Actual source code: matrix.c

petsc-master 2017-03-22
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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:       MatDestroy(&redund->matseq[0]);
1125:       PetscFree(redund->matseq);
1126:     } else {
1127:       PetscFree2(redund->send_rank,redund->recv_rank);
1128:       PetscFree(redund->sbuf_j);
1129:       PetscFree(redund->sbuf_a);
1130:       for (i=0; i<redund->nrecvs; i++) {
1131:         PetscFree(redund->rbuf_j[i]);
1132:         PetscFree(redund->rbuf_a[i]);
1133:       }
1134:       PetscFree4(redund->sbuf_nz,redund->rbuf_nz,redund->rbuf_j,redund->rbuf_a);
1135:     }

1137:     if (redund->subcomm) {
1138:       PetscCommDestroy(&redund->subcomm);
1139:     }
1140:     PetscFree(redund);
1141:   }
1142:   return(0);
1143: }

1145: /*@
1146:    MatDestroy - Frees space taken by a matrix.

1148:    Collective on Mat

1150:    Input Parameter:
1151: .  A - the matrix

1153:    Level: beginner

1155: @*/
1156: PetscErrorCode MatDestroy(Mat *A)
1157: {

1161:   if (!*A) return(0);
1163:   if (--((PetscObject)(*A))->refct > 0) {*A = NULL; return(0);}

1165:   /* if memory was published with SAWs then destroy it */
1166:   PetscObjectSAWsViewOff((PetscObject)*A);
1167:   if ((*A)->ops->destroy) {
1168:     (*(*A)->ops->destroy)(*A);
1169:   }

1171:   PetscFree((*A)->solvertype);
1172:   MatDestroy_Redundant(&(*A)->redundant);
1173:   MatNullSpaceDestroy(&(*A)->nullsp);
1174:   MatNullSpaceDestroy(&(*A)->transnullsp);
1175:   MatNullSpaceDestroy(&(*A)->nearnullsp);
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,"Mat type %s",((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: PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X)
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:     MatSolve(A,b,x);
3293:     VecResetArray(x);
3294:     VecResetArray(b);
3295:   }
3296:   VecDestroy(&b);
3297:   VecDestroy(&x);
3298:   MatDenseRestoreArray(B,&bb);
3299:   MatDenseRestoreArray(X,&xx);
3300:   return(0);
3301: }

3303: /*@
3304:    MatMatSolve - Solves A X = B, given a factored matrix.

3306:    Neighbor-wise Collective on Mat

3308:    Input Parameters:
3309: +  A - the factored matrix
3310: -  B - the right-hand-side matrix  (dense matrix)

3312:    Output Parameter:
3313: .  X - the result matrix (dense matrix)

3315:    Notes:
3316:    The matrices b and x cannot be the same.  I.e., one cannot
3317:    call MatMatSolve(A,x,x).

3319:    Notes:
3320:    Most users should usually employ the simplified KSP interface for linear solvers
3321:    instead of working directly with matrix algebra routines such as this.
3322:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3323:    at a time.

3325:    When using SuperLU_Dist as a parallel solver PETSc will use the SuperLU_Dist functionality to solve multiple right hand sides simultaneously. For MUMPS
3326:    it calls a separate solve for each right hand side since MUMPS does not yet support distributed right hand sides.

3328:    Since the resulting matrix X must always be dense we do not support sparse representation of the matrix B.

3330:    Level: developer

3332:    Concepts: matrices^triangular solves

3334: .seealso: MatMatSolveAdd(), MatMatSolveTranspose(), MatMatSolveTransposeAdd(), MatLUFactor(), MatCholeskyFactor()
3335: @*/
3336: PetscErrorCode MatMatSolve(Mat A,Mat B,Mat X)
3337: {

3347:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3348:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3349:   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);
3350:   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);
3351:   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);
3352:   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");
3353:   if (!A->rmap->N && !A->cmap->N) return(0);
3354:   MatCheckPreallocated(A,1);

3356:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3357:   if (!A->ops->matsolve) {
3358:     PetscInfo1(A,"Mat type %s using basic MatMatSolve\n",((PetscObject)A)->type_name);
3359:     MatMatSolve_Basic(A,B,X);
3360:   } else {
3361:     (*A->ops->matsolve)(A,B,X);
3362:   }
3363:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3364:   PetscObjectStateIncrease((PetscObject)X);
3365:   return(0);
3366: }


3369: /*@
3370:    MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3371:                             U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3373:    Neighbor-wise Collective on Mat and Vec

3375:    Input Parameters:
3376: +  mat - the factored matrix
3377: -  b - the right-hand-side vector

3379:    Output Parameter:
3380: .  x - the result vector

3382:    Notes:
3383:    MatSolve() should be used for most applications, as it performs
3384:    a forward solve followed by a backward solve.

3386:    The vectors b and x cannot be the same,  i.e., one cannot
3387:    call MatForwardSolve(A,x,x).

3389:    For matrix in seqsbaij format with block size larger than 1,
3390:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3391:    MatForwardSolve() solves U^T*D y = b, and
3392:    MatBackwardSolve() solves U x = y.
3393:    Thus they do not provide a symmetric preconditioner.

3395:    Most users should employ the simplified KSP interface for linear solvers
3396:    instead of working directly with matrix algebra routines such as this.
3397:    See, e.g., KSPCreate().

3399:    Level: developer

3401:    Concepts: matrices^forward solves

3403: .seealso: MatSolve(), MatBackwardSolve()
3404: @*/
3405: PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x)
3406: {

3416:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3417:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3418:   if (!mat->ops->forwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3419:   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);
3420:   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);
3421:   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);
3422:   MatCheckPreallocated(mat,1);
3423:   PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);
3424:   (*mat->ops->forwardsolve)(mat,b,x);
3425:   PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);
3426:   PetscObjectStateIncrease((PetscObject)x);
3427:   return(0);
3428: }

3430: /*@
3431:    MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3432:                              D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3434:    Neighbor-wise Collective on Mat and Vec

3436:    Input Parameters:
3437: +  mat - the factored matrix
3438: -  b - the right-hand-side vector

3440:    Output Parameter:
3441: .  x - the result vector

3443:    Notes:
3444:    MatSolve() should be used for most applications, as it performs
3445:    a forward solve followed by a backward solve.

3447:    The vectors b and x cannot be the same.  I.e., one cannot
3448:    call MatBackwardSolve(A,x,x).

3450:    For matrix in seqsbaij format with block size larger than 1,
3451:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3452:    MatForwardSolve() solves U^T*D y = b, and
3453:    MatBackwardSolve() solves U x = y.
3454:    Thus they do not provide a symmetric preconditioner.

3456:    Most users should employ the simplified KSP interface for linear solvers
3457:    instead of working directly with matrix algebra routines such as this.
3458:    See, e.g., KSPCreate().

