:   (*mat->ops->getrowmax)(mat,v,idx);
4591:   PetscObjectStateIncrease((PetscObject)v);
4592:   return(0);
4593: }

4595: /*@C
4596:    MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
4597:         row of the matrix

4599:    Logically Collective on Mat and Vec

4601:    Input Parameters:
4602: .  mat - the matrix

4604:    Output Parameter:
4605: +  v - the vector for storing the maximums
4606: -  idx - the indices of the column found for each row (or NULL if not needed)

4608:    Level: intermediate

4610:    Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that
4611:     row is 0 (the first column).

4613:     This code is only implemented for a couple of matrix formats.

4615:    Concepts: matrices^getting row maximums

4617: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMin()
4618: @*/
4619: PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[])
4620: {

4627:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4628:   if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4629:   MatCheckPreallocated(mat,1);
4630:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4632:   (*mat->ops->getrowmaxabs)(mat,v,idx);
4633:   PetscObjectStateIncrease((PetscObject)v);
4634:   return(0);
4635: }

4637: /*@
4638:    MatGetRowSum - Gets the sum of each row of the matrix

4640:    Logically or Neighborhood Collective on Mat and Vec

4642:    Input Parameters:
4643: .  mat - the matrix

4645:    Output Parameter:
4646: .  v - the vector for storing the sum of rows

4648:    Level: intermediate

4650:    Notes: This code is slow since it is not currently specialized for different formats

4652:    Concepts: matrices^getting row sums

4654: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMin()
4655: @*/
4656: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
4657: {
4658:   Vec            ones;

4665:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4666:   MatCheckPreallocated(mat,1);
4667:   MatCreateVecs(mat,NULL,&ones);
4668:   VecSet(ones,1.);
4669:   MatMult(mat,ones,v);
4670:   VecDestroy(&ones);
4671:   return(0);
4672: }

4674: /*@
4675:    MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

4677:    Collective on Mat

4679:    Input Parameter:
4680: +  mat - the matrix to transpose
4681: -  reuse - either MAT_INITIAL_MATRIX, MAT_REUSE_MATRIX, or MAT_INPLACE_MATRIX

4683:    Output Parameters:
4684: .  B - the transpose

4686:    Notes:
4687:      If you use MAT_INPLACE_MATRIX then you must pass in &mat for B

4689:      MAT_REUSE_MATRIX causes the B matrix from a previous call to this function with MAT_INITIAL_MATRIX to be used

4691:      Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

4693:    Level: intermediate

4695:    Concepts: matrices^transposing

4697: .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4698: @*/
4699: PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B)
4700: {

4706:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4707:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4708:   if (!mat->ops->transpose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4709:   if (reuse == MAT_INPLACE_MATRIX && mat != *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires last matrix to match first");
4710:   if (reuse == MAT_REUSE_MATRIX && mat == *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Perhaps you mean MAT_INPLACE_MATRIX");
4711:   MatCheckPreallocated(mat,1);

4713:   PetscLogEventBegin(MAT_Transpose,mat,0,0,0);
4714:   (*mat->ops->transpose)(mat,reuse,B);
4715:   PetscLogEventEnd(MAT_Transpose,mat,0,0,0);
4716:   if (B) {PetscObjectStateIncrease((PetscObject)*B);}
4717:   return(0);
4718: }

4720: /*@
4721:    MatIsTranspose - Test whether a matrix is another one's transpose,
4722:         or its own, in which case it tests symmetry.

4724:    Collective on Mat

4726:    Input Parameter:
4727: +  A - the matrix to test
4728: -  B - the matrix to test against, this can equal the first parameter

4730:    Output Parameters:
4731: .  flg - the result

4733:    Notes:
4734:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4735:    has a running time of the order of the number of nonzeros; the parallel
4736:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4738:    Level: intermediate

4740:    Concepts: matrices^transposing, matrix^symmetry

4742: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian()
4743: @*/
4744: PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4745: {
4746:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4752:   PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",&f);
4753:   PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",&g);
4754:   *flg = PETSC_FALSE;
4755:   if (f && g) {
4756:     if (f == g) {
4757:       (*f)(A,B,tol,flg);
4758:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test");
4759:   } else {
4760:     MatType mattype;
4761:     if (!f) {
4762:       MatGetType(A,&mattype);
4763:     } else {
4764:       MatGetType(B,&mattype);
4765:     }
4766:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for transpose",mattype);
4767:   }
4768:   return(0);
4769: }

4771: /*@
4772:    MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate.

4774:    Collective on Mat

4776:    Input Parameter:
4777: +  mat - the matrix to transpose and complex conjugate
4778: -  reuse - MAT_INITIAL_MATRIX to create a new matrix, MAT_INPLACE_MATRIX to reuse the first argument to store the transpose

4780:    Output Parameters:
4781: .  B - the Hermitian

4783:    Level: intermediate

4785:    Concepts: matrices^transposing, complex conjugatex

4787: .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4788: @*/
4789: PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B)
4790: {

4794:   MatTranspose(mat,reuse,B);
4795: #if defined(PETSC_USE_COMPLEX)
4796:   MatConjugate(*B);
4797: #endif
4798:   return(0);
4799: }

4801: /*@
4802:    MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

4804:    Collective on Mat

4806:    Input Parameter:
4807: +  A - the matrix to test
4808: -  B - the matrix to test against, this can equal the first parameter

4810:    Output Parameters:
4811: .  flg - the result

4813:    Notes:
4814:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4815:    has a running time of the order of the number of nonzeros; the parallel
4816:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4818:    Level: intermediate

4820:    Concepts: matrices^transposing, matrix^symmetry

4822: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose()
4823: @*/
4824: PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4825: {
4826:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4832:   PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",&f);
4833:   PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",&g);
4834:   if (f && g) {
4835:     if (f==g) {
4836:       (*f)(A,B,tol,flg);
4837:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test");
4838:   }
4839:   return(0);
4840: }

4842: /*@
4843:    MatPermute - Creates a new matrix with rows and columns permuted from the
4844:    original.

4846:    Collective on Mat

4848:    Input Parameters:
4849: +  mat - the matrix to permute
4850: .  row - row permutation, each processor supplies only the permutation for its rows
4851: -  col - column permutation, each processor supplies only the permutation for its columns

4853:    Output Parameters:
4854: .  B - the permuted matrix

4856:    Level: advanced

4858:    Note:
4859:    The index sets map from row/col of permuted matrix to row/col of original matrix.
4860:    The index sets should be on the same communicator as Mat and have the same local sizes.

4862:    Concepts: matrices^permuting

4864: .seealso: MatGetOrdering(), ISAllGather()

4866: @*/
4867: PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B)
4868: {

4877:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4878:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4879:   if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name);
4880:   MatCheckPreallocated(mat,1);

4882:   (*mat->ops->permute)(mat,row,col,B);
4883:   PetscObjectStateIncrease((PetscObject)*B);
4884:   return(0);
4885: }

4887: /*@
4888:    MatEqual - Compares two matrices.

4890:    Collective on Mat

4892:    Input Parameters:
4893: +  A - the first matrix
4894: -  B - the second matrix

4896:    Output Parameter:
4897: .  flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise.

4899:    Level: intermediate

4901:    Concepts: matrices^equality between
4902: @*/
4903: PetscErrorCode MatEqual(Mat A,Mat B,PetscBool  *flg)
4904: {

4914:   MatCheckPreallocated(B,2);
4915:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4916:   if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4917:   if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D %D %D",A->rmap->N,B->rmap->N,A->cmap->N,B->cmap->N);
4918:   if (!A->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
4919:   if (!B->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name);
4920:   if (A->ops->equal != B->ops->equal) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"A is type: %s\nB is type: %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name);
4921:   MatCheckPreallocated(A,1);

4923:   (*A->ops->equal)(A,B,flg);
4924:   return(0);
4925: }

4927: /*@C
4928:    MatDiagonalScale - Scales a matrix on the left and right by diagonal
4929:    matrices that are stored as vectors.  Either of the two scaling
4930:    matrices can be NULL.

4932:    Collective on Mat

4934:    Input Parameters:
4935: +  mat - the matrix to be scaled
4936: .  l - the left scaling vector (or NULL)
4937: -  r - the right scaling vector (or NULL)

4939:    Notes:
4940:    MatDiagonalScale() computes A = LAR, where
4941:    L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
4942:    The L scales the rows of the matrix, the R scales the columns of the matrix.

4944:    Level: intermediate

4946:    Concepts: matrices^diagonal scaling
4947:    Concepts: diagonal scaling of matrices

4949: .seealso: MatScale()
4950: @*/
4951: PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r)
4952: {

4958:   if (!mat->ops->diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4961:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4962:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4963:   MatCheckPreallocated(mat,1);

4965:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
4966:   (*mat->ops->diagonalscale)(mat,l,r);
4967:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
4968:   PetscObjectStateIncrease((PetscObject)mat);
4969: #if defined(PETSC_HAVE_CUSP)
4970:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
4971:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
4972:   }
4973: #elif defined(PETSC_HAVE_VIENNACL)
4974:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
4975:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
4976:   }
4977: #elif defined(PETSC_HAVE_VECCUDA)
4978:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
4979:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
4980:   }
4981: #endif
4982:   return(0);
4983: }

4985: /*@
4986:     MatScale - Scales all elements of a matrix by a given number.

4988:     Logically Collective on Mat

4990:     Input Parameters:
4991: +   mat - the matrix to be scaled
4992: -   a  - the scaling value

4994:     Output Parameter:
4995: .   mat - the scaled matrix

4997:     Level: intermediate

4999:     Concepts: matrices^scaling all entries

5001: .seealso: MatDiagonalScale()
5002: @*/
5003: PetscErrorCode MatScale(Mat mat,PetscScalar a)
5004: {

5010:   if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5011:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5012:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5014:   MatCheckPreallocated(mat,1);

5016:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
5017:   if (a != (PetscScalar)1.0) {
5018:     (*mat->ops->scale)(mat,a);
5019:     PetscObjectStateIncrease((PetscObject)mat);
5020: #if defined(PETSC_HAVE_CUSP)
5021:     if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5022:       mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5023:     }
5024: #elif defined(PETSC_HAVE_VIENNACL)
5025:     if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5026:       mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5027:     }
5028: #elif defined(PETSC_HAVE_VECCUDA)
5029:     if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5030:       mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5031:     }
5032: #endif
5033:   }
5034:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
5035:   return(0);
5036: }

5038: /*@
5039:    MatNorm - Calculates various norms of a matrix.

5041:    Collective on Mat

5043:    Input Parameters:
5044: +  mat - the matrix
5045: -  type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY

5047:    Output Parameters:
5048: .  nrm - the resulting norm

5050:    Level: intermediate

5052:    Concepts: matrices^norm
5053:    Concepts: norm^of matrix
5054: @*/
5055: PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm)
5056: {


5064:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5065:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5066:   if (!mat->ops->norm) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5067:   MatCheckPreallocated(mat,1);

5069:   (*mat->ops->norm)(mat,type,nrm);
5070:   return(0);
5071: }

5073: /*
5074:      This variable is used to prevent counting of MatAssemblyBegin() that
5075:    are called from within a MatAssemblyEnd().
5076: */
5077: static PetscInt MatAssemblyEnd_InUse = 0;
5078: /*@
5079:    MatAssemblyBegin - Begins assembling the matrix.  This routine should
5080:    be called after completing all calls to MatSetValues().

5082:    Collective on Mat

5084:    Input Parameters:
5085: +  mat - the matrix
5086: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5088:    Notes:
5089:    MatSetValues() generally caches the values.  The matrix is ready to
5090:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5091:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5092:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5093:    using the matrix.

5095:    ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the
5096:    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
5097:    a global collective operation requring all processes that share the matrix.

5099:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5100:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5101:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5103:    Level: beginner

5105:    Concepts: matrices^assembling

5107: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled()
5108: @*/
5109: PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type)
5110: {

5116:   MatCheckPreallocated(mat,1);
5117:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5118:   if (mat->assembled) {
5119:     mat->was_assembled = PETSC_TRUE;
5120:     mat->assembled     = PETSC_FALSE;
5121:   }
5122:   if (!MatAssemblyEnd_InUse) {
5123:     PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);
5124:     if (mat->ops->assemblybegin) {(*mat->ops->assemblybegin)(mat,type);}
5125:     PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);
5126:   } else if (mat->ops->assemblybegin) {
5127:     (*mat->ops->assemblybegin)(mat,type);
5128:   }
5129:   return(0);
5130: }

5132: /*@
5133:    MatAssembled - Indicates if a matrix has been assembled and is ready for
5134:      use; for example, in matrix-vector product.

5136:    Not Collective

5138:    Input Parameter:
5139: .  mat - the matrix

5141:    Output Parameter:
5142: .  assembled - PETSC_TRUE or PETSC_FALSE

5144:    Level: advanced

5146:    Concepts: matrices^assembled?

5148: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin()
5149: @*/
5150: PetscErrorCode MatAssembled(Mat mat,PetscBool  *assembled)
5151: {
5156:   *assembled = mat->assembled;
5157:   return(0);
5158: }

5160: /*@
5161:    MatAssemblyEnd - Completes assembling the matrix.  This routine should
5162:    be called after MatAssemblyBegin().

5164:    Collective on Mat

5166:    Input Parameters:
5167: +  mat - the matrix
5168: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5170:    Options Database Keys:
5171: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly()
5172: .  -mat_view ::ascii_info_detail - Prints more detailed info
5173: .  -mat_view - Prints matrix in ASCII format
5174: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
5175: .  -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX().
5176: .  -display <name> - Sets display name (default is host)
5177: .  -draw_pause <sec> - Sets number of seconds to pause after display
5178: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See Users-Manual: Chapter 12 Using MATLAB with PETSc )
5179: .  -viewer_socket_machine <machine> - Machine to use for socket
5180: .  -viewer_socket_port <port> - Port number to use for socket
5181: -  -mat_view binary:filename[:append] - Save matrix to file in binary format

5183:    Notes:
5184:    MatSetValues() generally caches the values.  The matrix is ready to
5185:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5186:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5187:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5188:    using the matrix.