3460:    Level: developer

3462:    Concepts: matrices^backward solves

3464: .seealso: MatSolve(), MatForwardSolve()
3465: @*/
3466: PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x)
3467: {

3477:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3478:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3479:   if (!mat->ops->backwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3480:   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);
3481:   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);
3482:   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);
3483:   MatCheckPreallocated(mat,1);

3485:   PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);
3486:   (*mat->ops->backwardsolve)(mat,b,x);
3487:   PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);
3488:   PetscObjectStateIncrease((PetscObject)x);
3489:   return(0);
3490: }

3492: /*@
3493:    MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3495:    Neighbor-wise Collective on Mat and Vec

3497:    Input Parameters:
3498: +  mat - the factored matrix
3499: .  b - the right-hand-side vector
3500: -  y - the vector to be added to

3502:    Output Parameter:
3503: .  x - the result vector

3505:    Notes:
3506:    The vectors b and x cannot be the same.  I.e., one cannot
3507:    call MatSolveAdd(A,x,y,x).

3509:    Most users should employ the simplified KSP interface for linear solvers
3510:    instead of working directly with matrix algebra routines such as this.
3511:    See, e.g., KSPCreate().

3513:    Level: developer

3515:    Concepts: matrices^triangular solves

3517: .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd()
3518: @*/
3519: PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x)
3520: {
3521:   PetscScalar    one = 1.0;
3522:   Vec            tmp;

3534:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3535:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3536:   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);
3537:   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);
3538:   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);
3539:   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);
3540:   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);
3541:   MatCheckPreallocated(mat,1);

3543:   PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);
3544:   if (mat->ops->solveadd) {
3545:     (*mat->ops->solveadd)(mat,b,y,x);
3546:   } else {
3547:     /* do the solve then the add manually */
3548:     if (x != y) {
3549:       MatSolve(mat,b,x);
3550:       VecAXPY(x,one,y);
3551:     } else {
3552:       VecDuplicate(x,&tmp);
3553:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3554:       VecCopy(x,tmp);
3555:       MatSolve(mat,b,x);
3556:       VecAXPY(x,one,tmp);
3557:       VecDestroy(&tmp);
3558:     }
3559:   }
3560:   PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);
3561:   PetscObjectStateIncrease((PetscObject)x);
3562:   return(0);
3563: }

3565: /*@
3566:    MatSolveTranspose - Solves A' x = b, given a factored matrix.

3568:    Neighbor-wise Collective on Mat and Vec

3570:    Input Parameters:
3571: +  mat - the factored matrix
3572: -  b - the right-hand-side vector

3574:    Output Parameter:
3575: .  x - the result vector

3577:    Notes:
3578:    The vectors b and x cannot be the same.  I.e., one cannot
3579:    call MatSolveTranspose(A,x,x).

3581:    Most users should employ the simplified KSP interface for linear solvers
3582:    instead of working directly with matrix algebra routines such as this.
3583:    See, e.g., KSPCreate().

3585:    Level: developer

3587:    Concepts: matrices^triangular solves

3589: .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd()
3590: @*/
3591: PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x)
3592: {

3602:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3603:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3604:   if (!mat->ops->solvetranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name);
3605:   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);
3606:   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);
3607:   MatCheckPreallocated(mat,1);
3608:   PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);
3609:   if (mat->factorerrortype) {
3610:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3611:     VecSetInf(x);
3612:   } else {
3613:     (*mat->ops->solvetranspose)(mat,b,x);
3614:   }
3615:   PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);
3616:   PetscObjectStateIncrease((PetscObject)x);
3617:   return(0);
3618: }

3620: /*@
3621:    MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
3622:                       factored matrix.

3624:    Neighbor-wise Collective on Mat and Vec

3626:    Input Parameters:
3627: +  mat - the factored matrix
3628: .  b - the right-hand-side vector
3629: -  y - the vector to be added to

3631:    Output Parameter:
3632: .  x - the result vector

3634:    Notes:
3635:    The vectors b and x cannot be the same.  I.e., one cannot
3636:    call MatSolveTransposeAdd(A,x,y,x).

3638:    Most users should employ the simplified KSP interface for linear solvers
3639:    instead of working directly with matrix algebra routines such as this.
3640:    See, e.g., KSPCreate().

3642:    Level: developer

3644:    Concepts: matrices^triangular solves

3646: .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose()
3647: @*/
3648: PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x)
3649: {
3650:   PetscScalar    one = 1.0;
3652:   Vec            tmp;

3663:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3664:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3665:   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);
3666:   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);
3667:   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);
3668:   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);
3669:   MatCheckPreallocated(mat,1);

3671:   PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);
3672:   if (mat->ops->solvetransposeadd) {
3673:     if (mat->factorerrortype) {
3674:       PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3675:       VecSetInf(x);
3676:     } else {
3677:       (*mat->ops->solvetransposeadd)(mat,b,y,x);
3678:     }
3679:   } else {
3680:     /* do the solve then the add manually */
3681:     if (x != y) {
3682:       MatSolveTranspose(mat,b,x);
3683:       VecAXPY(x,one,y);
3684:     } else {
3685:       VecDuplicate(x,&tmp);
3686:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3687:       VecCopy(x,tmp);
3688:       MatSolveTranspose(mat,b,x);
3689:       VecAXPY(x,one,tmp);
3690:       VecDestroy(&tmp);
3691:     }
3692:   }
3693:   PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);
3694:   PetscObjectStateIncrease((PetscObject)x);
3695:   return(0);
3696: }
3697: /* ----------------------------------------------------------------*/

3699: /*@
3700:    MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

3702:    Neighbor-wise Collective on Mat and Vec

3704:    Input Parameters:
3705: +  mat - the matrix
3706: .  b - the right hand side
3707: .  omega - the relaxation factor
3708: .  flag - flag indicating the type of SOR (see below)
3709: .  shift -  diagonal shift
3710: .  its - the number of iterations
3711: -  lits - the number of local iterations

3713:    Output Parameters:
3714: .  x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess)

3716:    SOR Flags:
3717: .     SOR_FORWARD_SWEEP - forward SOR
3718: .     SOR_BACKWARD_SWEEP - backward SOR
3719: .     SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR)
3720: .     SOR_LOCAL_FORWARD_SWEEP - local forward SOR
3721: .     SOR_LOCAL_BACKWARD_SWEEP - local forward SOR
3722: .     SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR
3723: .     SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies
3724:          upper/lower triangular part of matrix to
3725:          vector (with omega)
3726: .     SOR_ZERO_INITIAL_GUESS - zero initial guess

3728:    Notes:
3729:    SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and
3730:    SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings
3731:    on each processor.

3733:    Application programmers will not generally use MatSOR() directly,
3734:    but instead will employ the KSP/PC interface.

3736:    Notes: for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

3738:    Notes for Advanced Users:
3739:    The flags are implemented as bitwise inclusive or operations.
3740:    For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP)
3741:    to specify a zero initial guess for SSOR.

3743:    Most users should employ the simplified KSP interface for linear solvers
3744:    instead of working directly with matrix algebra routines such as this.
3745:    See, e.g., KSPCreate().