5190:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5191:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5192:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5194:    Level: beginner

5196: .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), PetscDrawCreate(), MatView(), MatAssembled(), PetscViewerSocketOpen()
5197: @*/
5198: PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type)
5199: {
5200:   PetscErrorCode  ierr;
5201:   static PetscInt inassm = 0;
5202:   PetscBool       flg    = PETSC_FALSE;


5208:   inassm++;
5209:   MatAssemblyEnd_InUse++;
5210:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5211:     PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);
5212:     if (mat->ops->assemblyend) {
5213:       (*mat->ops->assemblyend)(mat,type);
5214:     }
5215:     PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);
5216:   } else if (mat->ops->assemblyend) {
5217:     (*mat->ops->assemblyend)(mat,type);
5218:   }

5220:   /* Flush assembly is not a true assembly */
5221:   if (type != MAT_FLUSH_ASSEMBLY) {
5222:     mat->assembled = PETSC_TRUE; mat->num_ass++;
5223:   }
5224:   mat->insertmode = NOT_SET_VALUES;
5225:   MatAssemblyEnd_InUse--;
5226:   PetscObjectStateIncrease((PetscObject)mat);
5227:   if (!mat->symmetric_eternal) {
5228:     mat->symmetric_set              = PETSC_FALSE;
5229:     mat->hermitian_set              = PETSC_FALSE;
5230:     mat->structurally_symmetric_set = PETSC_FALSE;
5231:   }
5232: #if defined(PETSC_HAVE_CUSP)
5233:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5234:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5235:   }
5236: #elif defined(PETSC_HAVE_VIENNACL)
5237:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5238:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5239:   }
5240: #elif defined(PETSC_HAVE_VECCUDA)
5241:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5242:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5243:   }
5244: #endif
5245:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5246:     MatViewFromOptions(mat,NULL,"-mat_view");

5248:     if (mat->checksymmetryonassembly) {
5249:       MatIsSymmetric(mat,mat->checksymmetrytol,&flg);
5250:       if (flg) {
5251:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5252:       } else {
5253:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is not symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5254:       }
5255:     }
5256:     if (mat->nullsp && mat->checknullspaceonassembly) {
5257:       MatNullSpaceTest(mat->nullsp,mat,NULL);
5258:     }
5259:   }
5260:   inassm--;
5261:   return(0);
5262: }

5264: /*@
5265:    MatSetOption - Sets a parameter option for a matrix. Some options
5266:    may be specific to certain storage formats.  Some options
5267:    determine how values will be inserted (or added). Sorted,
5268:    row-oriented input will generally assemble the fastest. The default
5269:    is row-oriented.

5271:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5273:    Input Parameters:
5274: +  mat - the matrix
5275: .  option - the option, one of those listed below (and possibly others),
5276: -  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5278:   Options Describing Matrix Structure:
5279: +    MAT_SPD - symmetric positive definite
5280: .    MAT_SYMMETRIC - symmetric in terms of both structure and value
5281: .    MAT_HERMITIAN - transpose is the complex conjugation
5282: .    MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure
5283: -    MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag
5284:                             you set to be kept with all future use of the matrix
5285:                             including after MatAssemblyBegin/End() which could
5286:                             potentially change the symmetry structure, i.e. you
5287:                             KNOW the matrix will ALWAYS have the property you set.


5290:    Options For Use with MatSetValues():
5291:    Insert a logically dense subblock, which can be
5292: .    MAT_ROW_ORIENTED - row-oriented (default)

5294:    Note these options reflect the data you pass in with MatSetValues(); it has
5295:    nothing to do with how the data is stored internally in the matrix
5296:    data structure.

5298:    When (re)assembling a matrix, we can restrict the input for
5299:    efficiency/debugging purposes.  These options include:
5300: +    MAT_NEW_NONZERO_LOCATIONS - additional insertions will be allowed if they generate a new nonzero (slow)
5301: .    MAT_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only)
5302: .    MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries
5303: .    MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry
5304: .    MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly
5305: .    MAT_NO_OFF_PROC_ENTRIES - you know each process will only set values for its own rows, will generate an error if
5306:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5307:         performance for very large process counts.
5308: -    MAT_SUBSET_OFF_PROC_ENTRIES - you know that the first assembly after setting this flag will set a superset
5309:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5310:         functions, instead sending only neighbor messages.

5312:    Notes:
5313:    Except for MAT_UNUSED_NONZERO_LOCATION_ERR and  MAT_ROW_ORIENTED all processes that share the matrix must pass the same value in flg!

5315:    Some options are relevant only for particular matrix types and
5316:    are thus ignored by others.  Other options are not supported by
5317:    certain matrix types and will generate an error message if set.

5319:    If using a Fortran 77 module to compute a matrix, one may need to
5320:    use the column-oriented option (or convert to the row-oriented
5321:    format).

5323:    MAT_NEW_NONZERO_LOCATIONS set to PETSC_FALSE indicates that any add or insertion
5324:    that would generate a new entry in the nonzero structure is instead
5325:    ignored.  Thus, if memory has not alredy been allocated for this particular
5326:    data, then the insertion is ignored. For dense matrices, in which
5327:    the entire array is allocated, no entries are ever ignored.
5328:    Set after the first MatAssemblyEnd(). If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5330:    MAT_NEW_NONZERO_LOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5331:    that would generate a new entry in the nonzero structure instead produces
5332:    an error. (Currently supported for AIJ and BAIJ formats only.) If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5334:    MAT_NEW_NONZERO_ALLOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5335:    that would generate a new entry that has not been preallocated will
5336:    instead produce an error. (Currently supported for AIJ and BAIJ formats
5337:    only.) This is a useful flag when debugging matrix memory preallocation.
5338:    If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5340:    MAT_IGNORE_OFF_PROC_ENTRIES set to PETSC_TRUE indicates entries destined for
5341:    other processors should be dropped, rather than stashed.
5342:    This is useful if you know that the "owning" processor is also
5343:    always generating the correct matrix entries, so that PETSc need
5344:    not transfer duplicate entries generated on another processor.

5346:    MAT_USE_HASH_TABLE indicates that a hash table be used to improve the
5347:    searches during matrix assembly. When this flag is set, the hash table
5348:    is created during the first Matrix Assembly. This hash table is
5349:    used the next time through, during MatSetVaules()/MatSetVaulesBlocked()
5350:    to improve the searching of indices. MAT_NEW_NONZERO_LOCATIONS flag
5351:    should be used with MAT_USE_HASH_TABLE flag. This option is currently
5352:    supported by MATMPIBAIJ format only.

5354:    MAT_KEEP_NONZERO_PATTERN indicates when MatZeroRows() is called the zeroed entries
5355:    are kept in the nonzero structure

5357:    MAT_IGNORE_ZERO_ENTRIES - for AIJ/IS matrices this will stop zero values from creating
5358:    a zero location in the matrix

5360:    MAT_USE_INODES - indicates using inode version of the code - works with AIJ matrix types

5362:    MAT_NO_OFF_PROC_ZERO_ROWS - you know each process will only zero its own rows. This avoids all reductions in the
5363:         zero row routines and thus improves performance for very large process counts.

5365:    MAT_IGNORE_LOWER_TRIANGULAR - For SBAIJ matrices will ignore any insertions you make in the lower triangular
5366:         part of the matrix (since they should match the upper triangular part).

5368:    Notes: Can only be called after MatSetSizes() and MatSetType() have been set.

5370:    Level: intermediate

5372:    Concepts: matrices^setting options

5374: .seealso:  MatOption, Mat

5376: @*/
5377: PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg)
5378: {

5384:   if (op > 0) {
5387:   }

5389:   if (((int) op) <= MAT_OPTION_MIN || ((int) op) >= MAT_OPTION_MAX) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Options %d is out of range",(int)op);
5390:   if (!((PetscObject)mat)->type_name) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_TYPENOTSET,"Cannot set options until type and size have been set, see MatSetType() and MatSetSizes()");

5392:   switch (op) {
5393:   case MAT_NO_OFF_PROC_ENTRIES:
5394:     mat->nooffprocentries = flg;
5395:     return(0);
5396:     break;
5397:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5398:     mat->subsetoffprocentries = flg;
5399:     return(0);
5400:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5401:     mat->nooffproczerorows = flg;
5402:     return(0);
5403:     break;
5404:   case MAT_SPD:
5405:     mat->spd_set = PETSC_TRUE;
5406:     mat->spd     = flg;
5407:     if (flg) {
5408:       mat->symmetric                  = PETSC_TRUE;
5409:       mat->structurally_symmetric     = PETSC_TRUE;
5410:       mat->symmetric_set              = PETSC_TRUE;
5411:       mat->structurally_symmetric_set = PETSC_TRUE;
5412:     }
5413:     break;
5414:   case MAT_SYMMETRIC:
5415:     mat->symmetric = flg;
5416:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5417:     mat->symmetric_set              = PETSC_TRUE;
5418:     mat->structurally_symmetric_set = flg;
5419: #if !defined(PETSC_USE_COMPLEX)
5420:     mat->hermitian     = flg;
5421:     mat->hermitian_set = PETSC_TRUE;
5422: #endif
5423:     break;
5424:   case MAT_HERMITIAN:
5425:     mat->hermitian = flg;
5426:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5427:     mat->hermitian_set              = PETSC_TRUE;
5428:     mat->structurally_symmetric_set = flg;
5429: #if !defined(PETSC_USE_COMPLEX)
5430:     mat->symmetric     = flg;
5431:     mat->symmetric_set = PETSC_TRUE;
5432: #endif
5433:     break;
5434:   case MAT_STRUCTURALLY_SYMMETRIC:
5435:     mat->structurally_symmetric     = flg;
5436:     mat->structurally_symmetric_set = PETSC_TRUE;
5437:     break;
5438:   case MAT_SYMMETRY_ETERNAL:
5439:     mat->symmetric_eternal = flg;
5440:     break;
5441:   case MAT_STRUCTURE_ONLY:
5442:     mat->structure_only = flg;
5443:     break;
5444:   default:
5445:     break;
5446:   }
5447:   if (mat->ops->setoption) {
5448:     (*mat->ops->setoption)(mat,op,flg);
5449:   }
5450:   return(0);
5451: }

5453: /*@
5454:    MatGetOption - Gets a parameter option that has been set for a matrix.

5456:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5458:    Input Parameters:
5459: +  mat - the matrix
5460: -  option - the option, this only responds to certain options, check the code for which ones

5462:    Output Parameter:
5463: .  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5465:     Notes: Can only be called after MatSetSizes() and MatSetType() have been set.

5467:    Level: intermediate

5469:    Concepts: matrices^setting options

5471: .seealso:  MatOption, MatSetOption()

5473: @*/
5474: PetscErrorCode MatGetOption(Mat mat,MatOption op,PetscBool *flg)
5475: {

5480:   if (((int) op) <= MAT_OPTION_MIN || ((int) op) >= MAT_OPTION_MAX) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Options %d is out of range",(int)op);
5481:   if (!((PetscObject)mat)->type_name) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_TYPENOTSET,"Cannot get options until type and size have been set, see MatSetType() and MatSetSizes()");

5483:   switch (op) {
5484:   case MAT_NO_OFF_PROC_ENTRIES:
5485:     *flg = mat->nooffprocentries;
5486:     break;
5487:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5488:     *flg = mat->nooffproczerorows;
5489:     break;
5490:   case MAT_SYMMETRIC:
5491:     *flg = mat->symmetric;
5492:     break;
5493:   case MAT_HERMITIAN:
5494:     *flg = mat->hermitian;
5495:     break;
5496:   case MAT_STRUCTURALLY_SYMMETRIC:
5497:     *flg = mat->structurally_symmetric;
5498:     break;
5499:   case MAT_SYMMETRY_ETERNAL:
5500:     *flg = mat->symmetric_eternal;
5501:     break;
5502:   case MAT_SPD:
5503:     *flg = mat->spd;
5504:     break;
5505:   default:
5506:     break;
5507:   }
5508:   return(0);
5509: }

5511: /*@
5512:    MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
5513:    this routine retains the old nonzero structure.

5515:    Logically Collective on Mat

5517:    Input Parameters:
5518: .  mat - the matrix

5520:    Level: intermediate

5522:    Notes: 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.
5523:    See the Performance chapter of the users manual for information on preallocating matrices.

5525:    Concepts: matrices^zeroing

5527: .seealso: MatZeroRows()
5528: @*/
5529: PetscErrorCode MatZeroEntries(Mat mat)
5530: {

5536:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5537:   if (mat->insertmode != NOT_SET_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for matrices where you have set values but not yet assembled");
5538:   if (!mat->ops->zeroentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5539:   MatCheckPreallocated(mat,1);

5541:   PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);
5542:   (*mat->ops->zeroentries)(mat);
5543:   PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);
5544:   PetscObjectStateIncrease((PetscObject)mat);
5545: #if defined(PETSC_HAVE_CUSP)
5546:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5547:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5548:   }
5549: #elif defined(PETSC_HAVE_VIENNACL)
5550:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5551:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5552:   }
5553: #elif defined(PETSC_HAVE_VECCUDA)
5554:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5555:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5556:   }
5557: #endif
5558:   return(0);
5559: }

5561: /*@C
5562:    MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
5563:    of a set of rows and columns of a matrix.

5565:    Collective on Mat

5567:    Input Parameters:
5568: +  mat - the matrix
5569: .  numRows - the number of rows to remove
5570: .  rows - the global row indices
5571: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5572: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5573: -  b - optional vector of right hand side, that will be adjusted by provided solution

5575:    Notes:
5576:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5578:    The user can set a value in the diagonal entry (or for the AIJ and
5579:    row formats can optionally remove the main diagonal entry from the
5580:    nonzero structure as well, by passing 0.0 as the final argument).

5582:    For the parallel case, all processes that share the matrix (i.e.,
5583:    those in the communicator used for matrix creation) MUST call this
5584:    routine, regardless of whether any rows being zeroed are owned by
5585:    them.

5587:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5588:    list only rows local to itself).

5590:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5592:    Level: intermediate

5594:    Concepts: matrices^zeroing rows

5596: .seealso: MatZeroRowsIS(), MatZeroRows(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5597:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5598: @*/
5599: PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5600: {

5607:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5608:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5609:   if (!mat->ops->zerorowscolumns) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5610:   MatCheckPreallocated(mat,1);

5612:   (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);
5613:   MatViewFromOptions(mat,NULL,"-mat_view");
5614:   PetscObjectStateIncrease((PetscObject)mat);
5615: #if defined(PETSC_HAVE_CUSP)
5616:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5617:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5618:   }
5619: #elif defined(PETSC_HAVE_VIENNACL)
5620:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5621:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5622:   }
5623: #elif defined(PETSC_HAVE_VECCUDA)
5624:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5625:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5626:   }
5627: #endif
5628:   return(0);
5629: }

5631: /*@C
5632:    MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
5633:    of a set of rows and columns of a matrix.

5635:    Collective on Mat

5637:    Input Parameters:
5638: +  mat - the matrix
5639: .  is - the rows to zero
5640: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5641: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5642: -  b - optional vector of right hand side, that will be adjusted by provided solution

5644:    Notes:
5645:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5647:    The user can set a value in the diagonal entry (or for the AIJ and
5648:    row formats can optionally remove the main diagonal entry from the
5649:    nonzero structure as well, by passing 0.0 as the final argument).

5651:    For the parallel case, all processes that share the matrix (i.e.,
5652:    those in the communicator used for matrix creation) MUST call this
5653:    routine, regardless of whether any rows being zeroed are owned by
5654:    them.

5656:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5657:    list only rows local to itself).

5659:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5661:    Level: intermediate

5663:    Concepts: matrices^zeroing rows

5665: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5666:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRows(), MatZeroRowsColumnsStencil()
5667: @*/
5668: PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5669: {
5671:   PetscInt       numRows;
5672:   const PetscInt *rows;

5679:   ISGetLocalSize(is,&numRows);
5680:   ISGetIndices(is,&rows);
5681:   MatZeroRowsColumns(mat,numRows,rows,diag,x,b);
5682:   ISRestoreIndices(is,&rows);
5683:   return(0);
5684: }

5686: /*@C
5687:    MatZeroRows - Zeros all entries (except possibly the main diagonal)
5688:    of a set of rows of a matrix.

5690:    Collective on Mat

5692:    Input Parameters:
5693: +  mat - the matrix
5694: .  numRows - the number of rows to remove
5695: .  rows - the global row indices
5696: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5697: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5698: -  b - optional vector of right hand side, that will be adjusted by provided solution

5700:    Notes:
5701:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5702:    but does not release memory.  For the dense and block diagonal
5703:    formats this does not alter the nonzero structure.

5705:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5706:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5707:    merely zeroed.

5709:    The user can set a value in the diagonal entry (or for the AIJ and
5710:    row formats can optionally remove the main diagonal entry from the
5711:    nonzero structure as well, by passing 0.0 as the final argument).

5713:    For the parallel case, all processes that share the matrix (i.e.,
5714:    those in the communicator used for matrix creation) MUST call this
5715:    routine, regardless of whether any rows being zeroed are owned by
5716:    them.

5718:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5719:    list only rows local to itself).

5721:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5722:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5724:    Level: intermediate

5726:    Concepts: matrices^zeroing rows

5728: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5729:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5730: @*/
5731: PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5732: {

5739:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5740:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5741:   if (!mat->ops->zerorows) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5742:   MatCheckPreallocated(mat,1);

5744:   (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);
5745:   MatViewFromOptions(mat,NULL,"-mat_view");
5746:   PetscObjectStateIncrease((PetscObject)mat);
5747: #if defined(PETSC_HAVE_CUSP)
5748:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5749:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5750:   }
5751: #elif defined(PETSC_HAVE_VIENNACL)
5752:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5753:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5754:   }
5755: #elif defined(PETSC_HAVE_VECCUDA)
5756:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5757:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5758:   }
5759: #endif
5760:   return(0);
5761: }

5763: /*@C
5764:    MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
5765:    of a set of rows of a matrix.

5767:    Collective on Mat

5769:    Input Parameters:
5770: +  mat - the matrix
5771: .  is - index set of rows to remove
5772: .  diag - value put in all diagonals of eliminated rows
5773: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5774: -  b - optional vector of right hand side, that will be adjusted by provided solution

5776:    Notes:
5777:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5778:    but does not release memory.  For the dense and block diagonal
5779:    formats this does not alter the nonzero structure.

5781:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5782:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5783:    merely zeroed.

5785:    The user can set a value in the diagonal entry (or for the AIJ and
5786:    row formats can optionally remove the main diagonal entry from the
5787:    nonzero structure as well, by passing 0.0 as the final argument).

5789:    For the parallel case, all processes that share the matrix (i.e.,
5790:    those in the communicator used for matrix creation) MUST call this
5791:    routine, regardless of whether any rows being zeroed are owned by
5792:    them.

5794:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5795:    list only rows local to itself).

5797:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5798:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5800:    Level: intermediate

5802:    Concepts: matrices^zeroing rows

5804: .seealso: MatZeroRows(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5805:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5806: @*/
5807: PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5808: {
5809:   PetscInt       numRows;
5810:   const PetscInt *rows;

5817:   ISGetLocalSize(is,&numRows);
5818:   ISGetIndices(is,&rows);
5819:   MatZeroRows(mat,numRows,rows,diag,x,b);
5820:   ISRestoreIndices(is,&rows);
5821:   return(0);
5822: }

5824: /*@C
5825:    MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
5826:    of a set of rows of a matrix. These rows must be local to the process.

5828:    Collective on Mat

5830:    Input Parameters:
5831: +  mat - the matrix
5832: .  numRows - the number of rows to remove
5833: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5834: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5835: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5836: -  b - optional vector of right hand side, that will be adjusted by provided solution

5838:    Notes:
5839:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5840:    but does not release memory.  For the dense and block diagonal
5841:    formats this does not alter the nonzero structure.

5843:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5844:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5845:    merely zeroed.

5847:    The user can set a value in the diagonal entry (or for the AIJ and
5848:    row formats can optionally remove the main diagonal entry from the
5849:    nonzero structure as well, by passing 0.0 as the final argument).

5851:    For the parallel case, all processes that share the matrix (i.e.,
5852:    those in the communicator used for matrix creation) MUST call this
5853:    routine, regardless of whether any rows being zeroed are owned by
5854:    them.

5856:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5857:    list only rows local to itself).

5859:    The grid coordinates are across the entire grid, not just the local portion

5861:    In Fortran idxm and idxn should be declared as
5862: $     MatStencil idxm(4,m)
5863:    and the values inserted using
5864: $    idxm(MatStencil_i,1) = i
5865: $    idxm(MatStencil_j,1) = j
5866: $    idxm(MatStencil_k,1) = k
5867: $    idxm(MatStencil_c,1) = c
5868:    etc

5870:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5871:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5872:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5873:    DM_BOUNDARY_PERIODIC boundary type.

5875:    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
5876:    a single value per point) you can skip filling those indices.

5878:    Level: intermediate

5880:    Concepts: matrices^zeroing rows

5882: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsl(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5883:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5884: @*/
5885: PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5886: {
5887:   PetscInt       dim     = mat->stencil.dim;
5888:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5889:   PetscInt       *dims   = mat->stencil.dims+1;
5890:   PetscInt       *starts = mat->stencil.starts;
5891:   PetscInt       *dxm    = (PetscInt*) rows;
5892:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


5900:   PetscMalloc1(numRows, &jdxm);
5901:   for (i = 0; i < numRows; ++i) {
5902:     /* Skip unused dimensions (they are ordered k, j, i, c) */
5903:     for (j = 0; j < 3-sdim; ++j) dxm++;
5904:     /* Local index in X dir */
5905:     tmp = *dxm++ - starts[0];
5906:     /* Loop over remaining dimensions */
5907:     for (j = 0; j < dim-1; ++j) {
5908:       /* If nonlocal, set index to be negative */
5909:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
5910:       /* Update local index */
5911:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
5912:     }
5913:     /* Skip component slot if necessary */
5914:     if (mat->stencil.noc) dxm++;
5915:     /* Local row number */
5916:     if (tmp >= 0) {
5917:       jdxm[numNewRows++] = tmp;
5918:     }
5919:   }
5920:   MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);
5921:   PetscFree(jdxm);
5922:   return(0);
5923: }

5925: /*@C
5926:    MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
5927:    of a set of rows and columns of a matrix.

5929:    Collective on Mat

5931:    Input Parameters:
5932: +  mat - the matrix
5933: .  numRows - the number of rows/columns to remove
5934: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5935: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5936: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5937: -  b - optional vector of right hand side, that will be adjusted by provided solution

5939:    Notes:
5940:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5941:    but does not release memory.  For the dense and block diagonal
5942:    formats this does not alter the nonzero structure.

5944:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5945:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5946:    merely zeroed.

5948:    The user can set a value in the diagonal entry (or for the AIJ and
5949:    row formats can optionally remove the main diagonal entry from the
5950:    nonzero structure as well, by passing 0.0 as the final argument).

5952:    For the parallel case, all processes that share the matrix (i.e.,
5953:    those in the communicator used for matrix creation) MUST call this
5954:    routine, regardless of whether any rows being zeroed are owned by
5955:    them.

5957:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5958:    list only rows local to itself, but the row/column numbers are given in local numbering).

5960:    The grid coordinates are across the entire grid, not just the local portion

5962:    In Fortran idxm and idxn should be declared as
5963: $     MatStencil idxm(4,m)
5964:    and the values inserted using
5965: $    idxm(MatStencil_i,1) = i
5966: $    idxm(MatStencil_j,1) = j
5967: $    idxm(MatStencil_k,1) = k
5968: $    idxm(MatStencil_c,1) = c
5969:    etc

5971:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5972:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5973:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5974:    DM_BOUNDARY_PERIODIC boundary type.

5976:    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
5977:    a single value per point) you can skip filling those indices.

5979:    Level: intermediate

5981:    Concepts: matrices^zeroing rows

5983: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5984:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRows()
5985: @*/
5986: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5987: {
5988:   PetscInt       dim     = mat->stencil.dim;
5989:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5990:   PetscInt       *dims   = mat->stencil.dims+1;
5991:   PetscInt       *starts = mat->stencil.starts;
5992:   PetscInt       *dxm    = (PetscInt*) rows;
5993:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


6001:   PetscMalloc1(numRows, &jdxm);
6002:   for (i = 0; i < numRows; ++i) {
6003:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6004:     for (j = 0; j < 3-sdim; ++j) dxm++;
6005:     /* Local index in X dir */
6006:     tmp = *dxm++ - starts[0];
6007:     /* Loop over remaining dimensions */
6008:     for (j = 0; j < dim-1; ++j) {
6009:       /* If nonlocal, set index to be negative */
6010:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6011:       /* Update local index */
6012:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
6013:     }
6014:     /* Skip component slot if necessary */
6015:     if (mat->stencil.noc) dxm++;
6016:     /* Local row number */
6017:     if (tmp >= 0) {
6018:       jdxm[numNewRows++] = tmp;
6019:     }
6020:   }
6021:   MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);
6022:   PetscFree(jdxm);
6023:   return(0);
6024: }

6026: /*@C
6027:    MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6028:    of a set of rows of a matrix; using local numbering of rows.

6030:    Collective on Mat

6032:    Input Parameters:
6033: +  mat - the matrix
6034: .  numRows - the number of rows to remove
6035: .  rows - the global row indices
6036: .  diag - value put in all diagonals of eliminated rows
6037: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6038: -  b - optional vector of right hand side, that will be adjusted by provided solution

6040:    Notes:
6041:    Before calling MatZeroRowsLocal(), the user must first set the
6042:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6044:    For the AIJ matrix formats this removes the old nonzero structure,
6045:    but does not release memory.  For the dense and block diagonal
6046:    formats this does not alter the nonzero structure.

6048:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6049:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6050:    merely zeroed.

6052:    The user can set a value in the diagonal entry (or for the AIJ and
6053:    row formats can optionally remove the main diagonal entry from the
6054:    nonzero structure as well, by passing 0.0 as the final argument).

6056:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6057:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6059:    Level: intermediate

6061:    Concepts: matrices^zeroing

6063: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRows(), MatSetOption(),
6064:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6065: @*/
6066: PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6067: {

6074:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6075:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6076:   MatCheckPreallocated(mat,1);

6078:   if (mat->ops->zerorowslocal) {
6079:     (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);
6080:   } else {
6081:     IS             is, newis;
6082:     const PetscInt *newRows;

6084:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6085:     ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6086:     ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);
6087:     ISGetIndices(newis,&newRows);
6088:     (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);
6089:     ISRestoreIndices(newis,&newRows);
6090:     ISDestroy(&newis);
6091:     ISDestroy(&is);
6092:   }
6093:   PetscObjectStateIncrease((PetscObject)mat);
6094: #if defined(PETSC_HAVE_CUSP)
6095:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
6096:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
6097:   }
6098: #elif defined(PETSC_HAVE_VIENNACL)
6099:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
6100:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
6101:   }
6102: #elif defined(PETSC_HAVE_VECCUDA)
6103:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
6104:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
6105:   }
6106: #endif
6107:   return(0);
6108: }

6110: /*@C
6111:    MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6112:    of a set of rows of a matrix; using local numbering of rows.

6114:    Collective on Mat

6116:    Input Parameters:
6117: +  mat - the matrix
6118: .  is - index set of rows to remove
6119: .  diag - value put in all diagonals of eliminated rows
6120: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6121: -  b - optional vector of right hand side, that will be adjusted by provided solution

6123:    Notes:
6124:    Before calling MatZeroRowsLocalIS(), the user must first set the
6125:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6127:    For the AIJ matrix formats this removes the old nonzero structure,
6128:    but does not release memory.  For the dense and block diagonal
6129:    formats this does not alter the nonzero structure.

6131:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6132:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6133:    merely zeroed.

6135:    The user can set a value in the diagonal entry (or for the AIJ and
6136:    row formats can optionally remove the main diagonal entry from the
6137:    nonzero structure as well, by passing 0.0 as the final argument).

6139:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6140:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6142:    Level: intermediate

6144:    Concepts: matrices^zeroing

6146: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6147:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6148: @*/
6149: PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6150: {
6152:   PetscInt       numRows;
6153:   const PetscInt *rows;

6159:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6160:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6161:   MatCheckPreallocated(mat,1);

6163:   ISGetLocalSize(is,&numRows);
6164:   ISGetIndices(is,&rows);
6165:   MatZeroRowsLocal(mat,numRows,rows,diag,x,b);
6166:   ISRestoreIndices(is,&rows);
6167:   return(0);
6168: }

6170: /*@C
6171:    MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6172:    of a set of rows and columns of a matrix; using local numbering of rows.

6174:    Collective on Mat

6176:    Input Parameters:
6177: +  mat - the matrix
6178: .  numRows - the number of rows to remove
6179: .  rows - the global row indices
6180: .  diag - value put in all diagonals of eliminated rows
6181: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6182: -  b - optional vector of right hand side, that will be adjusted by provided solution

6184:    Notes:
6185:    Before calling MatZeroRowsColumnsLocal(), the user must first set the
6186:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6188:    The user can set a value in the diagonal entry (or for the AIJ and
6189:    row formats can optionally remove the main diagonal entry from the
6190:    nonzero structure as well, by passing 0.0 as the final argument).

6192:    Level: intermediate

6194:    Concepts: matrices^zeroing

6196: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6197:           MatZeroRows(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6198: @*/
6199: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6200: {
6202:   IS             is, newis;
6203:   const PetscInt *newRows;

6209:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6210:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6211:   MatCheckPreallocated(mat,1);

6213:   if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6214:   ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6215:   ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);
6216:   ISGetIndices(newis,&newRows);
6217:   (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);
6218:   ISRestoreIndices(newis,&newRows);
6219:   ISDestroy(&newis);
6220:   ISDestroy(&is);
6221:   PetscObjectStateIncrease((PetscObject)mat);
6222: #if defined(PETSC_HAVE_CUSP)
6223:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
6224:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
6225:   }
6226: #elif defined(PETSC_HAVE_VIENNACL)
6227:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
6228:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
6229:   }
6230: #elif defined(PETSC_HAVE_VECCUDA)
6231:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
6232:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
6233:   }
6234: #endif
6235:   return(0);
6236: }

6238: /*@C
6239:    MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6240:    of a set of rows and columns of a matrix; using local numbering of rows.

6242:    Collective on Mat

6244:    Input Parameters:
6245: +  mat - the matrix
6246: .  is - index set of rows to remove
6247: .  diag - value put in all diagonals of eliminated rows
6248: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6249: -  b - optional vector of right hand side, that will be adjusted by provided solution

6251:    Notes:
6252:    Before calling MatZeroRowsColumnsLocalIS(), the user must first set the
6253:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6255:    The user can set a value in the diagonal entry (or for the AIJ and
6256:    row formats can optionally remove the main diagonal entry from the
6257:    nonzero structure as well, by passing 0.0 as the final argument).

6259:    Level: intermediate

6261:    Concepts: matrices^zeroing

6263: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6264:           MatZeroRowsColumnsLocal(), MatZeroRows(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6265: @*/
6266: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6267: {
6269:   PetscInt       numRows;
6270:   const PetscInt *rows;

6276:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6277:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6278:   MatCheckPreallocated(mat,1);

6280:   ISGetLocalSize(is,&numRows);
6281:   ISGetIndices(is,&rows);
6282:   MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);
6283:   ISRestoreIndices(is,&rows);
6284:   return(0);
6285: }

6287: /*@C
6288:    MatGetSize - Returns the numbers of rows and columns in a matrix.