3747:    Vectors x and b CANNOT be the same

3749:    Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes

3751:    Level: developer

3753:    Concepts: matrices^relaxation
3754:    Concepts: matrices^SOR
3755:    Concepts: matrices^Gauss-Seidel

3757: @*/
3758: PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x)
3759: {

3769:   if (!mat->ops->sor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3770:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3771:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3772:   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);
3773:   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);
3774:   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);
3775:   if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its);
3776:   if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits);
3777:   if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same");

3779:   MatCheckPreallocated(mat,1);
3780:   PetscLogEventBegin(MAT_SOR,mat,b,x,0);
3781:   ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);
3782:   PetscLogEventEnd(MAT_SOR,mat,b,x,0);
3783:   PetscObjectStateIncrease((PetscObject)x);
3784:   return(0);
3785: }

3787: /*
3788:       Default matrix copy routine.
3789: */
3790: PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str)
3791: {
3792:   PetscErrorCode    ierr;
3793:   PetscInt          i,rstart = 0,rend = 0,nz;
3794:   const PetscInt    *cwork;
3795:   const PetscScalar *vwork;

3798:   if (B->assembled) {
3799:     MatZeroEntries(B);
3800:   }
3801:   MatGetOwnershipRange(A,&rstart,&rend);
3802:   for (i=rstart; i<rend; i++) {
3803:     MatGetRow(A,i,&nz,&cwork,&vwork);
3804:     MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);
3805:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
3806:   }
3807:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3808:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3809:   PetscObjectStateIncrease((PetscObject)B);
3810:   return(0);
3811: }

3813: /*@
3814:    MatCopy - Copys a matrix to another matrix.

3816:    Collective on Mat

3818:    Input Parameters:
3819: +  A - the matrix
3820: -  str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN

3822:    Output Parameter:
3823: .  B - where the copy is put

3825:    Notes:
3826:    If you use SAME_NONZERO_PATTERN then the two matrices had better have the
3827:    same nonzero pattern or the routine will crash.

3829:    MatCopy() copies the matrix entries of a matrix to another existing
3830:    matrix (after first zeroing the second matrix).  A related routine is
3831:    MatConvert(), which first creates a new matrix and then copies the data.

3833:    Level: intermediate

3835:    Concepts: matrices^copying

3837: .seealso: MatConvert(), MatDuplicate()

3839: @*/
3840: PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str)
3841: {
3843:   PetscInt       i;

3851:   MatCheckPreallocated(B,2);
3852:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3853:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3854:   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);
3855:   MatCheckPreallocated(A,1);

3857:   PetscLogEventBegin(MAT_Copy,A,B,0,0);
3858:   if (A->ops->copy) {
3859:     (*A->ops->copy)(A,B,str);
3860:   } else { /* generic conversion */
3861:     MatCopy_Basic(A,B,str);
3862:   }

3864:   B->stencil.dim = A->stencil.dim;
3865:   B->stencil.noc = A->stencil.noc;
3866:   for (i=0; i<=A->stencil.dim; i++) {
3867:     B->stencil.dims[i]   = A->stencil.dims[i];
3868:     B->stencil.starts[i] = A->stencil.starts[i];
3869:   }

3871:   PetscLogEventEnd(MAT_Copy,A,B,0,0);
3872:   PetscObjectStateIncrease((PetscObject)B);
3873:   return(0);
3874: }

3876: /*@C
3877:    MatConvert - Converts a matrix to another matrix, either of the same
3878:    or different type.

3880:    Collective on Mat

3882:    Input Parameters:
3883: +  mat - the matrix
3884: .  newtype - new matrix type.  Use MATSAME to create a new matrix of the
3885:    same type as the original matrix.
3886: -  reuse - denotes if the destination matrix is to be created or reused.
3887:    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
3888:    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).

3890:    Output Parameter:
3891: .  M - pointer to place new matrix

3893:    Notes:
3894:    MatConvert() first creates a new matrix and then copies the data from
3895:    the first matrix.  A related routine is MatCopy(), which copies the matrix
3896:    entries of one matrix to another already existing matrix context.

3898:    Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
3899:    the MPI communicator of the generated matrix is always the same as the communicator
3900:    of the input matrix.

3902:    Level: intermediate

3904:    Concepts: matrices^converting between storage formats

3906: .seealso: MatCopy(), MatDuplicate()
3907: @*/
3908: PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M)
3909: {
3911:   PetscBool      sametype,issame,flg;
3912:   char           convname[256],mtype[256];
3913:   Mat            B;

3919:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3920:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3921:   MatCheckPreallocated(mat,1);
3922:   MatSetOption(mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);

3924:   PetscOptionsGetString(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);
3925:   if (flg) {
3926:     newtype = mtype;
3927:   }
3928:   PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);
3929:   PetscStrcmp(newtype,"same",&issame);
3930:   if ((reuse == MAT_INPLACE_MATRIX) && (mat != *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires same input and output matrix");
3931:   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");

3933:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) return(0);

3935:   if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
3936:     (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);
3937:   } else {
3938:     PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=NULL;
3939:     const char     *prefix[3] = {"seq","mpi",""};
3940:     PetscInt       i;
3941:     /*
3942:        Order of precedence:
3943:        1) See if a specialized converter is known to the current matrix.
3944:        2) See if a specialized converter is known to the desired matrix class.
3945:        3) See if a good general converter is registered for the desired class
3946:           (as of 6/27/03 only MATMPIADJ falls into this category).
3947:        4) See if a good general converter is known for the current matrix.
3948:        5) Use a really basic converter.
3949:     */

3951:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
3952:     for (i=0; i<3; i++) {
3953:       PetscStrcpy(convname,"MatConvert_");
3954:       PetscStrcat(convname,((PetscObject)mat)->type_name);
3955:       PetscStrcat(convname,"_");
3956:       PetscStrcat(convname,prefix[i]);
3957:       PetscStrcat(convname,issame ? ((PetscObject)mat)->type_name : newtype);
3958:       PetscStrcat(convname,"_C");
3959:       PetscObjectQueryFunction((PetscObject)mat,convname,&conv);
3960:       if (conv) goto foundconv;
3961:     }

3963:     /* 2)  See if a specialized converter is known to the desired matrix class. */
3964:     MatCreate(PetscObjectComm((PetscObject)mat),&B);
3965:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);
3966:     MatSetType(B,newtype);
3967:     for (i=0; i<3; i++) {
3968:       PetscStrcpy(convname,"MatConvert_");
3969:       PetscStrcat(convname,((PetscObject)mat)->type_name);
3970:       PetscStrcat(convname,"_");
3971:       PetscStrcat(convname,prefix[i]);
3972:       PetscStrcat(convname,newtype);
3973:       PetscStrcat(convname,"_C");
3974:       PetscObjectQueryFunction((PetscObject)B,convname,&conv);
3975:       if (conv) {
3976:         MatDestroy(&B);
3977:         goto foundconv;
3978:       }
3979:     }

3981:     /* 3) See if a good general converter is registered for the desired class */
3982:     conv = B->ops->convertfrom;
3983:     MatDestroy(&B);
3984:     if (conv) goto foundconv;

3986:     /* 4) See if a good general converter is known for the current matrix */
3987:     if (mat->ops->convert) {
3988:       conv = mat->ops->convert;
3989:     }
3990:     if (conv) goto foundconv;

3992:     /* 5) Use a really basic converter. */
3993:     conv = MatConvert_Basic;

3995: foundconv:
3996:     PetscLogEventBegin(MAT_Convert,mat,0,0,0);
3997:     (*conv)(mat,newtype,reuse,M);
3998:     PetscLogEventEnd(MAT_Convert,mat,0,0,0);
3999:   }
4000:   PetscObjectStateIncrease((PetscObject)*M);

4002:   /* Copy Mat options */
4003:   if (mat->symmetric) {MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);}
4004:   if (mat->hermitian) {MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);}
4005:   return(0);
4006: }