6290:    Not Collective

6292:    Input Parameter:
6293: .  mat - the matrix

6295:    Output Parameters:
6296: +  m - the number of global rows
6297: -  n - the number of global columns

6299:    Note: both output parameters can be NULL on input.

6301:    Level: beginner

6303:    Concepts: matrices^size

6305: .seealso: MatGetLocalSize()
6306: @*/
6307: PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n)
6308: {
6311:   if (m) *m = mat->rmap->N;
6312:   if (n) *n = mat->cmap->N;
6313:   return(0);
6314: }

6316: /*@C
6317:    MatGetLocalSize - Returns the number of rows and columns in a matrix
6318:    stored locally.  This information may be implementation dependent, so
6319:    use with care.

6321:    Not Collective

6323:    Input Parameters:
6324: .  mat - the matrix

6326:    Output Parameters:
6327: +  m - the number of local rows
6328: -  n - the number of local columns

6330:    Note: both output parameters can be NULL on input.

6332:    Level: beginner

6334:    Concepts: matrices^local size

6336: .seealso: MatGetSize()
6337: @*/
6338: PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n)
6339: {
6344:   if (m) *m = mat->rmap->n;
6345:   if (n) *n = mat->cmap->n;
6346:   return(0);
6347: }

6349: /*@
6350:    MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6351:    this processor. (The columns of the "diagonal block")

6353:    Not Collective, unless matrix has not been allocated, then collective on Mat

6355:    Input Parameters:
6356: .  mat - the matrix

6358:    Output Parameters:
6359: +  m - the global index of the first local column
6360: -  n - one more than the global index of the last local column

6362:    Notes: both output parameters can be NULL on input.

6364:    Level: developer

6366:    Concepts: matrices^column ownership

6368: .seealso:  MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn()

6370: @*/
6371: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n)
6372: {
6378:   MatCheckPreallocated(mat,1);
6379:   if (m) *m = mat->cmap->rstart;
6380:   if (n) *n = mat->cmap->rend;
6381:   return(0);
6382: }

6384: /*@
6385:    MatGetOwnershipRange - Returns the range of matrix rows owned by
6386:    this processor, assuming that the matrix is laid out with the first
6387:    n1 rows on the first processor, the next n2 rows on the second, etc.
6388:    For certain parallel layouts this range may not be well defined.

6390:    Not Collective

6392:    Input Parameters:
6393: .  mat - the matrix

6395:    Output Parameters:
6396: +  m - the global index of the first local row
6397: -  n - one more than the global index of the last local row

6399:    Note: Both output parameters can be NULL on input.
6400: $  This function requires that the matrix be preallocated. If you have not preallocated, consider using
6401: $    PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N)
6402: $  and then MPI_Scan() to calculate prefix sums of the local sizes.

6404:    Level: beginner

6406:    Concepts: matrices^row ownership

6408: .seealso:   MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock()

6410: @*/
6411: PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n)
6412: {
6418:   MatCheckPreallocated(mat,1);
6419:   if (m) *m = mat->rmap->rstart;
6420:   if (n) *n = mat->rmap->rend;
6421:   return(0);
6422: }

6424: /*@C
6425:    MatGetOwnershipRanges - Returns the range of matrix rows owned by
6426:    each process

6428:    Not Collective, unless matrix has not been allocated, then collective on Mat

6430:    Input Parameters:
6431: .  mat - the matrix

6433:    Output Parameters:
6434: .  ranges - start of each processors portion plus one more than the total length at the end

6436:    Level: beginner

6438:    Concepts: matrices^row ownership

6440: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn()

6442: @*/
6443: PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges)
6444: {

6450:   MatCheckPreallocated(mat,1);
6451:   PetscLayoutGetRanges(mat->rmap,ranges);
6452:   return(0);
6453: }

6455: /*@C
6456:    MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6457:    this processor. (The columns of the "diagonal blocks" for each process)

6459:    Not Collective, unless matrix has not been allocated, then collective on Mat

6461:    Input Parameters:
6462: .  mat - the matrix

6464:    Output Parameters:
6465: .  ranges - start of each processors portion plus one more then the total length at the end

6467:    Level: beginner

6469:    Concepts: matrices^column ownership

6471: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges()

6473: @*/
6474: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges)
6475: {

6481:   MatCheckPreallocated(mat,1);
6482:   PetscLayoutGetRanges(mat->cmap,ranges);
6483:   return(0);
6484: }

6486: /*@C
6487:    MatGetOwnershipIS - Get row and column ownership as index sets

6489:    Not Collective

6491:    Input Arguments:
6492: .  A - matrix of type Elemental

6494:    Output Arguments:
6495: +  rows - rows in which this process owns elements
6496: .  cols - columns in which this process owns elements

6498:    Level: intermediate

6500: .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL, MatSetValues()
6501: @*/
6502: PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols)
6503: {
6504:   PetscErrorCode ierr,(*f)(Mat,IS*,IS*);

6507:   MatCheckPreallocated(A,1);
6508:   PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",&f);
6509:   if (f) {
6510:     (*f)(A,rows,cols);
6511:   } else {   /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6512:     if (rows) {ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);}
6513:     if (cols) {ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);}
6514:   }
6515:   return(0);
6516: }

6518: /*@C
6519:    MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix.
6520:    Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric()
6521:    to complete the factorization.

6523:    Collective on Mat

6525:    Input Parameters:
6526: +  mat - the matrix
6527: .  row - row permutation
6528: .  column - column permutation
6529: -  info - structure containing
6530: $      levels - number of levels of fill.
6531: $      expected fill - as ratio of original fill.
6532: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6533:                 missing diagonal entries)

6535:    Output Parameters:
6536: .  fact - new matrix that has been symbolically factored

6538:    Notes: See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

6540:    Most users should employ the simplified KSP interface for linear solvers
6541:    instead of working directly with matrix algebra routines such as this.
6542:    See, e.g., KSPCreate().

6544:    Level: developer

6546:   Concepts: matrices^symbolic LU factorization
6547:   Concepts: matrices^factorization
6548:   Concepts: LU^symbolic factorization

6550: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()
6551:           MatGetOrdering(), MatFactorInfo

6553:     Developer Note: fortran interface is not autogenerated as the f90
6554:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6556: @*/
6557: PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
6558: {

6568:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels);
6569:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6570:   if (!(fact)->ops->ilufactorsymbolic) {
6571:     const MatSolverPackage spackage;
6572:     MatFactorGetSolverPackage(fact,&spackage);
6573:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage);
6574:   }
6575:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6576:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6577:   MatCheckPreallocated(mat,2);

6579:   PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);
6580:   (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);
6581:   PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);
6582:   return(0);
6583: }

6585: /*@C
6586:    MatICCFactorSymbolic - Performs symbolic incomplete
6587:    Cholesky factorization for a symmetric matrix.  Use
6588:    MatCholeskyFactorNumeric() to complete the factorization.

6590:    Collective on Mat

6592:    Input Parameters:
6593: +  mat - the matrix
6594: .  perm - row and column permutation
6595: -  info - structure containing
6596: $      levels - number of levels of fill.
6597: $      expected fill - as ratio of original fill.

6599:    Output Parameter:
6600: .  fact - the factored matrix

6602:    Notes:
6603:    Most users should employ the KSP interface for linear solvers
6604:    instead of working directly with matrix algebra routines such as this.
6605:    See, e.g., KSPCreate().

6607:    Level: developer

6609:   Concepts: matrices^symbolic incomplete Cholesky factorization
6610:   Concepts: matrices^factorization
6611:   Concepts: Cholsky^symbolic factorization

6613: .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

6615:     Developer Note: fortran interface is not autogenerated as the f90
6616:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6618: @*/
6619: PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
6620: {

6629:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6630:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels);
6631:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6632:   if (!(fact)->ops->iccfactorsymbolic) {
6633:     const MatSolverPackage spackage;
6634:     MatFactorGetSolverPackage(fact,&spackage);
6635:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage);
6636:   }
6637:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6638:   MatCheckPreallocated(mat,2);

6640:   PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);
6641:   (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);
6642:   PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);
6643:   return(0);
6644: }

6646: /*@C
6647:    MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
6648:    points to an array of valid matrices, they may be reused to store the new
6649:    submatrices.

6651:    Collective on Mat

6653:    Input Parameters:
6654: +  mat - the matrix
6655: .  n   - the number of submatrixes to be extracted (on this processor, may be zero)
6656: .  irow, icol - index sets of rows and columns to extract
6657: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6659:    Output Parameter:
6660: .  submat - the array of submatrices

6662:    Notes:
6663:    MatCreateSubMatrices() can extract ONLY sequential submatrices
6664:    (from both sequential and parallel matrices). Use MatCreateSubMatrix()
6665:    to extract a parallel submatrix.

6667:    Some matrix types place restrictions on the row and column
6668:    indices, such as that they be sorted or that they be equal to each other.

6670:    The index sets may not have duplicate entries.

6672:    When extracting submatrices from a parallel matrix, each processor can
6673:    form a different submatrix by setting the rows and columns of its
6674:    individual index sets according to the local submatrix desired.

6676:    When finished using the submatrices, the user should destroy
6677:    them with MatDestroyMatrices().

6679:    MAT_REUSE_MATRIX can only be used when the nonzero structure of the
6680:    original matrix has not changed from that last call to MatCreateSubMatrices().

6682:    This routine creates the matrices in submat; you should NOT create them before
6683:    calling it. It also allocates the array of matrix pointers submat.

6685:    For BAIJ matrices the index sets must respect the block structure, that is if they
6686:    request one row/column in a block, they must request all rows/columns that are in
6687:    that block. For example, if the block size is 2 you cannot request just row 0 and
6688:    column 0.

6690:    Fortran Note:
6691:    The Fortran interface is slightly different from that given below; it
6692:    requires one to pass in  as submat a Mat (integer) array of size at least m.

6694:    Level: advanced

6696:    Concepts: matrices^accessing submatrices
6697:    Concepts: submatrices

6699: .seealso: MatDestroySubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6700: @*/
6701: PetscErrorCode MatCreateSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6702: {
6704:   PetscInt       i;
6705:   PetscBool      eq;

6710:   if (n) {
6715:   }
6717:   if (n && scall == MAT_REUSE_MATRIX) {
6720:   }
6721:   if (!mat->ops->createsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6722:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6723:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6724:   MatCheckPreallocated(mat,1);

6726:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6727:   (*mat->ops->createsubmatrices)(mat,n,irow,icol,scall,submat);
6728:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6729:   for (i=0; i<n; i++) {
6730:     (*submat)[i]->factortype = MAT_FACTOR_NONE;  /* in case in place factorization was previously done on submatrix */
6731:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6732:       ISEqual(irow[i],icol[i],&eq);
6733:       if (eq) {
6734:         if (mat->symmetric) {
6735:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6736:         } else if (mat->hermitian) {
6737:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6738:         } else if (mat->structurally_symmetric) {
6739:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6740:         }
6741:       }
6742:     }
6743:   }
6744:   return(0);
6745: }

6747: /*@C
6748:    MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of IS that may live on subcomms).

6750:    Collective on Mat

6752:    Input Parameters:
6753: +  mat - the matrix
6754: .  n   - the number of submatrixes to be extracted
6755: .  irow, icol - index sets of rows and columns to extract
6756: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6758:    Output Parameter:
6759: .  submat - the array of submatrices

6761:    Level: advanced

6763:    Concepts: matrices^accessing submatrices
6764:    Concepts: submatrices

6766: .seealso: MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6767: @*/
6768: PetscErrorCode MatCreateSubMatricesMPI(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6769: {
6771:   PetscInt       i;
6772:   PetscBool      eq;

6777:   if (n) {
6782:   }
6784:   if (n && scall == MAT_REUSE_MATRIX) {
6787:   }
6788:   if (!mat->ops->createsubmatricesmpi) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6789:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6790:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6791:   MatCheckPreallocated(mat,1);

6793:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6794:   (*mat->ops->createsubmatricesmpi)(mat,n,irow,icol,scall,submat);
6795:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6796:   for (i=0; i<n; i++) {
6797:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6798:       ISEqual(irow[i],icol[i],&eq);
6799:       if (eq) {
6800:         if (mat->symmetric) {
6801:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6802:         } else if (mat->hermitian) {
6803:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6804:         } else if (mat->structurally_symmetric) {
6805:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6806:         }
6807:       }
6808:     }
6809:   }
6810:   return(0);
6811: }

6813: /*@C
6814:    MatDestroyMatrices - Destroys an array of matrices.

6816:    Collective on Mat

6818:    Input Parameters:
6819: +  n - the number of local matrices
6820: -  mat - the matrices (note that this is a pointer to the array of matrices)

6822:    Level: advanced

6824:     Notes: Frees not only the matrices, but also the array that contains the matrices
6825:            In Fortran will not free the array.

6827: .seealso: MatCreateSubMatrices() MatDestroySubMatrices()
6828: @*/
6829: PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[])
6830: {
6832:   PetscInt       i;

6835:   if (!*mat) return(0);
6836:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6839:   for (i=0; i<n; i++) {
6840:     MatDestroy(&(*mat)[i]);
6841:   }

6843:   /* memory is allocated even if n = 0 */
6844:   PetscFree(*mat);
6845:   return(0);
6846: }

6848: /*@C
6849:    MatDestroySubMatrices - Destroys a set of matrices obtained with MatCreateSubMatrices().

6851:    Collective on Mat

6853:    Input Parameters:
6854: +  n - the number of local matrices
6855: -  mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling
6856:                        sequence of MatCreateSubMatrices())

6858:    Level: advanced

6860:     Notes: Frees not only the matrices, but also the array that contains the matrices
6861:            In Fortran will not free the array.

6863: .seealso: MatCreateSubMatrices()
6864: @*/
6865: PetscErrorCode MatDestroySubMatrices(PetscInt n,Mat *mat[])
6866: {
6868:   Mat            mat0;

6871:   if (!*mat) return(0);
6872:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
6873:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6876:   mat0 = (*mat)[0];
6877:   if (mat0 && mat0->ops->destroysubmatrices) {
6878:     (mat0->ops->destroysubmatrices)(n,mat);
6879:   } else {
6880:     MatDestroyMatrices(n,mat);
6881:   }
6882:   return(0);
6883: }

6885: /*@C
6886:    MatGetSeqNonzeroStructure - Extracts the sequential nonzero structure from a matrix.

6888:    Collective on Mat

6890:    Input Parameters:
6891: .  mat - the matrix

6893:    Output Parameter:
6894: .  matstruct - the sequential matrix with the nonzero structure of mat

6896:   Level: intermediate

6898: .seealso: MatDestroySeqNonzeroStructure(), MatCreateSubMatrices(), MatDestroyMatrices()
6899: @*/
6900: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat,Mat *matstruct)
6901: {


6909:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6910:   MatCheckPreallocated(mat,1);

6912:   if (!mat->ops->getseqnonzerostructure) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name);
6913:   PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6914:   (*mat->ops->getseqnonzerostructure)(mat,matstruct);
6915:   PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6916:   return(0);
6917: }

6919: /*@C
6920:    MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure().

6922:    Collective on Mat

6924:    Input Parameters:
6925: .  mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling
6926:                        sequence of MatGetSequentialNonzeroStructure())

6928:    Level: advanced

6930:     Notes: Frees not only the matrices, but also the array that contains the matrices

6932: .seealso: MatGetSeqNonzeroStructure()
6933: @*/
6934: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
6935: {

6940:   MatDestroy(mat);
6941:   return(0);
6942: }

6944: /*@
6945:    MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
6946:    replaces the index sets by larger ones that represent submatrices with
6947:    additional overlap.

6949:    Collective on Mat

6951:    Input Parameters:
6952: +  mat - the matrix
6953: .  n   - the number of index sets
6954: .  is  - the array of index sets (these index sets will changed during the call)
6955: -  ov  - the additional overlap requested

6957:    Options Database:
6958: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

6960:    Level: developer

6962:    Concepts: overlap
6963:    Concepts: ASM^computing overlap

6965: .seealso: MatCreateSubMatrices()
6966: @*/
6967: PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov)
6968: {

6974:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
6975:   if (n) {
6978:   }
6979:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6980:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6981:   MatCheckPreallocated(mat,1);

6983:   if (!ov) return(0);
6984:   if (!mat->ops->increaseoverlap) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6985:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
6986:   (*mat->ops->increaseoverlap)(mat,n,is,ov);
6987:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
6988:   return(0);
6989: }


6992: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat,IS*,PetscInt);

6994: /*@
6995:    MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
6996:    a sub communicator, replaces the index sets by larger ones that represent submatrices with
6997:    additional overlap.

6999:    Collective on Mat

7001:    Input Parameters:
7002: +  mat - the matrix
7003: .  n   - the number of index sets
7004: .  is  - the array of index sets (these index sets will changed during the call)
7005: -  ov  - the additional overlap requested

7007:    Options Database:
7008: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7010:    Level: developer

7012:    Concepts: overlap
7013:    Concepts: ASM^computing overlap

7015: .seealso: MatCreateSubMatrices()
7016: @*/
7017: PetscErrorCode MatIncreaseOverlapSplit(Mat mat,PetscInt n,IS is[],PetscInt ov)
7018: {
7019:   PetscInt       i;

7025:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
7026:   if (n) {
7029:   }
7030:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
7031:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7032:   MatCheckPreallocated(mat,1);
7033:   if (!ov) return(0);
7034:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
7035:   for(i=0; i<n; i++){
7036:          MatIncreaseOverlapSplit_Single(mat,&is[i],ov);
7037:   }
7038:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
7039:   return(0);
7040: }




7045: /*@
7046:    MatGetBlockSize - Returns the matrix block size.

7048:    Not Collective

7050:    Input Parameter:
7051: .  mat - the matrix

7053:    Output Parameter:
7054: .  bs - block size

7056:    Notes:
7057:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.

7059:    If the block size has not been set yet this routine returns 1.

7061:    Level: intermediate

7063:    Concepts: matrices^block size

7065: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes()
7066: @*/
7067: PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs)
7068: {
7072:   *bs = PetscAbs(mat->rmap->bs);
7073:   return(0);
7074: }

7076: /*@
7077:    MatGetBlockSizes - Returns the matrix block row and column sizes.

7079:    Not Collective

7081:    Input Parameter:
7082: .  mat - the matrix

7084:    Output Parameter:
7085: .  rbs - row block size
7086: .  cbs - column block size

7088:    Notes:
7089:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7090:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7092:    If a block size has not been set yet this routine returns 1.

7094:    Level: intermediate

7096:    Concepts: matrices^block size

7098: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatSetBlockSizes()
7099: @*/
7100: PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs)
7101: {
7106:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7107:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7108:   return(0);
7109: }

7111: /*@
7112:    MatSetBlockSize - Sets the matrix block size.

7114:    Logically Collective on Mat

7116:    Input Parameters:
7117: +  mat - the matrix
7118: -  bs - block size

7120:    Notes:
7121:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7122:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7124:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block size
7125:     is compatible with the matrix local sizes.

7127:    Level: intermediate

7129:    Concepts: matrices^block size

7131: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes()
7132: @*/
7133: PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs)
7134: {

7140:   MatSetBlockSizes(mat,bs,bs);
7141:   return(0);
7142: }

7144: /*@
7145:    MatSetBlockSizes - Sets the matrix block row and column sizes.

7147:    Logically Collective on Mat

7149:    Input Parameters:
7150: +  mat - the matrix
7151: -  rbs - row block size
7152: -  cbs - column block size

7154:    Notes:
7155:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7156:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7157:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later

7159:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block sizes
7160:     are compatible with the matrix local sizes.

7162:     The row and column block size determine the blocksize of the "row" and "column" vectors returned by MatCreateVecs().

7164:    Level: intermediate

7166:    Concepts: matrices^block size

7168: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatGetBlockSizes()
7169: @*/
7170: PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs)
7171: {

7178:   if (mat->ops->setblocksizes) {
7179:     (*mat->ops->setblocksizes)(mat,rbs,cbs);
7180:   }
7181:   if (mat->rmap->refcnt) {
7182:     ISLocalToGlobalMapping l2g = NULL;
7183:     PetscLayout            nmap = NULL;

7185:     PetscLayoutDuplicate(mat->rmap,&nmap);
7186:     if (mat->rmap->mapping) {
7187:       ISLocalToGlobalMappingDuplicate(mat->rmap->mapping,&l2g);
7188:     }
7189:     PetscLayoutDestroy(&mat->rmap);
7190:     mat->rmap = nmap;
7191:     mat->rmap->mapping = l2g;
7192:   }
7193:   if (mat->cmap->refcnt) {
7194:     ISLocalToGlobalMapping l2g = NULL;
7195:     PetscLayout            nmap = NULL;

7197:     PetscLayoutDuplicate(mat->cmap,&nmap);
7198:     if (mat->cmap->mapping) {
7199:       ISLocalToGlobalMappingDuplicate(mat->cmap->mapping,&l2g);
7200:     }
7201:     PetscLayoutDestroy(&mat->cmap);
7202:     mat->cmap = nmap;
7203:     mat->cmap->mapping = l2g;
7204:   }
7205:   PetscLayoutSetBlockSize(mat->rmap,rbs);
7206:   PetscLayoutSetBlockSize(mat->cmap,cbs);
7207:   return(0);
7208: }

7210: /*@
7211:    MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7213:    Logically Collective on Mat

7215:    Input Parameters:
7216: +  mat - the matrix
7217: .  fromRow - matrix from which to copy row block size
7218: -  fromCol - matrix from which to copy column block size (can be same as fromRow)

7220:    Level: developer

7222:    Concepts: matrices^block size

7224: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes()
7225: @*/
7226: PetscErrorCode MatSetBlockSizesFromMats(Mat mat,Mat fromRow,Mat fromCol)
7227: {

7234:   if (fromRow->rmap->bs > 0) {PetscLayoutSetBlockSize(mat->rmap,fromRow->rmap->bs);}
7235:   if (fromCol->cmap->bs > 0) {PetscLayoutSetBlockSize(mat->cmap,fromCol->cmap->bs);}
7236:   return(0);
7237: }

7239: /*@
7240:    MatResidual - Default routine to calculate the residual.

7242:    Collective on Mat and Vec

7244:    Input Parameters:
7245: +  mat - the matrix
7246: .  b   - the right-hand-side
7247: -  x   - the approximate solution

7249:    Output Parameter:
7250: .  r - location to store the residual

7252:    Level: developer

7254: .keywords: MG, default, multigrid, residual

7256: .seealso: PCMGSetResidual()
7257: @*/
7258: PetscErrorCode MatResidual(Mat mat,Vec b,Vec x,Vec r)
7259: {

7268:   MatCheckPreallocated(mat,1);
7269:   PetscLogEventBegin(MAT_Residual,mat,0,0,0);
7270:   if (!mat->ops->residual) {
7271:     MatMult(mat,x,r);
7272:     VecAYPX(r,-1.0,b);
7273:   } else {
7274:     (*mat->ops->residual)(mat,b,x,r);
7275:   }
7276:   PetscLogEventEnd(MAT_Residual,mat,0,0,0);
7277:   return(0);
7278: }

7280: /*@C
7281:     MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices.

7283:    Collective on Mat

7285:     Input Parameters:
7286: +   mat - the matrix
7287: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7288: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be   symmetrized
7289: -   inodecompressed - PETSC_TRUE or PETSC_FALSE  indicating if the nonzero structure of the
7290:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7291:                  always used.

7293:     Output Parameters:
7294: +   n - number of rows in the (possibly compressed) matrix
7295: .   ia - the row pointers [of length n+1]
7296: .   ja - the column indices
7297: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7298:            are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set

7300:     Level: developer

7302:     Notes: You CANNOT change any of the ia[] or ja[] values.

7304:            Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values

7306:     Fortran Node

7308:            In Fortran use
7309: $           PetscInt ia(1), ja(1)
7310: $           PetscOffset iia, jja
7311: $      call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr)
7312: $      Acess the ith and jth entries via ia(iia + i) and ja(jja + j)
7313: $
7314: $          or
7315: $
7316: $           PetscInt, pointer :: ia(:),ja(:)
7317: $    call  MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7318: $      Acess the ith and jth entries via ia(i) and ja(j)



7322: .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray()
7323: @*/
7324: PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7325: {

7335:   MatCheckPreallocated(mat,1);
7336:   if (!mat->ops->getrowij) *done = PETSC_FALSE;
7337:   else {
7338:     *done = PETSC_TRUE;
7339:     PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);
7340:     (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7341:     PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);
7342:   }
7343:   return(0);
7344: }

7346: /*@C
7347:     MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7349:     Collective on Mat

7351:     Input Parameters:
7352: +   mat - the matrix
7353: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7354: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7355:                 symmetrized
7356: .   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7357:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7358:                  always used.
7359: .   n - number of columns in the (possibly compressed) matrix
7360: .   ia - the column pointers
7361: -   ja - the row indices

7363:     Output Parameters:
7364: .   done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned

7366:     Note:
7367:     This routine zeros out n, ia, and ja. This is to prevent accidental
7368:     us of the array after it has been restored. If you pass NULL, it will
7369:     not zero the pointers.  Use of ia or ja after MatRestoreColumnIJ() is invalid.

7371:     Level: developer

7373: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7374: @*/
7375: PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7376: {

7386:   MatCheckPreallocated(mat,1);
7387:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
7388:   else {
7389:     *done = PETSC_TRUE;
7390:     (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7391:   }
7392:   return(0);
7393: }

7395: /*@C
7396:     MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with
7397:     MatGetRowIJ().

7399:     Collective on Mat

7401:     Input Parameters:
7402: +   mat - the matrix
7403: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7404: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7405:                 symmetrized
7406: .   inodecompressed -  PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7407:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7408:                  always used.
7409: .   n - size of (possibly compressed) matrix
7410: .   ia - the row pointers
7411: -   ja - the column indices

7413:     Output Parameters:
7414: .   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7416:     Note:
7417:     This routine zeros out n, ia, and ja. This is to prevent accidental
7418:     us of the array after it has been restored. If you pass NULL, it will
7419:     not zero the pointers.  Use of ia or ja after MatRestoreRowIJ() is invalid.

7421:     Level: developer

7423: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7424: @*/
7425: PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7426: {

7435:   MatCheckPreallocated(mat,1);

7437:   if (!mat->ops->restorerowij) *done = PETSC_FALSE;
7438:   else {
7439:     *done = PETSC_TRUE;
7440:     (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7441:     if (n)  *n = 0;
7442:     if (ia) *ia = NULL;
7443:     if (ja) *ja = NULL;
7444:   }
7445:   return(0);
7446: }

7448: /*@C
7449:     MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with
7450:     MatGetColumnIJ().

7452:     Collective on Mat

7454:     Input Parameters:
7455: +   mat - the matrix
7456: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7457: -   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7458:                 symmetrized
7459: -   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7460:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7461:                  always used.

7463:     Output Parameters:
7464: +   n - size of (possibly compressed) matrix
7465: .   ia - the column pointers
7466: .   ja - the row indices
7467: -   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7469:     Level: developer

7471: .seealso: MatGetColumnIJ(), MatRestoreRowIJ()
7472: @*/
7473: PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7474: {

7483:   MatCheckPreallocated(mat,1);

7485:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
7486:   else {
7487:     *done = PETSC_TRUE;
7488:     (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7489:     if (n)  *n = 0;
7490:     if (ia) *ia = NULL;
7491:     if (ja) *ja = NULL;
7492:   }
7493:   return(0);
7494: }

7496: /*@C
7497:     MatColoringPatch -Used inside matrix coloring routines that
7498:     use MatGetRowIJ() and/or MatGetColumnIJ().

7500:     Collective on Mat

7502:     Input Parameters:
7503: +   mat - the matrix
7504: .   ncolors - max color value
7505: .   n   - number of entries in colorarray
7506: -   colorarray - array indicating color for each column

7508:     Output Parameters:
7509: .   iscoloring - coloring generated using colorarray information

7511:     Level: developer

7513: .seealso: MatGetRowIJ(), MatGetColumnIJ()

7515: @*/
7516: PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring)
7517: {

7525:   MatCheckPreallocated(mat,1);

7527:   if (!mat->ops->coloringpatch) {
7528:     ISColoringCreate(PetscObjectComm((PetscObject)mat),ncolors,n,colorarray,PETSC_OWN_POINTER,iscoloring);
7529:   } else {
7530:     (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);
7531:   }
7532:   return(0);
7533: }


7536: /*@
7537:    MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

7539:    Logically Collective on Mat

7541:    Input Parameter:
7542: .  mat - the factored matrix to be reset

7544:    Notes:
7545:    This routine should be used only with factored matrices formed by in-place
7546:    factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE
7547:    format).  This option can save memory, for example, when solving nonlinear
7548:    systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
7549:    ILU(0) preconditioner.

7551:    Note that one can specify in-place ILU(0) factorization by calling
7552: .vb
7553:      PCType(pc,PCILU);
7554:      PCFactorSeUseInPlace(pc);
7555: .ve
7556:    or by using the options -pc_type ilu -pc_factor_in_place

7558:    In-place factorization ILU(0) can also be used as a local
7559:    solver for the blocks within the block Jacobi or additive Schwarz
7560:    methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
7561:    for details on setting local solver options.

7563:    Most users should employ the simplified KSP interface for linear solvers
7564:    instead of working directly with matrix algebra routines such as this.
7565:    See, e.g., KSPCreate().

7567:    Level: developer

7569: .seealso: PCFactorSetUseInPlace(), PCFactorGetUseInPlace()

7571:    Concepts: matrices^unfactored

7573: @*/
7574: PetscErrorCode MatSetUnfactored(Mat mat)
7575: {

7581:   MatCheckPreallocated(mat,1);
7582:   mat->factortype = MAT_FACTOR_NONE;
7583:   if (!mat->ops->setunfactored) return(0);
7584:   (*mat->ops->setunfactored)(mat);
7585:   return(0);
7586: }

7588: /*MC
7589:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran90.

7591:     Synopsis:
7592:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7594:     Not collective

7596:     Input Parameter:
7597: .   x - matrix

7599:     Output Parameters:
7600: +   xx_v - the Fortran90 pointer to the array
7601: -   ierr - error code

7603:     Example of Usage:
7604: .vb
7605:       PetscScalar, pointer xx_v(:,:)
7606:       ....
7607:       call MatDenseGetArrayF90(x,xx_v,ierr)
7608:       a = xx_v(3)
7609:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
7610: .ve

7612:     Level: advanced

7614: .seealso:  MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJGetArrayF90()

7616:     Concepts: matrices^accessing array

7618: M*/

7620: /*MC
7621:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
7622:     accessed with MatDenseGetArrayF90().