4008: /*@C
4009:    MatFactorGetSolverPackage - Returns name of the package providing the factorization routines

4011:    Not Collective

4013:    Input Parameter:
4014: .  mat - the matrix, must be a factored matrix

4016:    Output Parameter:
4017: .   type - the string name of the package (do not free this string)

4019:    Notes:
4020:       In Fortran you pass in a empty string and the package name will be copied into it.
4021:     (Make sure the string is long enough)

4023:    Level: intermediate

4025: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor()
4026: @*/
4027: PetscErrorCode MatFactorGetSolverPackage(Mat mat, const MatSolverPackage *type)
4028: {
4029:   PetscErrorCode ierr, (*conv)(Mat,const MatSolverPackage*);

4034:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
4035:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverPackage_C",&conv);
4036:   if (!conv) {
4037:     *type = MATSOLVERPETSC;
4038:   } else {
4039:     (*conv)(mat,type);
4040:   }
4041:   return(0);
4042: }

4044: typedef struct _MatSolverPackageForSpecifcType* MatSolverPackageForSpecifcType;
4045: struct _MatSolverPackageForSpecifcType {
4046:   MatType                        mtype;
4047:   PetscErrorCode                 (*getfactor[4])(Mat,MatFactorType,Mat*);
4048:   MatSolverPackageForSpecifcType next;
4049: };

4051: typedef struct _MatSolverPackageHolder* MatSolverPackageHolder;
4052: struct _MatSolverPackageHolder {
4053:   char                           *name;
4054:   MatSolverPackageForSpecifcType handlers;
4055:   MatSolverPackageHolder         next;
4056: };

4058: static MatSolverPackageHolder MatSolverPackageHolders = NULL;

4060: /*@C
4061:    MatSolvePackageRegister - Registers a MatSolverPackage that works for a particular matrix type

4063:    Input Parameters:
4064: +    package - name of the package, for example petsc or superlu
4065: .    mtype - the matrix type that works with this package
4066: .    ftype - the type of factorization supported by the package
4067: -    getfactor - routine that will create the factored matrix ready to be used

4069:     Level: intermediate

4071: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4072: @*/
4073: PetscErrorCode MatSolverPackageRegister(const MatSolverPackage package,const MatType mtype,MatFactorType ftype,PetscErrorCode (*getfactor)(Mat,MatFactorType,Mat*))
4074: {
4075:   PetscErrorCode                 ierr;
4076:   MatSolverPackageHolder         next = MatSolverPackageHolders,prev;
4077:   PetscBool                      flg;
4078:   MatSolverPackageForSpecifcType inext,iprev = NULL;

4081:   if (!next) {
4082:     PetscNew(&MatSolverPackageHolders);
4083:     PetscStrallocpy(package,&MatSolverPackageHolders->name);
4084:     PetscNew(&MatSolverPackageHolders->handlers);
4085:     PetscStrallocpy(mtype,(char **)&MatSolverPackageHolders->handlers->mtype);
4086:     MatSolverPackageHolders->handlers->getfactor[(int)ftype-1] = getfactor;
4087:     return(0);
4088:   }
4089:   while (next) {
4090:     PetscStrcasecmp(package,next->name,&flg);
4091:     if (flg) {
4092:       if (!next->handlers) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MatSolverPackageHolder is missing handlers");
4093:       inext = next->handlers;
4094:       while (inext) {
4095:         PetscStrcasecmp(mtype,inext->mtype,&flg);
4096:         if (flg) {
4097:           inext->getfactor[(int)ftype-1] = getfactor;
4098:           return(0);
4099:         }
4100:         iprev = inext;
4101:         inext = inext->next;
4102:       }
4103:       PetscNew(&iprev->next);
4104:       PetscStrallocpy(mtype,(char **)&iprev->next->mtype);
4105:       iprev->next->getfactor[(int)ftype-1] = getfactor;
4106:       return(0);
4107:     }
4108:     prev = next;
4109:     next = next->next;
4110:   }
4111:   PetscNew(&prev->next);
4112:   PetscStrallocpy(package,&prev->next->name);
4113:   PetscNew(&prev->next->handlers);
4114:   PetscStrallocpy(mtype,(char **)&prev->next->handlers->mtype);
4115:   prev->next->handlers->getfactor[(int)ftype-1] = getfactor;
4116:   return(0);
4117: }

4119: /*@C
4120:    MatSolvePackageGet - Get's the function that creates the factor matrix if it exist

4122:    Input Parameters:
4123: +    package - name of the package, for example petsc or superlu
4124: .    ftype - the type of factorization supported by the package
4125: -    mtype - the matrix type that works with this package

4127:    Output Parameters:
4128: +   foundpackage - PETSC_TRUE if the package was registered
4129: .   foundmtype - PETSC_TRUE if the package supports the requested mtype
4130: -   getfactor - routine that will create the factored matrix ready to be used or NULL if not found

4132:     Level: intermediate

4134: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4135: @*/
4136: PetscErrorCode MatSolverPackageGet(const MatSolverPackage package,const MatType mtype,MatFactorType ftype,PetscBool *foundpackage,PetscBool *foundmtype,PetscErrorCode (**getfactor)(Mat,MatFactorType,Mat*))
4137: {
4138:   PetscErrorCode                 ierr;
4139:   MatSolverPackageHolder         next = MatSolverPackageHolders;
4140:   PetscBool                      flg;
4141:   MatSolverPackageForSpecifcType inext;

4144:   if (foundpackage) *foundpackage = PETSC_FALSE;
4145:   if (foundmtype)   *foundmtype   = PETSC_FALSE;
4146:   if (getfactor)    *getfactor    = NULL;

4148:   if (package) {
4149:     while (next) {
4150:       PetscStrcasecmp(package,next->name,&flg);
4151:       if (flg) {
4152:         if (foundpackage) *foundpackage = PETSC_TRUE;
4153:         inext = next->handlers;
4154:         while (inext) {
4155:           PetscStrcasecmp(mtype,inext->mtype,&flg);
4156:           if (flg) {
4157:             if (foundmtype) *foundmtype = PETSC_TRUE;
4158:             if (getfactor)  *getfactor  = inext->getfactor[(int)ftype-1];
4159:             return(0);
4160:           }
4161:           inext = inext->next;
4162:         }
4163:       }
4164:       next = next->next;
4165:     }
4166:   } else {
4167:     while (next) {
4168:       inext = next->handlers;
4169:       while (inext) {
4170:         PetscStrcasecmp(mtype,inext->mtype,&flg);
4171:         if (flg && inext->getfactor[(int)ftype-1]) {
4172:           if (foundpackage) *foundpackage = PETSC_TRUE;
4173:           if (foundmtype)   *foundmtype   = PETSC_TRUE;
4174:           if (getfactor)    *getfactor    = inext->getfactor[(int)ftype-1];
4175:           return(0);
4176:         }
4177:         inext = inext->next;
4178:       }
4179:       next = next->next;
4180:     }
4181:   }
4182:   return(0);
4183: }

4185: PetscErrorCode MatSolverPackageDestroy(void)
4186: {
4187:   PetscErrorCode                 ierr;
4188:   MatSolverPackageHolder         next = MatSolverPackageHolders,prev;
4189:   MatSolverPackageForSpecifcType inext,iprev;

4192:   while (next) {
4193:     PetscFree(next->name);
4194:     inext = next->handlers;
4195:     while (inext) {
4196:       PetscFree(inext->mtype);
4197:       iprev = inext;
4198:       inext = inext->next;
4199:       PetscFree(iprev);
4200:     }
4201:     prev = next;
4202:     next = next->next;
4203:     PetscFree(prev);
4204:   }
4205:   MatSolverPackageHolders = NULL;
4206:   return(0);
4207: }

4209: /*@C
4210:    MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4212:    Collective on Mat

4214:    Input Parameters:
4215: +  mat - the matrix
4216: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4217: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4219:    Output Parameters:
4220: .  f - the factor matrix used with MatXXFactorSymbolic() calls

4222:    Notes:
4223:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4224:      such as pastix, superlu, mumps etc.

4226:       PETSc must have been ./configure to use the external solver, using the option --download-package

4228:    Level: intermediate

4230: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4231: @*/
4232: PetscErrorCode MatGetFactor(Mat mat, const MatSolverPackage type,MatFactorType ftype,Mat *f)
4233: {
4234:   PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*);
4235:   PetscBool      foundpackage,foundmtype;


4241:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4242:   MatCheckPreallocated(mat,1);

4244:   MatSolverPackageGet(type,((PetscObject)mat)->type_name,ftype,&foundpackage,&foundmtype,&conv);
4245:   if (!foundpackage) {
4246:     if (type) {
4247:       SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate solver package %s. Perhaps you must ./configure with --download-%s",type,type);
4248:     } else {
4249:       SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate a solver package. Perhaps you must ./configure with --download-<package>");
4250:     }
4251:   }

4253:   if (!foundmtype) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverPackage %s does not support matrix type %s",type,((PetscObject)mat)->type_name);
4254:   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);

4256:   (*conv)(mat,ftype,f);
4257:   return(0);
4258: }

4260: /*@C
4261:    MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type

4263:    Not Collective

4265:    Input Parameters:
4266: +  mat - the matrix
4267: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4268: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4270:    Output Parameter:
4271: .    flg - PETSC_TRUE if the factorization is available

4273:    Notes:
4274:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4275:      such as pastix, superlu, mumps etc.

4277:       PETSc must have been ./configure to use the external solver, using the option --download-package

4279:    Level: intermediate

4281: .seealso: MatCopy(), MatDuplicate(), MatGetFactor()
4282: @*/
4283: PetscErrorCode MatGetFactorAvailable(Mat mat, const MatSolverPackage type,MatFactorType ftype,PetscBool  *flg)
4284: {
4285:   PetscErrorCode ierr, (*gconv)(Mat,MatFactorType,Mat*);


4291:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4292:   MatCheckPreallocated(mat,1);

4294:   *flg = PETSC_FALSE;
4295:   MatSolverPackageGet(type,((PetscObject)mat)->type_name,ftype,NULL,NULL,&gconv);
4296:   if (gconv) {
4297:     *flg = PETSC_TRUE;
4298:   }
4299:   return(0);
4300: }

4302:  #include <petscdmtypes.h>

4304: /*@
4305:    MatDuplicate - Duplicates a matrix including the non-zero structure.

4307:    Collective on Mat

4309:    Input Parameters:
4310: +  mat - the matrix
4311: -  op - either MAT_DO_NOT_COPY_VALUES or MAT_COPY_VALUES, cause it to copy the numerical values in the matrix
4312:         MAT_SHARE_NONZERO_PATTERN to share the nonzero patterns with the previous matrix and not copy them.

4314:    Output Parameter:
4315: .  M - pointer to place new matrix

4317:    Level: intermediate

4319:    Concepts: matrices^duplicating

4321:     Notes: You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN.

4323: .seealso: MatCopy(), MatConvert()
4324: @*/
4325: PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M)
4326: {
4328:   Mat            B;
4329:   PetscInt       i;
4330:   DM             dm;

4336:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4337:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4338:   MatCheckPreallocated(mat,1);

4340:   *M = 0;
4341:   if (!mat->ops->duplicate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not written for this matrix type");
4342:   PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4343:   (*mat->ops->duplicate)(mat,op,M);
4344:   B    = *M;

4346:   B->stencil.dim = mat->stencil.dim;
4347:   B->stencil.noc = mat->stencil.noc;
4348:   for (i=0; i<=mat->stencil.dim; i++) {
4349:     B->stencil.dims[i]   = mat->stencil.dims[i];
4350:     B->stencil.starts[i] = mat->stencil.starts[i];
4351:   }

4353:   B->nooffproczerorows = mat->nooffproczerorows;
4354:   B->nooffprocentries  = mat->nooffprocentries;

4356:   PetscObjectQuery((PetscObject) mat, "__PETSc_dm", (PetscObject*) &dm);
4357:   if (dm) {
4358:     PetscObjectCompose((PetscObject) B, "__PETSc_dm", (PetscObject) dm);
4359:   }
4360:   PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4361:   PetscObjectStateIncrease((PetscObject)B);
4362:   return(0);
4363: }

4365: /*@
4366:    MatGetDiagonal - Gets the diagonal of a matrix.

4368:    Logically Collective on Mat and Vec

4370:    Input Parameters:
4371: +  mat - the matrix
4372: -  v - the vector for storing the diagonal

4374:    Output Parameter:
4375: .  v - the diagonal of the matrix

4377:    Level: intermediate

4379:    Note:
4380:    Currently only correct in parallel for square matrices.

4382:    Concepts: matrices^accessing diagonals

4384: .seealso: MatGetRow(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs()
4385: @*/
4386: PetscErrorCode MatGetDiagonal(Mat mat,Vec v)
4387: {

4394:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4395:   if (!mat->ops->getdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4396:   MatCheckPreallocated(mat,1);

4398:   (*mat->ops->getdiagonal)(mat,v);
4399:   PetscObjectStateIncrease((PetscObject)v);
4400:   return(0);
4401: }

4403: /*@C
4404:    MatGetRowMin - Gets the minimum value (of the real part) of each
4405:         row of the matrix

4407:    Logically Collective on Mat and Vec

4409:    Input Parameters:
4410: .  mat - the matrix

4412:    Output Parameter:
4413: +  v - the vector for storing the maximums
4414: -  idx - the indices of the column found for each row (optional)

4416:    Level: intermediate

4418:    Notes: The result of this call are the same as if one converted the matrix to dense format
4419:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4421:     This code is only implemented for a couple of matrix formats.

4423:    Concepts: matrices^getting row maximums

4425: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs(),
4426:           MatGetRowMax()
4427: @*/
4428: PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[])
4429: {

4436:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4437:   if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4438:   MatCheckPreallocated(mat,1);

4440:   (*mat->ops->getrowmin)(mat,v,idx);
4441:   PetscObjectStateIncrease((PetscObject)v);
4442:   return(0);
4443: }

4445: /*@C
4446:    MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4447:         row of the matrix

4449:    Logically Collective on Mat and Vec

4451:    Input Parameters:
4452: .  mat - the matrix

4454:    Output Parameter:
4455: +  v - the vector for storing the minimums
4456: -  idx - the indices of the column found for each row (or NULL if not needed)

4458:    Level: intermediate

4460:    Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that
4461:     row is 0 (the first column).

4463:     This code is only implemented for a couple of matrix formats.