7624:     Synopsis:
7625:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7627:     Not collective

7629:     Input Parameters:
7630: +   x - matrix
7631: -   xx_v - the Fortran90 pointer to the array

7633:     Output Parameter:
7634: .   ierr - error code

7636:     Example of Usage:
7637: .vb
7638:        PetscScalar, pointer xx_v(:,:)
7639:        ....
7640:        call MatDenseGetArrayF90(x,xx_v,ierr)
7641:        a = xx_v(3)
7642:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
7643: .ve

7645:     Level: advanced

7647: .seealso:  MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJRestoreArrayF90()

7649: M*/


7652: /*MC
7653:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran90.

7655:     Synopsis:
7656:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7658:     Not collective

7660:     Input Parameter:
7661: .   x - matrix

7663:     Output Parameters:
7664: +   xx_v - the Fortran90 pointer to the array
7665: -   ierr - error code

7667:     Example of Usage:
7668: .vb
7669:       PetscScalar, pointer xx_v(:)
7670:       ....
7671:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7672:       a = xx_v(3)
7673:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7674: .ve

7676:     Level: advanced

7678: .seealso:  MatSeqAIJRestoreArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseGetArrayF90()

7680:     Concepts: matrices^accessing array

7682: M*/

7684: /*MC
7685:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
7686:     accessed with MatSeqAIJGetArrayF90().

7688:     Synopsis:
7689:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7691:     Not collective

7693:     Input Parameters:
7694: +   x - matrix
7695: -   xx_v - the Fortran90 pointer to the array

7697:     Output Parameter:
7698: .   ierr - error code

7700:     Example of Usage:
7701: .vb
7702:        PetscScalar, pointer xx_v(:)
7703:        ....
7704:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7705:        a = xx_v(3)
7706:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7707: .ve

7709:     Level: advanced

7711: .seealso:  MatSeqAIJGetArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseRestoreArrayF90()

7713: M*/


7716: /*@
7717:     MatCreateSubMatrix - Gets a single submatrix on the same number of processors
7718:                       as the original matrix.

7720:     Collective on Mat

7722:     Input Parameters:
7723: +   mat - the original matrix
7724: .   isrow - parallel IS containing the rows this processor should obtain
7725: .   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.
7726: -   cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

7728:     Output Parameter:
7729: .   newmat - the new submatrix, of the same type as the old

7731:     Level: advanced

7733:     Notes:
7734:     The submatrix will be able to be multiplied with vectors using the same layout as iscol.

7736:     Some matrix types place restrictions on the row and column indices, such
7737:     as that they be sorted or that they be equal to each other.

7739:     The index sets may not have duplicate entries.

7741:       The first time this is called you should use a cll of MAT_INITIAL_MATRIX,
7742:    the MatCreateSubMatrix() routine will create the newmat for you. Any additional calls
7743:    to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX
7744:    will reuse the matrix generated the first time.  You should call MatDestroy() on newmat when
7745:    you are finished using it.

7747:     The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
7748:     the input matrix.

7750:     If iscol is NULL then all columns are obtained (not supported in Fortran).

7752:    Example usage:
7753:    Consider the following 8x8 matrix with 34 non-zero values, that is
7754:    assembled across 3 processors. Let's assume that proc0 owns 3 rows,
7755:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
7756:    as follows:

7758: .vb
7759:             1  2  0  |  0  3  0  |  0  4
7760:     Proc0   0  5  6  |  7  0  0  |  8  0
7761:             9  0 10  | 11  0  0  | 12  0
7762:     -------------------------------------
7763:            13  0 14  | 15 16 17  |  0  0
7764:     Proc1   0 18  0  | 19 20 21  |  0  0
7765:             0  0  0  | 22 23  0  | 24  0
7766:     -------------------------------------
7767:     Proc2  25 26 27  |  0  0 28  | 29  0
7768:            30  0  0  | 31 32 33  |  0 34
7769: .ve

7771:     Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6].  The resulting submatrix is

7773: .vb
7774:             2  0  |  0  3  0  |  0
7775:     Proc0   5  6  |  7  0  0  |  8
7776:     -------------------------------
7777:     Proc1  18  0  | 19 20 21  |  0
7778:     -------------------------------
7779:     Proc2  26 27  |  0  0 28  | 29
7780:             0  0  | 31 32 33  |  0
7781: .ve


7784:     Concepts: matrices^submatrices

7786: .seealso: MatCreateSubMatrices()
7787: @*/
7788: PetscErrorCode MatCreateSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat)
7789: {
7791:   PetscMPIInt    size;
7792:   Mat            *local;
7793:   IS             iscoltmp;

7802:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7803:   if (cll == MAT_IGNORE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot use MAT_IGNORE_MATRIX");

7805:   MatCheckPreallocated(mat,1);
7806:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

7808:   if (!iscol || isrow == iscol) {
7809:     PetscBool   stride;
7810:     PetscMPIInt grabentirematrix = 0,grab;
7811:     PetscObjectTypeCompare((PetscObject)isrow,ISSTRIDE,&stride);
7812:     if (stride) {
7813:       PetscInt first,step,n,rstart,rend;
7814:       ISStrideGetInfo(isrow,&first,&step);
7815:       if (step == 1) {
7816:         MatGetOwnershipRange(mat,&rstart,&rend);
7817:         if (rstart == first) {
7818:           ISGetLocalSize(isrow,&n);
7819:           if (n == rend-rstart) {
7820:             grabentirematrix = 1;
7821:           }
7822:         }
7823:       }
7824:     }
7825:     MPIU_Allreduce(&grabentirematrix,&grab,1,MPI_INT,MPI_MIN,PetscObjectComm((PetscObject)mat));
7826:     if (grab) {
7827:       PetscInfo(mat,"Getting entire matrix as submatrix\n");
7828:       if (cll == MAT_INITIAL_MATRIX) {
7829:         *newmat = mat;
7830:         PetscObjectReference((PetscObject)mat);
7831:       }
7832:       return(0);
7833:     }
7834:   }

7836:   if (!iscol) {
7837:     ISCreateStride(PetscObjectComm((PetscObject)mat),mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);
7838:   } else {
7839:     iscoltmp = iscol;
7840:   }

7842:   /* if original matrix is on just one processor then use submatrix generated */
7843:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
7844:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);
7845:     if (!iscol) {ISDestroy(&iscoltmp);}
7846:     return(0);
7847:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
7848:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);
7849:     *newmat = *local;
7850:     PetscFree(local);
7851:     if (!iscol) {ISDestroy(&iscoltmp);}
7852:     return(0);
7853:   } else if (!mat->ops->createsubmatrix) {
7854:     /* Create a new matrix type that implements the operation using the full matrix */
7855:     PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7856:     switch (cll) {
7857:     case MAT_INITIAL_MATRIX:
7858:       MatCreateSubMatrixVirtual(mat,isrow,iscoltmp,newmat);
7859:       break;
7860:     case MAT_REUSE_MATRIX:
7861:       MatSubMatrixVirtualUpdate(*newmat,mat,isrow,iscoltmp);
7862:       break;
7863:     default: SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
7864:     }
7865:     PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
7866:     if (!iscol) {ISDestroy(&iscoltmp);}
7867:     return(0);
7868:   }

7870:   if (!mat->ops->createsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
7871:   PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7872:   (*mat->ops->createsubmatrix)(mat,isrow,iscoltmp,cll,newmat);
7873:   PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
7874:   if (!iscol) {ISDestroy(&iscoltmp);}
7875:   if (*newmat && cll == MAT_INITIAL_MATRIX) {PetscObjectStateIncrease((PetscObject)*newmat);}
7876:   return(0);
7877: }

7879: /*@
7880:    MatStashSetInitialSize - sets the sizes of the matrix stash, that is
7881:    used during the assembly process to store values that belong to
7882:    other processors.

7884:    Not Collective

7886:    Input Parameters:
7887: +  mat   - the matrix
7888: .  size  - the initial size of the stash.
7889: -  bsize - the initial size of the block-stash(if used).

7891:    Options Database Keys:
7892: +   -matstash_initial_size <size> or <size0,size1,...sizep-1>
7893: -   -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1>

7895:    Level: intermediate

7897:    Notes:
7898:      The block-stash is used for values set with MatSetValuesBlocked() while
7899:      the stash is used for values set with MatSetValues()

7901:      Run with the option -info and look for output of the form
7902:      MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
7903:      to determine the appropriate value, MM, to use for size and
7904:      MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
7905:      to determine the value, BMM to use for bsize

7907:    Concepts: stash^setting matrix size
7908:    Concepts: matrices^stash

7910: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo()

7912: @*/
7913: PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize)
7914: {

7920:   MatStashSetInitialSize_Private(&mat->stash,size);
7921:   MatStashSetInitialSize_Private(&mat->bstash,bsize);
7922:   return(0);
7923: }

7925: /*@
7926:    MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
7927:      the matrix

7929:    Neighbor-wise Collective on Mat

7931:    Input Parameters:
7932: +  mat   - the matrix
7933: .  x,y - the vectors
7934: -  w - where the result is stored

7936:    Level: intermediate

7938:    Notes:
7939:     w may be the same vector as y.

7941:     This allows one to use either the restriction or interpolation (its transpose)
7942:     matrix to do the interpolation

7944:     Concepts: interpolation

7946: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

7948: @*/
7949: PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w)
7950: {
7952:   PetscInt       M,N,Ny;

7960:   MatCheckPreallocated(A,1);
7961:   MatGetSize(A,&M,&N);
7962:   VecGetSize(y,&Ny);
7963:   if (M == Ny) {
7964:     MatMultAdd(A,x,y,w);
7965:   } else {
7966:     MatMultTransposeAdd(A,x,y,w);
7967:   }
7968:   return(0);
7969: }

7971: /*@
7972:    MatInterpolate - y = A*x or A'*x depending on the shape of
7973:      the matrix

7975:    Neighbor-wise Collective on Mat

7977:    Input Parameters:
7978: +  mat   - the matrix
7979: -  x,y - the vectors

7981:    Level: intermediate

7983:    Notes:
7984:     This allows one to use either the restriction or interpolation (its transpose)
7985:     matrix to do the interpolation

7987:    Concepts: matrices^interpolation

7989: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

7991: @*/
7992: PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y)
7993: {
7995:   PetscInt       M,N,Ny;

8002:   MatCheckPreallocated(A,1);
8003:   MatGetSize(A,&M,&N);
8004:   VecGetSize(y,&Ny);
8005:   if (M == Ny) {
8006:     MatMult(A,x,y);
8007:   } else {
8008:     MatMultTranspose(A,x,y);
8009:   }
8010:   return(0);
8011: }

8013: /*@
8014:    MatRestrict - y = A*x or A'*x

8016:    Neighbor-wise Collective on Mat

8018:    Input Parameters:
8019: +  mat   - the matrix
8020: -  x,y - the vectors

8022:    Level: intermediate

8024:    Notes:
8025:     This allows one to use either the restriction or interpolation (its transpose)
8026:     matrix to do the restriction

8028:    Concepts: matrices^restriction

8030: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate()

8032: @*/
8033: PetscErrorCode MatRestrict(Mat A,Vec x,Vec y)
8034: {
8036:   PetscInt       M,N,Ny;

8043:   MatCheckPreallocated(A,1);

8045:   MatGetSize(A,&M,&N);
8046:   VecGetSize(y,&Ny);
8047:   if (M == Ny) {
8048:     MatMult(A,x,y);
8049:   } else {
8050:     MatMultTranspose(A,x,y);
8051:   }
8052:   return(0);
8053: }

8055: /*@
8056:    MatGetNullSpace - retrieves the null space to a matrix.

8058:    Logically Collective on Mat and MatNullSpace

8060:    Input Parameters:
8061: +  mat - the matrix
8062: -  nullsp - the null space object

8064:    Level: developer

8066:    Concepts: null space^attaching to matrix

8068: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetNullSpace()
8069: @*/
8070: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8071: {
8076:   *nullsp = mat->nullsp;
8077:   return(0);
8078: }

8080: /*@
8081:    MatSetNullSpace - attaches a null space to a matrix.

8083:    Logically Collective on Mat and MatNullSpace

8085:    Input Parameters:
8086: +  mat - the matrix
8087: -  nullsp - the null space object

8089:    Level: advanced

8091:    Notes:
8092:       This null space is used by the linear solvers. Overwrites any previous null space that may have been attached

8094:       For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) you also likely should
8095:       call MatSetTransposeNullSpace(). This allows the linear system to be solved in a least squares sense.

8097:       You can remove the null space by calling this routine with an nullsp of NULL


8100:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8101:    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).
8102:    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
8103:    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
8104:    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).

8106:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8108:     If the matrix is known to be symmetric because it is an SBAIJ matrix or one as called MatSetOption(mat,MAT_SYMMETRIC or MAT_SYMMETRIC_ETERNAL,PETSC_TRUE); this
8109:     routine also automatically calls MatSetTransposeNullSpace().

8111:    Concepts: null space^attaching to matrix

8113: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetTransposeNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8114: @*/
8115: PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp)
8116: {

8123:   MatCheckPreallocated(mat,1);
8124:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8125:   MatNullSpaceDestroy(&mat->nullsp);
8126:   mat->nullsp = nullsp;
8127:   if (mat->symmetric_set && mat->symmetric) {
8128:     MatSetTransposeNullSpace(mat,nullsp);
8129:   }
8130:   return(0);
8131: }

8133: /*@
8134:    MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8136:    Logically Collective on Mat and MatNullSpace

8138:    Input Parameters:
8139: +  mat - the matrix
8140: -  nullsp - the null space object

8142:    Level: developer

8144:    Concepts: null space^attaching to matrix

8146: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetTransposeNullSpace(), MatSetNullSpace(), MatGetNullSpace()
8147: @*/
8148: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8149: {
8154:   *nullsp = mat->transnullsp;
8155:   return(0);
8156: }

8158: /*@
8159:    MatSetTransposeNullSpace - attaches a null space to a matrix.

8161:    Logically Collective on Mat and MatNullSpace

8163:    Input Parameters:
8164: +  mat - the matrix
8165: -  nullsp - the null space object

8167:    Level: advanced

8169:    Notes:
8170:       For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) this allows the linear system to be solved in a least squares sense.
8171:       You must also call MatSetNullSpace()


8174:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8175:    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).
8176:    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
8177:    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
8178:    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).