4465:    Concepts: matrices^getting row maximums

4467: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin()
4468: @*/
4469: PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[])
4470: {

4477:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4478:   if (!mat->ops->getrowminabs) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4479:   MatCheckPreallocated(mat,1);
4480:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4482:   (*mat->ops->getrowminabs)(mat,v,idx);
4483:   PetscObjectStateIncrease((PetscObject)v);
4484:   return(0);
4485: }

4487: /*@C
4488:    MatGetRowMax - Gets the maximum value (of the real part) of each
4489:         row of the matrix

4491:    Logically Collective on Mat and Vec

4493:    Input Parameters:
4494: .  mat - the matrix

4496:    Output Parameter:
4497: +  v - the vector for storing the maximums
4498: -  idx - the indices of the column found for each row (optional)

4500:    Level: intermediate

4502:    Notes: The result of this call are the same as if one converted the matrix to dense format
4503:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4505:     This code is only implemented for a couple of matrix formats.

4507:    Concepts: matrices^getting row maximums

4509: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMaxAbs(), MatGetRowMin()
4510: @*/
4511: PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[])
4512: {

4519:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4520:   if (!mat->ops->getrowmax) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4521:   MatCheckPreallocated(mat,1);

4523:   (*mat->ops->getrowmax)(mat,v,idx);
4524:   PetscObjectStateIncrease((PetscObject)v);
4525:   return(0);
4526: }

4528: /*@C
4529:    MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
4530:         row of the matrix

4532:    Logically Collective on Mat and Vec

4534:    Input Parameters:
4535: .  mat - the matrix

4537:    Output Parameter:
4538: +  v - the vector for storing the maximums
4539: -  idx - the indices of the column found for each row (or NULL if not needed)

4541:    Level: intermediate

4543:    Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that
4544:     row is 0 (the first column).

4546:     This code is only implemented for a couple of matrix formats.

4548:    Concepts: matrices^getting row maximums

4550: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMin()
4551: @*/
4552: PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[])
4553: {

4560:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4561:   if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4562:   MatCheckPreallocated(mat,1);
4563:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4565:   (*mat->ops->getrowmaxabs)(mat,v,idx);
4566:   PetscObjectStateIncrease((PetscObject)v);
4567:   return(0);
4568: }

4570: /*@
4571:    MatGetRowSum - Gets the sum of each row of the matrix

4573:    Logically Collective on Mat and Vec

4575:    Input Parameters:
4576: .  mat - the matrix

4578:    Output Parameter:
4579: .  v - the vector for storing the sum of rows

4581:    Level: intermediate

4583:    Notes: This code is slow since it is not currently specialized for different formats

4585:    Concepts: matrices^getting row sums

4587: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubmatrix(), MatGetRowMax(), MatGetRowMin()
4588: @*/
4589: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
4590: {
4591:   PetscInt       start = 0, end = 0, row;
4592:   PetscScalar    *array;

4599:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4600:   MatCheckPreallocated(mat,1);
4601:   MatGetOwnershipRange(mat, &start, &end);
4602:   VecGetArray(v, &array);
4603:   for (row = start; row < end; ++row) {
4604:     PetscInt          ncols, col;
4605:     const PetscInt    *cols;
4606:     const PetscScalar *vals;

4608:     array[row - start] = 0.0;

4610:     MatGetRow(mat, row, &ncols, &cols, &vals);
4611:     for (col = 0; col < ncols; col++) {
4612:       array[row - start] += vals[col];
4613:     }
4614:     MatRestoreRow(mat, row, &ncols, &cols, &vals);
4615:   }
4616:   VecRestoreArray(v, &array);
4617:   PetscObjectStateIncrease((PetscObject) v);
4618:   return(0);
4619: }

4621: /*@
4622:    MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

4624:    Collective on Mat

4626:    Input Parameter:
4627: +  mat - the matrix to transpose
4628: -  reuse - either MAT_INITIAL_MATRIX, MAT_REUSE_MATRIX, or MAT_INPLACE_MATRIX

4630:    Output Parameters:
4631: .  B - the transpose

4633:    Notes:
4634:      If you use MAT_INPLACE_MATRIX then you must pass in &mat for B

4636:      MAT_REUSE_MATRIX causes the B matrix from a previous call to this function with MAT_INITIAL_MATRIX to be used

4638:      Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

4640:    Level: intermediate

4642:    Concepts: matrices^transposing

4644: .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4645: @*/
4646: PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B)
4647: {

4653:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4654:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4655:   if (!mat->ops->transpose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4656:   if (reuse == MAT_INPLACE_MATRIX && mat != *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires last matrix to match first");
4657:   if (reuse == MAT_REUSE_MATRIX && mat == *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Perhaps you mean MAT_INPLACE_MATRIX");
4658:   MatCheckPreallocated(mat,1);

4660:   PetscLogEventBegin(MAT_Transpose,mat,0,0,0);
4661:   (*mat->ops->transpose)(mat,reuse,B);
4662:   PetscLogEventEnd(MAT_Transpose,mat,0,0,0);
4663:   if (B) {PetscObjectStateIncrease((PetscObject)*B);}
4664:   return(0);
4665: }

4667: /*@
4668:    MatIsTranspose - Test whether a matrix is another one's transpose,
4669:         or its own, in which case it tests symmetry.

4671:    Collective on Mat

4673:    Input Parameter:
4674: +  A - the matrix to test
4675: -  B - the matrix to test against, this can equal the first parameter

4677:    Output Parameters:
4678: .  flg - the result

4680:    Notes:
4681:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4682:    has a running time of the order of the number of nonzeros; the parallel
4683:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4685:    Level: intermediate

4687:    Concepts: matrices^transposing, matrix^symmetry

4689: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian()
4690: @*/
4691: PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4692: {
4693:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4699:   PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",&f);
4700:   PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",&g);
4701:   *flg = PETSC_FALSE;
4702:   if (f && g) {
4703:     if (f == g) {
4704:       (*f)(A,B,tol,flg);
4705:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test");
4706:   } else {
4707:     MatType mattype;
4708:     if (!f) {
4709:       MatGetType(A,&mattype);
4710:     } else {
4711:       MatGetType(B,&mattype);
4712:     }
4713:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for transpose",mattype);
4714:   }
4715:   return(0);
4716: }

4718: /*@
4719:    MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate.

4721:    Collective on Mat

4723:    Input Parameter:
4724: +  mat - the matrix to transpose and complex conjugate
4725: -  reuse - MAT_INITIAL_MATRIX to create a new matrix, MAT_INPLACE_MATRIX to reuse the first argument to store the transpose

4727:    Output Parameters:
4728: .  B - the Hermitian

4730:    Level: intermediate

4732:    Concepts: matrices^transposing, complex conjugatex

4734: .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4735: @*/
4736: PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B)
4737: {

4741:   MatTranspose(mat,reuse,B);
4742: #if defined(PETSC_USE_COMPLEX)
4743:   MatConjugate(*B);
4744: #endif
4745:   return(0);
4746: }

4748: /*@
4749:    MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

4751:    Collective on Mat

4753:    Input Parameter:
4754: +  A - the matrix to test
4755: -  B - the matrix to test against, this can equal the first parameter

4757:    Output Parameters:
4758: .  flg - the result

4760:    Notes:
4761:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4762:    has a running time of the order of the number of nonzeros; the parallel
4763:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4765:    Level: intermediate

4767:    Concepts: matrices^transposing, matrix^symmetry

4769: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose()
4770: @*/
4771: PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4772: {
4773:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4779:   PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",&f);
4780:   PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",&g);
4781:   if (f && g) {
4782:     if (f==g) {
4783:       (*f)(A,B,tol,flg);
4784:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test");
4785:   }
4786:   return(0);
4787: }

4789: /*@
4790:    MatPermute - Creates a new matrix with rows and columns permuted from the
4791:    original.

4793:    Collective on Mat

4795:    Input Parameters:
4796: +  mat - the matrix to permute
4797: .  row - row permutation, each processor supplies only the permutation for its rows
4798: -  col - column permutation, each processor supplies only the permutation for its columns

4800:    Output Parameters:
4801: .  B - the permuted matrix

4803:    Level: advanced

4805:    Note:
4806:    The index sets map from row/col of permuted matrix to row/col of original matrix.
4807:    The index sets should be on the same communicator as Mat and have the same local sizes.

4809:    Concepts: matrices^permuting

4811: .seealso: MatGetOrdering(), ISAllGather()

4813: @*/
4814: PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B)
4815: {

4824:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4825:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4826:   if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name);
4827:   MatCheckPreallocated(mat,1);

4829:   (*mat->ops->permute)(mat,row,col,B);
4830:   PetscObjectStateIncrease((PetscObject)*B);
4831:   return(0);
4832: }

4834: /*@
4835:    MatEqual - Compares two matrices.

4837:    Collective on Mat

4839:    Input Parameters:
4840: +  A - the first matrix
4841: -  B - the second matrix

4843:    Output Parameter:
4844: .  flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise.

4846:    Level: intermediate

4848:    Concepts: matrices^equality between
4849: @*/
4850: PetscErrorCode MatEqual(Mat A,Mat B,PetscBool  *flg)
4851: {

4861:   MatCheckPreallocated(B,2);
4862:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4863:   if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4864:   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);
4865:   if (!A->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
4866:   if (!B->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name);
4867:   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);
4868:   MatCheckPreallocated(A,1);

4870:   (*A->ops->equal)(A,B,flg);
4871:   return(0);
4872: }

4874: /*@C
4875:    MatDiagonalScale - Scales a matrix on the left and right by diagonal
4876:    matrices that are stored as vectors.  Either of the two scaling
4877:    matrices can be NULL.

4879:    Collective on Mat

4881:    Input Parameters:
4882: +  mat - the matrix to be scaled
4883: .  l - the left scaling vector (or NULL)
4884: -  r - the right scaling vector (or NULL)

4886:    Notes:
4887:    MatDiagonalScale() computes A = LAR, where
4888:    L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
4889:    The L scales the rows of the matrix, the R scales the columns of the matrix.

4891:    Level: intermediate

4893:    Concepts: matrices^diagonal scaling
4894:    Concepts: diagonal scaling of matrices

4896: .seealso: MatScale()
4897: @*/
4898: PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r)
4899: {

4905:   if (!mat->ops->diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4908:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4909:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4910:   MatCheckPreallocated(mat,1);

4912:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
4913:   (*mat->ops->diagonalscale)(mat,l,r);
4914:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
4915:   PetscObjectStateIncrease((PetscObject)mat);
4916: #if defined(PETSC_HAVE_CUSP)
4917:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
4918:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
4919:   }
4920: #elif defined(PETSC_HAVE_VIENNACL)
4921:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
4922:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
4923:   }
4924: #elif defined(PETSC_HAVE_VECCUDA)
4925:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
4926:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
4927:   }
4928: #endif
4929:   return(0);
4930: }

4932: /*@
4933:     MatScale - Scales all elements of a matrix by a given number.

4935:     Logically Collective on Mat

4937:     Input Parameters:
4938: +   mat - the matrix to be scaled
4939: -   a  - the scaling value

4941:     Output Parameter:
4942: .   mat - the scaled matrix

4944:     Level: intermediate

4946:     Concepts: matrices^scaling all entries

4948: .seealso: MatDiagonalScale()
4949: @*/
4950: PetscErrorCode MatScale(Mat mat,PetscScalar a)
4951: {

4957:   if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4958:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4959:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4961:   MatCheckPreallocated(mat,1);

4963:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
4964:   if (a != (PetscScalar)1.0) {
4965:     (*mat->ops->scale)(mat,a);
4966:     PetscObjectStateIncrease((PetscObject)mat);
4967: #if defined(PETSC_HAVE_CUSP)
4968:     if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
4969:       mat->valid_GPU_matrix = PETSC_CUSP_CPU;
4970:     }
4971: #elif defined(PETSC_HAVE_VIENNACL)
4972:     if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
4973:       mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
4974:     }
4975: #elif defined(PETSC_HAVE_VECCUDA)
4976:     if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
4977:       mat->valid_GPU_matrix = PETSC_CUDA_CPU;
4978:     }
4979: #endif
4980:   }
4981:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
4982:   return(0);
4983: }

4985: /*@
4986:    MatNorm - Calculates various norms of a matrix.

4988:    Collective on Mat

4990:    Input Parameters:
4991: +  mat - the matrix
4992: -  type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY

4994:    Output Parameters:
4995: .  nrm - the resulting norm

4997:    Level: intermediate

4999:    Concepts: matrices^norm
5000:    Concepts: norm^of matrix
5001: @*/
5002: PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm)
5003: {


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");
5013:   if (!mat->ops->norm) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5014:   MatCheckPreallocated(mat,1);

5016:   (*mat->ops->norm)(mat,type,nrm);
5017:   return(0);
5018: }

5020: /*
5021:      This variable is used to prevent counting of MatAssemblyBegin() that
5022:    are called from within a MatAssemblyEnd().
5023: */
5024: static PetscInt MatAssemblyEnd_InUse = 0;
5025: /*@
5026:    MatAssemblyBegin - Begins assembling the matrix.  This routine should
5027:    be called after completing all calls to MatSetValues().

5029:    Collective on Mat

5031:    Input Parameters:
5032: +  mat - the matrix
5033: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5035:    Notes:
5036:    MatSetValues() generally caches the values.  The matrix is ready to
5037:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5038:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5039:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5040:    using the matrix.

5042:    ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the
5043:    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
5044:    a global collective operation requring all processes that share the matrix.

5046:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5047:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5048:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5050:    Level: beginner

5052:    Concepts: matrices^assembling

5054: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled()
5055: @*/
5056: PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type)
5057: {

5063:   MatCheckPreallocated(mat,1);
5064:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5065:   if (mat->assembled) {
5066:     mat->was_assembled = PETSC_TRUE;
5067:     mat->assembled     = PETSC_FALSE;
5068:   }
5069:   if (!MatAssemblyEnd_InUse) {
5070:     PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);
5071:     if (mat->ops->assemblybegin) {(*mat->ops->assemblybegin)(mat,type);}
5072:     PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);
5073:   } else if (mat->ops->assemblybegin) {
5074:     (*mat->ops->assemblybegin)(mat,type);
5075:   }
5076:   return(0);
5077: }

5079: /*@
5080:    MatAssembled - Indicates if a matrix has been assembled and is ready for
5081:      use; for example, in matrix-vector product.