8180:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8182:    Concepts: null space^attaching to matrix

8184: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8185: @*/
8186: PetscErrorCode MatSetTransposeNullSpace(Mat mat,MatNullSpace nullsp)
8187: {

8194:   MatCheckPreallocated(mat,1);
8195:   PetscObjectReference((PetscObject)nullsp);
8196:   MatNullSpaceDestroy(&mat->transnullsp);
8197:   mat->transnullsp = nullsp;
8198:   return(0);
8199: }

8201: /*@
8202:    MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8203:         This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8205:    Logically Collective on Mat and MatNullSpace

8207:    Input Parameters:
8208: +  mat - the matrix
8209: -  nullsp - the null space object

8211:    Level: advanced

8213:    Notes:
8214:       Overwrites any previous near null space that may have been attached

8216:       You can remove the null space by calling this routine with an nullsp of NULL

8218:    Concepts: null space^attaching to matrix

8220: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace(), MatNullSpaceCreateRigidBody(), MatGetNearNullSpace()
8221: @*/
8222: PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp)
8223: {

8230:   MatCheckPreallocated(mat,1);
8231:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8232:   MatNullSpaceDestroy(&mat->nearnullsp);
8233:   mat->nearnullsp = nullsp;
8234:   return(0);
8235: }

8237: /*@
8238:    MatGetNearNullSpace -Get null space attached with MatSetNearNullSpace()

8240:    Not Collective

8242:    Input Parameters:
8243: .  mat - the matrix

8245:    Output Parameters:
8246: .  nullsp - the null space object, NULL if not set

8248:    Level: developer

8250:    Concepts: null space^attaching to matrix

8252: .seealso: MatSetNearNullSpace(), MatGetNullSpace(), MatNullSpaceCreate()
8253: @*/
8254: PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp)
8255: {
8260:   MatCheckPreallocated(mat,1);
8261:   *nullsp = mat->nearnullsp;
8262:   return(0);
8263: }

8265: /*@C
8266:    MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8268:    Collective on Mat

8270:    Input Parameters:
8271: +  mat - the matrix
8272: .  row - row/column permutation
8273: .  fill - expected fill factor >= 1.0
8274: -  level - level of fill, for ICC(k)

8276:    Notes:
8277:    Probably really in-place only when level of fill is zero, otherwise allocates
8278:    new space to store factored matrix and deletes previous memory.

8280:    Most users should employ the simplified KSP interface for linear solvers
8281:    instead of working directly with matrix algebra routines such as this.
8282:    See, e.g., KSPCreate().

8284:    Level: developer

8286:    Concepts: matrices^incomplete Cholesky factorization
8287:    Concepts: Cholesky factorization

8289: .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()

8291:     Developer Note: fortran interface is not autogenerated as the f90
8292:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8294: @*/
8295: PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info)
8296: {

8304:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
8305:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
8306:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
8307:   if (!mat->ops->iccfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8308:   MatCheckPreallocated(mat,1);
8309:   (*mat->ops->iccfactor)(mat,row,info);
8310:   PetscObjectStateIncrease((PetscObject)mat);
8311:   return(0);
8312: }

8314: /*@
8315:    MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
8316:          ghosted ones.

8318:    Not Collective

8320:    Input Parameters:
8321: +  mat - the matrix
8322: -  diag = the diagonal values, including ghost ones

8324:    Level: developer

8326:    Notes: Works only for MPIAIJ and MPIBAIJ matrices

8328: .seealso: MatDiagonalScale()
8329: @*/
8330: PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag)
8331: {
8333:   PetscMPIInt    size;


8340:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled");
8341:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
8342:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
8343:   if (size == 1) {
8344:     PetscInt n,m;
8345:     VecGetSize(diag,&n);
8346:     MatGetSize(mat,0,&m);
8347:     if (m == n) {
8348:       MatDiagonalScale(mat,0,diag);
8349:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions");
8350:   } else {
8351:     PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));
8352:   }
8353:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
8354:   PetscObjectStateIncrease((PetscObject)mat);
8355:   return(0);
8356: }

8358: /*@
8359:    MatGetInertia - Gets the inertia from a factored matrix

8361:    Collective on Mat

8363:    Input Parameter:
8364: .  mat - the matrix

8366:    Output Parameters:
8367: +   nneg - number of negative eigenvalues
8368: .   nzero - number of zero eigenvalues
8369: -   npos - number of positive eigenvalues

8371:    Level: advanced

8373:    Notes: Matrix must have been factored by MatCholeskyFactor()


8376: @*/
8377: PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
8378: {

8384:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8385:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled");
8386:   if (!mat->ops->getinertia) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8387:   (*mat->ops->getinertia)(mat,nneg,nzero,npos);
8388:   return(0);
8389: }

8391: /* ----------------------------------------------------------------*/
8392: /*@C
8393:    MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

8395:    Neighbor-wise Collective on Mat and Vecs

8397:    Input Parameters:
8398: +  mat - the factored matrix
8399: -  b - the right-hand-side vectors

8401:    Output Parameter:
8402: .  x - the result vectors

8404:    Notes:
8405:    The vectors b and x cannot be the same.  I.e., one cannot
8406:    call MatSolves(A,x,x).

8408:    Notes:
8409:    Most users should employ the simplified KSP interface for linear solvers
8410:    instead of working directly with matrix algebra routines such as this.
8411:    See, e.g., KSPCreate().

8413:    Level: developer

8415:    Concepts: matrices^triangular solves

8417: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve()
8418: @*/
8419: PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x)
8420: {

8426:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
8427:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8428:   if (!mat->rmap->N && !mat->cmap->N) return(0);

8430:   if (!mat->ops->solves) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8431:   MatCheckPreallocated(mat,1);
8432:   PetscLogEventBegin(MAT_Solves,mat,0,0,0);
8433:   (*mat->ops->solves)(mat,b,x);
8434:   PetscLogEventEnd(MAT_Solves,mat,0,0,0);
8435:   return(0);
8436: }

8438: /*@
8439:    MatIsSymmetric - Test whether a matrix is symmetric

8441:    Collective on Mat

8443:    Input Parameter:
8444: +  A - the matrix to test
8445: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

8447:    Output Parameters:
8448: .  flg - the result

8450:    Notes: For real numbers MatIsSymmetric() and MatIsHermitian() return identical results

8452:    Level: intermediate

8454:    Concepts: matrix^symmetry

8456: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown()
8457: @*/
8458: PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool  *flg)
8459: {


8466:   if (!A->symmetric_set) {
8467:     if (!A->ops->issymmetric) {
8468:       MatType mattype;
8469:       MatGetType(A,&mattype);
8470:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8471:     }
8472:     (*A->ops->issymmetric)(A,tol,flg);
8473:     if (!tol) {
8474:       A->symmetric_set = PETSC_TRUE;
8475:       A->symmetric     = *flg;
8476:       if (A->symmetric) {
8477:         A->structurally_symmetric_set = PETSC_TRUE;
8478:         A->structurally_symmetric     = PETSC_TRUE;
8479:       }
8480:     }
8481:   } else if (A->symmetric) {
8482:     *flg = PETSC_TRUE;
8483:   } else if (!tol) {
8484:     *flg = PETSC_FALSE;
8485:   } else {
8486:     if (!A->ops->issymmetric) {
8487:       MatType mattype;
8488:       MatGetType(A,&mattype);
8489:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8490:     }
8491:     (*A->ops->issymmetric)(A,tol,flg);
8492:   }
8493:   return(0);
8494: }

8496: /*@
8497:    MatIsHermitian - Test whether a matrix is Hermitian

8499:    Collective on Mat

8501:    Input Parameter:
8502: +  A - the matrix to test
8503: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

8505:    Output Parameters:
8506: .  flg - the result

8508:    Level: intermediate

8510:    Concepts: matrix^symmetry

8512: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(),
8513:           MatIsSymmetricKnown(), MatIsSymmetric()
8514: @*/
8515: PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool  *flg)
8516: {


8523:   if (!A->hermitian_set) {
8524:     if (!A->ops->ishermitian) {
8525:       MatType mattype;
8526:       MatGetType(A,&mattype);
8527:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8528:     }
8529:     (*A->ops->ishermitian)(A,tol,flg);
8530:     if (!tol) {
8531:       A->hermitian_set = PETSC_TRUE;
8532:       A->hermitian     = *flg;
8533:       if (A->hermitian) {
8534:         A->structurally_symmetric_set = PETSC_TRUE;
8535:         A->structurally_symmetric     = PETSC_TRUE;
8536:       }
8537:     }
8538:   } else if (A->hermitian) {
8539:     *flg = PETSC_TRUE;
8540:   } else if (!tol) {
8541:     *flg = PETSC_FALSE;
8542:   } else {
8543:     if (!A->ops->ishermitian) {
8544:       MatType mattype;
8545:       MatGetType(A,&mattype);
8546:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8547:     }
8548:     (*A->ops->ishermitian)(A,tol,flg);
8549:   }
8550:   return(0);
8551: }

8553: /*@
8554:    MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric.

8556:    Not Collective

8558:    Input Parameter:
8559: .  A - the matrix to check

8561:    Output Parameters:
8562: +  set - if the symmetric flag is set (this tells you if the next flag is valid)
8563: -  flg - the result

8565:    Level: advanced

8567:    Concepts: matrix^symmetry

8569:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric()
8570:          if you want it explicitly checked

8572: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8573: @*/
8574: PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8575: {
8580:   if (A->symmetric_set) {
8581:     *set = PETSC_TRUE;
8582:     *flg = A->symmetric;
8583:   } else {
8584:     *set = PETSC_FALSE;
8585:   }
8586:   return(0);
8587: }

8589: /*@
8590:    MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian.

8592:    Not Collective

8594:    Input Parameter:
8595: .  A - the matrix to check

8597:    Output Parameters:
8598: +  set - if the hermitian flag is set (this tells you if the next flag is valid)
8599: -  flg - the result

8601:    Level: advanced

8603:    Concepts: matrix^symmetry

8605:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian()
8606:          if you want it explicitly checked

8608: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8609: @*/
8610: PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8611: {
8616:   if (A->hermitian_set) {
8617:     *set = PETSC_TRUE;
8618:     *flg = A->hermitian;
8619:   } else {
8620:     *set = PETSC_FALSE;
8621:   }
8622:   return(0);
8623: }

8625: /*@
8626:    MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

8628:    Collective on Mat

8630:    Input Parameter:
8631: .  A - the matrix to test

8633:    Output Parameters:
8634: .  flg - the result

8636:    Level: intermediate

8638:    Concepts: matrix^symmetry

8640: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption()
8641: @*/
8642: PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool  *flg)
8643: {

8649:   if (!A->structurally_symmetric_set) {
8650:     if (!A->ops->isstructurallysymmetric) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric");
8651:     (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);

8653:     A->structurally_symmetric_set = PETSC_TRUE;
8654:   }
8655:   *flg = A->structurally_symmetric;
8656:   return(0);
8657: }

8659: /*@
8660:    MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
8661:        to be communicated to other processors during the MatAssemblyBegin/End() process

8663:     Not collective

8665:    Input Parameter:
8666: .   vec - the vector

8668:    Output Parameters:
8669: +   nstash   - the size of the stash
8670: .   reallocs - the number of additional mallocs incurred.
8671: .   bnstash   - the size of the block stash
8672: -   breallocs - the number of additional mallocs incurred.in the block stash

8674:    Level: advanced

8676: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize()

8678: @*/
8679: PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs)
8680: {

8684:   MatStashGetInfo_Private(&mat->stash,nstash,reallocs);
8685:   MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);
8686:   return(0);
8687: }

8689: /*@C
8690:    MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
8691:      parallel layout

8693:    Collective on Mat

8695:    Input Parameter:
8696: .  mat - the matrix

8698:    Output Parameter:
8699: +   right - (optional) vector that the matrix can be multiplied against
8700: -   left - (optional) vector that the matrix vector product can be stored in

8702:    Notes:
8703:     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().

8705:   Notes: These are new vectors which are not owned by the Mat, they should be destroyed in VecDestroy() when no longer needed

8707:   Level: advanced

8709: .seealso: MatCreate(), VecDestroy()
8710: @*/
8711: PetscErrorCode MatCreateVecs(Mat mat,Vec *right,Vec *left)
8712: {

8718:   if (mat->ops->getvecs) {
8719:     (*mat->ops->getvecs)(mat,right,left);
8720:   } else {
8721:     PetscInt rbs,cbs;
8722:     MatGetBlockSizes(mat,&rbs,&cbs);
8723:     if (right) {
8724:       if (mat->cmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for columns not yet setup");
8725:       VecCreate(PetscObjectComm((PetscObject)mat),right);
8726:       VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
8727:       VecSetBlockSize(*right,cbs);
8728:       VecSetType(*right,VECSTANDARD);
8729:       PetscLayoutReference(mat->cmap,&(*right)->map);
8730:     }
8731:     if (left) {
8732:       if (mat->rmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for rows not yet setup");
8733:       VecCreate(PetscObjectComm((PetscObject)mat),left);
8734:       VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
8735:       VecSetBlockSize(*left,rbs);
8736:       VecSetType(*left,VECSTANDARD);
8737:       PetscLayoutReference(mat->rmap,&(*left)->map);
8738:     }
8739:   }
8740:   return(0);
8741: }

8743: /*@C
8744:    MatFactorInfoInitialize - Initializes a MatFactorInfo data structure
8745:      with default values.

8747:    Not Collective

8749:    Input Parameters:
8750: .    info - the MatFactorInfo data structure


8753:    Notes: The solvers are generally used through the KSP and PC objects, for example
8754:           PCLU, PCILU, PCCHOLESKY, PCICC

8756:    Level: developer

8758: .seealso: MatFactorInfo

8760:     Developer Note: fortran interface is not autogenerated as the f90
8761:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8763: @*/

8765: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
8766: {

8770:   PetscMemzero(info,sizeof(MatFactorInfo));
8771:   return(0);
8772: }

8774: /*@
8775:    MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

8777:    Collective on Mat

8779:    Input Parameters:
8780: +  mat - the factored matrix
8781: -  is - the index set defining the Schur indices (0-based)

8783:    Notes:  Call MatFactorSolveSchurComplement() or MatFactorSolveSchurComplementTranspose() after this call to solve a Schur complement system.

8785:    You can call MatFactorGetSchurComplement() or MatFactorCreateSchurComplement() after this call.

8787:    Level: developer

8789:    Concepts:

8791: .seealso: MatGetFactor(), MatFactorGetSchurComplement(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSolveSchurComplement(),
8792:           MatFactorSolveSchurComplementTranspose(), MatFactorSolveSchurComplement()

8794: @*/
8795: PetscErrorCode MatFactorSetSchurIS(Mat mat,IS is)
8796: {
8797:   PetscErrorCode ierr,(*f)(Mat,IS);

8805:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
8806:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorSetSchurIS_C",&f);
8807:   if (!f) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"The selected MatSolverPackage does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
8808:   if (mat->schur) {
8809:     MatDestroy(&mat->schur);
8810:   }
8811:   (*f)(mat,is);
8812:   if (!mat->schur) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_PLIB,"Schur complement has not been created");
8813:   MatFactorSetUpInPlaceSchur_Private(mat);
8814:   return(0);
8815: }

8817: /*@
8818:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

8820:    Logically Collective on Mat

8822:    Input Parameters:
8823: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
8824: .  S - location where to return the Schur complement, can be NULL
8825: -  status - the status of the Schur complement matrix, can be NULL

8827:    Notes:
8828:    You must call MatFactorSetSchurIS() before calling this routine.

8830:    The routine provides a copy of the Schur matrix stored within the solver data structures.
8831:    The caller must destroy the object when it is no longer needed.
8832:    If MatFactorInvertSchurComplement() has been called, the routine gets back the inverse.

8834:    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)

8836:    Developer Notes: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
8837:    matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix. 

8839:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

8841:    Level: advanced

8843:    References:

8845: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorSchurStatus
8846: @*/
8847: PetscErrorCode MatFactorCreateSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
8848: {

8855:   if (S) {
8856:     PetscErrorCode (*f)(Mat,Mat*);

8858:     PetscObjectQueryFunction((PetscObject)F,"MatFactorCreateSchurComplement_C",&f);
8859:     if (f) {
8860:       (*f)(F,S);
8861:     } else {
8862:       MatDuplicate(F->schur,MAT_COPY_VALUES,S);
8863:     }
8864:   }
8865:   if (status) *status = F->schur_status;
8866:   return(0);
8867: }

8869: /*@
8870:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

8872:    Logically Collective on Mat

8874:    Input Parameters:
8875: +  F - the factored matrix obtained by calling MatGetFactor()
8876: .  *S - location where to return the Schur complement, can be NULL
8877: -  status - the status of the Schur complement matrix, can be NULL

8879:    Notes:
8880:    You must call MatFactorSetSchurIS() before calling this routine.

8882:    Schur complement mode is currently implemented for sequential matrices.
8883:    The routine returns a the Schur Complement stored within the data strutures of the solver.
8884:    If MatFactorInvertSchurComplement() has previously been called, the returned matrix is actually the inverse of the Schur complement.
8885:    The returned matrix should not be destroyed; the caller should call MatFactorRestoreSchurComplement() when the object is no longer needed.

8887:    Use MatFactorCreateSchurComplement() to create a copy of the Schur complement matrix that is within a factored matrix

8889:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

8891:    Level: advanced

8893:    References:

8895: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
8896: @*/
8897: PetscErrorCode MatFactorGetSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
8898: {
8903:   if (S) *S = F->schur;
8904:   if (status) *status = F->schur_status;
8905:   return(0);
8906: }

8908: /*@
8909:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to MatFactorGetSchurComplement

8911:    Logically Collective on Mat

8913:    Input Parameters:
8914: +  F - the factored matrix obtained by calling MatGetFactor()
8915: .  *S - location where the Schur complement is stored
8916: -  status - the status of the Schur complement matrix (see MatFactorSchurStatus)

8918:    Notes:

8920:    Level: advanced

8922:    References:

8924: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
8925: @*/
8926: PetscErrorCode MatFactorRestoreSchurComplement(Mat F,Mat* S,MatFactorSchurStatus status)
8927: {

8932:   if (S) {
8934:     *S = NULL;
8935:   }
8936:   F->schur_status = status;
8937:   MatFactorUpdateSchurStatus_Private(F);
8938:   return(0);
8939: }

8941: /*@
8942:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

8944:    Logically Collective on Mat

8946:    Input Parameters:
8947: +  F - the factored matrix obtained by calling MatGetFactor()
8948: .  rhs - location where the right hand side of the Schur complement system is stored
8949: -  sol - location where the solution of the Schur complement system has to be returned

8951:    Notes:
8952:    The sizes of the vectors should match the size of the Schur complement

8954:    Must be called after MatFactorSetSchurIS()

8956:    Level: advanced

8958:    References:

8960: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplement()
8961: @*/
8962: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
8963: {

8975:   MatFactorFactorizeSchurComplement(F);
8976:   switch (F->schur_status) {
8977:   case MAT_FACTOR_SCHUR_FACTORED:
8978:     MatSolveTranspose(F->schur,rhs,sol);
8979:     break;
8980:   case MAT_FACTOR_SCHUR_INVERTED:
8981:     MatMultTranspose(F->schur,rhs,sol);
8982:     break;
8983:   default:
8984:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
8985:     break;
8986:   }
8987:   return(0);
8988: }

8990: /*@
8991:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

8993:    Logically Collective on Mat

8995:    Input Parameters:
8996: +  F - the factored matrix obtained by calling MatGetFactor()
8997: .  rhs - location where the right hand side of the Schur complement system is stored
8998: -  sol - location where the solution of the Schur complement system has to be returned

9000:    Notes:
9001:    The sizes of the vectors should match the size of the Schur complement

9003:    Must be called after MatFactorSetSchurIS()

9005:    Level: advanced

9007:    References:

9009: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplementTranspose()
9010: @*/
9011: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9012: {

9024:   MatFactorFactorizeSchurComplement(F);
9025:   switch (F->schur_status) {
9026:   case MAT_FACTOR_SCHUR_FACTORED:
9027:     MatSolve(F->schur,rhs,sol);
9028:     break;
9029:   case MAT_FACTOR_SCHUR_INVERTED:
9030:     MatMult(F->schur,rhs,sol);
9031:     break;
9032:   default:
9033:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
9034:     break;
9035:   }
9036:   return(0);
9037: }

9039: /*@
9040:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9042:    Logically Collective on Mat

9044:    Input Parameters:
9045: +  F - the factored matrix obtained by calling MatGetFactor()

9047:    Notes: Must be called after MatFactorSetSchurIS().

9049:    Call MatFactorGetSchurComplement() or  MatFactorCreateSchurComplement() AFTER this call to actually compute the inverse and get access to it.

9051:    Level: advanced

9053:    References:

9055: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorCreateSchurComplement()
9056: @*/
9057: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9058: {

9064:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) return(0);
9065:   MatFactorFactorizeSchurComplement(F);
9066:   MatFactorInvertSchurComplement_Private(F);
9067:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9068:   return(0);
9069: }

9071: /*@
9072:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9074:    Logically Collective on Mat

9076:    Input Parameters:
9077: +  F - the factored matrix obtained by calling MatGetFactor()

9079:    Notes: Must be called after MatFactorSetSchurIS().

9081:    Level: advanced

9083:    References:

9085: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorInvertSchurComplement()
9086: @*/
9087: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9088: {

9094:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) return(0);
9095:   MatFactorFactorizeSchurComplement_Private(F);
9096:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9097:   return(0);
9098: }

9100: /*@
9101:    MatPtAP - Creates the matrix product C = P^T * A * P

9103:    Neighbor-wise Collective on Mat

9105:    Input Parameters:
9106: +  A - the matrix
9107: .  P - the projection matrix
9108: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9109: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use PETSC_DEFAULT if you do not have a good estimate
9110:           if the result is a dense matrix this is irrelevent

9112:    Output Parameters:
9113: .  C - the product matrix

9115:    Notes:
9116:    C will be created and must be destroyed by the user with MatDestroy().

9118:    This routine is currently only implemented for pairs of AIJ matrices and classes
9119:    which inherit from AIJ.

9121:    Level: intermediate

9123: .seealso: MatPtAPSymbolic(), MatPtAPNumeric(), MatMatMult(), MatRARt()
9124: @*/
9125: PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C)
9126: {
9128:   PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*);
9129:   PetscErrorCode (*fP)(Mat,Mat,MatReuse,PetscReal,Mat*);
9130:   PetscErrorCode (*ptap)(Mat,Mat,MatReuse,PetscReal,Mat*)=NULL;
9131:   PetscBool      viatranspose=PETSC_FALSE,viamatmatmatmult=PETSC_FALSE;

9134:   PetscOptionsGetBool(((PetscObject)A)->options,((PetscObject)A)->prefix,"-matptap_viatranspose",&viatranspose,NULL);
9135:   PetscOptionsGetBool(((PetscObject)A)->options,((PetscObject)A)->prefix,"-matptap_viamatmatmatmult",&viamatmatmatmult,NULL);

9139:   MatCheckPreallocated(A,1);
9140:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
9141:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9142:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9145:   MatCheckPreallocated(P,2);
9146:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9147:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9149:   if (A->rmap->N!= A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix A must be square, %D != %D",A->rmap->N,A->cmap->N);
9150:   if (P->rmap->N != A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N);
9151:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9152:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9154:   if (scall == MAT_REUSE_MATRIX) {
9157:     if (viatranspose || viamatmatmatmult) {
9158:       Mat Pt;
9159:       MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);
9160:       if (viamatmatmatmult) {
9161:         MatMatMatMult(Pt,A,P,scall,fill,C);
9162:       } else {
9163:         Mat AP;
9164:         MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);
9165:         MatMatMult(Pt,AP,scall,fill,C);
9166:         MatDestroy(&AP);
9167:       }
9168:       MatDestroy(&Pt);
9169:     } else {
9170:       PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9171:       PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9172:       (*(*C)->ops->ptapnumeric)(A,P,*C);
9173:       PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9174:       PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9175:     }
9176:     return(0);
9177:   }

9179:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9180:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9182:   fA = A->ops->ptap;
9183:   fP = P->ops->ptap;
9184:   if (fP == fA) {
9185:     if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",((PetscObject)A)->type_name);
9186:     ptap = fA;
9187:   } else {
9188:     /* dispatch based on the type of A and P from their PetscObject's PetscFunctionLists. */
9189:     char ptapname[256];
9190:     PetscStrcpy(ptapname,"MatPtAP_");
9191:     PetscStrcat(ptapname,((PetscObject)A)->type_name);
9192:     PetscStrcat(ptapname,"_");
9193:     PetscStrcat(ptapname,((PetscObject)P)->type_name);
9194:     PetscStrcat(ptapname,"_C"); /* e.g., ptapname = "MatPtAP_seqdense_seqaij_C" */
9195:     PetscObjectQueryFunction((PetscObject)P,ptapname,&ptap);
9196:     if (!ptap) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatPtAP requires A, %s, to be compatible with P, %s",((PetscObject)A)->type_name,((PetscObject)P)->type_name);
9197:   }

9199:   if (viatranspose || viamatmatmatmult) {
9200:     Mat Pt;
9201:     MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);
9202:     if (viamatmatmatmult) {
9203:       MatMatMatMult(Pt,A,P,scall,fill,C);
9204:       PetscInfo(*C,"MatPtAP via MatMatMatMult\n");
9205:     } else {
9206:       Mat AP;
9207:       MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);
9208:       MatMatMult(Pt,AP,scall,fill,C);
9209:       MatDestroy(&AP);
9210:       PetscInfo(*C,"MatPtAP via MatTranspose and MatMatMult\n");
9211:     }
9212:     MatDestroy(&Pt);
9213:   } else {
9214:     PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9215:     (*ptap)(A,P,scall,fill,C);
9216:     PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9217:   }
9218:   return(0);
9219: }

9221: /*@
9222:    MatPtAPNumeric - Computes the matrix product C = P^T * A * P

9224:    Neighbor-wise Collective on Mat

9226:    Input Parameters:
9227: +  A - the matrix
9228: -  P - the projection matrix

9230:    Output Parameters:
9231: .  C - the product matrix

9233:    Notes:
9234:    C must have been created by calling MatPtAPSymbolic and must be destroyed by
9235:    the user using MatDeatroy().

9237:    This routine is currently only implemented for pairs of AIJ matrices and classes
9238:    which inherit from AIJ.  C will be of type MATAIJ.

9240:    Level: intermediate

9242: .seealso: MatPtAP(), MatPtAPSymbolic(), MatMatMultNumeric()
9243: @*/
9244: PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C)
9245: {

9251:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9252:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9255:   MatCheckPreallocated(P,2);
9256:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9257:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9260:   MatCheckPreallocated(C,3);
9261:   if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9262:   if (P->cmap->N!=C->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->rmap->N);
9263:   if (P->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N);
9264:   if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N);
9265:   if (P->cmap->N!=C->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->cmap->N);
9266:   MatCheckPreallocated(A,1);

9268:   PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9269:   (*C->ops->ptapnumeric)(A,P,C);
9270:   PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9271:   return(0);
9272: }

9274: /*@
9275:    MatPtAPSymbolic - Creates the (i,j) structure of the matrix product C = P^T * A * P

9277:    Neighbor-wise Collective on Mat

9279:    Input Parameters:
9280: +  A - the matrix
9281: -  P - the projection matrix

9283:    Output Parameters:
9284: .  C - the (i,j) structure of the product matrix

9286:    Notes:
9287:    C will be created and must be destroyed by the user with MatDestroy().

9289:    This routine is currently only implemented for pairs of SeqAIJ matrices and classes
9290:    which inherit from SeqAIJ.  C will be of type MATSEQAIJ.  The product is computed using
9291:    this (i,j) structure by calling MatPtAPNumeric().

9293:    Level: intermediate

9295: .seealso: MatPtAP(), MatPtAPNumeric(), MatMatMultSymbolic()
9296: @*/
9297: PetscErrorCode MatPtAPSymbolic(Mat A,Mat P,PetscReal fill,Mat *C)
9298: {

9304:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9305:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9306:   if (fill <1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);
9309:   MatCheckPreallocated(P,2);
9310:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9311:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9314:   if (P->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N);
9315:   if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N);
9316:   MatCheckPreallocated(A,1);
9317:   PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);
9318:   (*A->ops->ptapsymbolic)(A,P,fill,C);
9319:   PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);

9321:   /* MatSetBlockSize(*C,A->rmap->bs); NO! this is not always true -ma */
9322:   return(0);
9323: }

9325: /*@
9326:    MatRARt - Creates the matrix product C = R * A * R^T

9328:    Neighbor-wise Collective on Mat

9330:    Input Parameters:
9331: +  A - the matrix
9332: .  R - the projection matrix
9333: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9334: -  fill - expected fill as ratio of nnz(C)/nnz(A), use PETSC_DEFAULT if you do not have a good estimate
9335:           if the result is a dense matrix this is irrelevent

9337:    Output Parameters:
9338: .  C - the product matrix

9340:    Notes:
9341:    C will be created and must be destroyed by the user with MatDestroy().

9343:    This routine is currently only implemented for pairs of AIJ matrices and classes
9344:    which inherit from AIJ. Due to PETSc sparse matrix block row distribution among processes,
9345:    parallel MatRARt is implemented via explicit transpose of R, which could be very expensive.
9346:    We recommend using MatPtAP().

9348:    Level: intermediate

9350: .seealso: MatRARtSymbolic(), MatRARtNumeric(), MatMatMult(), MatPtAP()
9351: @*/
9352: PetscErrorCode MatRARt(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C)
9353: {

9359:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
9360:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9361:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9364:   MatCheckPreallocated(R,2);
9365:   if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9366:   if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9368:   if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)R),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N);

9370:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9371:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);
9372:   MatCheckPreallocated(A,1);

9374:   if (!A->ops->rart) {
9375:     Mat Rt;
9376:     MatTranspose(R,MAT_INITIAL_MATRIX,&Rt);
9377:     MatMatMatMult(R,A,Rt,scall,fill,C);
9378:     MatDestroy(&Rt);
9379:   }
9380:   PetscLogEventBegin(MAT_RARt,A,R,0,0);
9381:   (*A->ops->rart)(A,R,scall,fill,C);
9382:   PetscLogEventEnd(MAT_RARt,A,R,0,0);
9383:   return(0);
9384: }

9386: /*@
9387:    MatRARtNumeric - Computes the matrix product C = R * A * R^T

9389:    Neighbor-wise Collective on Mat

9391:    Input Parameters:
9392: +  A - the matrix
9393: -  R - the projection matrix

9395:    Output Parameters:
9396: .  C - the product matrix

9398:    Notes:
9399:    C must have been created by calling MatRARtSymbolic and must be destroyed by
9400:    the user using MatDestroy().

9402:    This routine is currently only implemented for pairs of AIJ matrices and classes
9403:    which inherit from AIJ.  C will be of type MATAIJ.

9405:    Level: intermediate

9407: .seealso: MatRARt(), MatRARtSymbolic(), MatMatMultNumeric()
9408: @*/
9409: PetscErrorCode