5083:    Not Collective

5085:    Input Parameter:
5086: .  mat - the matrix

5088:    Output Parameter:
5089: .  assembled - PETSC_TRUE or PETSC_FALSE

5091:    Level: advanced

5093:    Concepts: matrices^assembled?

5095: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin()
5096: @*/
5097: PetscErrorCode MatAssembled(Mat mat,PetscBool  *assembled)
5098: {
5103:   *assembled = mat->assembled;
5104:   return(0);
5105: }

5107: /*@
5108:    MatAssemblyEnd - Completes assembling the matrix.  This routine should
5109:    be called after MatAssemblyBegin().

5111:    Collective on Mat

5113:    Input Parameters:
5114: +  mat - the matrix
5115: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5117:    Options Database Keys:
5118: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly()
5119: .  -mat_view ::ascii_info_detail - Prints more detailed info
5120: .  -mat_view - Prints matrix in ASCII format
5121: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
5122: .  -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX().
5123: .  -display <name> - Sets display name (default is host)
5124: .  -draw_pause <sec> - Sets number of seconds to pause after display
5125: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See Users-Manual: Chapter 12 Using MATLAB with PETSc )
5126: .  -viewer_socket_machine <machine> - Machine to use for socket
5127: .  -viewer_socket_port <port> - Port number to use for socket
5128: -  -mat_view binary:filename[:append] - Save matrix to file in binary format

5130:    Notes:
5131:    MatSetValues() generally caches the values.  The matrix is ready to
5132:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5133:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5134:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5135:    using the matrix.

5137:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5138:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5139:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5141:    Level: beginner

5143: .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), PetscDrawCreate(), MatView(), MatAssembled(), PetscViewerSocketOpen()
5144: @*/
5145: PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type)
5146: {
5147:   PetscErrorCode  ierr;
5148:   static PetscInt inassm = 0;
5149:   PetscBool       flg    = PETSC_FALSE;


5155:   inassm++;
5156:   MatAssemblyEnd_InUse++;
5157:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5158:     PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);
5159:     if (mat->ops->assemblyend) {
5160:       (*mat->ops->assemblyend)(mat,type);
5161:     }
5162:     PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);
5163:   } else if (mat->ops->assemblyend) {
5164:     (*mat->ops->assemblyend)(mat,type);
5165:   }

5167:   /* Flush assembly is not a true assembly */
5168:   if (type != MAT_FLUSH_ASSEMBLY) {
5169:     mat->assembled = PETSC_TRUE; mat->num_ass++;
5170:   }
5171:   mat->insertmode = NOT_SET_VALUES;
5172:   MatAssemblyEnd_InUse--;
5173:   PetscObjectStateIncrease((PetscObject)mat);
5174:   if (!mat->symmetric_eternal) {
5175:     mat->symmetric_set              = PETSC_FALSE;
5176:     mat->hermitian_set              = PETSC_FALSE;
5177:     mat->structurally_symmetric_set = PETSC_FALSE;
5178:   }
5179: #if defined(PETSC_HAVE_CUSP)
5180:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5181:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5182:   }
5183: #elif defined(PETSC_HAVE_VIENNACL)
5184:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5185:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5186:   }
5187: #elif defined(PETSC_HAVE_VECCUDA)
5188:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5189:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5190:   }
5191: #endif
5192:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5193:     MatViewFromOptions(mat,NULL,"-mat_view");

5195:     if (mat->checksymmetryonassembly) {
5196:       MatIsSymmetric(mat,mat->checksymmetrytol,&flg);
5197:       if (flg) {
5198:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5199:       } else {
5200:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is not symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5201:       }
5202:     }
5203:     if (mat->nullsp && mat->checknullspaceonassembly) {
5204:       MatNullSpaceTest(mat->nullsp,mat,NULL);
5205:     }
5206:   }
5207:   inassm--;
5208:   return(0);
5209: }

5211: /*@
5212:    MatSetOption - Sets a parameter option for a matrix. Some options
5213:    may be specific to certain storage formats.  Some options
5214:    determine how values will be inserted (or added). Sorted,
5215:    row-oriented input will generally assemble the fastest. The default
5216:    is row-oriented.

5218:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5220:    Input Parameters:
5221: +  mat - the matrix
5222: .  option - the option, one of those listed below (and possibly others),
5223: -  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5225:   Options Describing Matrix Structure:
5226: +    MAT_SPD - symmetric positive definite
5227: .    MAT_SYMMETRIC - symmetric in terms of both structure and value
5228: .    MAT_HERMITIAN - transpose is the complex conjugation
5229: .    MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure
5230: -    MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag
5231:                             you set to be kept with all future use of the matrix
5232:                             including after MatAssemblyBegin/End() which could
5233:                             potentially change the symmetry structure, i.e. you
5234:                             KNOW the matrix will ALWAYS have the property you set.


5237:    Options For Use with MatSetValues():
5238:    Insert a logically dense subblock, which can be
5239: .    MAT_ROW_ORIENTED - row-oriented (default)

5241:    Note these options reflect the data you pass in with MatSetValues(); it has
5242:    nothing to do with how the data is stored internally in the matrix
5243:    data structure.

5245:    When (re)assembling a matrix, we can restrict the input for
5246:    efficiency/debugging purposes.  These options include:
5247: +    MAT_NEW_NONZERO_LOCATIONS - additional insertions will be allowed if they generate a new nonzero (slow)
5248: .    MAT_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only)
5249: .    MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries
5250: .    MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry
5251: .    MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly
5252: .    MAT_NO_OFF_PROC_ENTRIES - you know each process will only set values for its own rows, will generate an error if
5253:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5254:         performance for very large process counts.
5255: -    MAT_SUBSET_OFF_PROC_ENTRIES - you know that the first assembly after setting this flag will set a superset
5256:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5257:         functions, instead sending only neighbor messages.

5259:    Notes:
5260:    Except for MAT_UNUSED_NONZERO_LOCATION_ERR and  MAT_ROW_ORIENTED all processes that share the matrix must pass the same value in flg!

5262:    Some options are relevant only for particular matrix types and
5263:    are thus ignored by others.  Other options are not supported by
5264:    certain matrix types and will generate an error message if set.

5266:    If using a Fortran 77 module to compute a matrix, one may need to
5267:    use the column-oriented option (or convert to the row-oriented
5268:    format).

5270:    MAT_NEW_NONZERO_LOCATIONS set to PETSC_FALSE indicates that any add or insertion
5271:    that would generate a new entry in the nonzero structure is instead
5272:    ignored.  Thus, if memory has not alredy been allocated for this particular
5273:    data, then the insertion is ignored. For dense matrices, in which
5274:    the entire array is allocated, no entries are ever ignored.
5275:    Set after the first MatAssemblyEnd(). If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5277:    MAT_NEW_NONZERO_LOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5278:    that would generate a new entry in the nonzero structure instead produces
5279:    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

5281:    MAT_NEW_NONZERO_ALLOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5282:    that would generate a new entry that has not been preallocated will
5283:    instead produce an error. (Currently supported for AIJ and BAIJ formats
5284:    only.) This is a useful flag when debugging matrix memory preallocation.
5285:    If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5287:    MAT_IGNORE_OFF_PROC_ENTRIES set to PETSC_TRUE indicates entries destined for
5288:    other processors should be dropped, rather than stashed.
5289:    This is useful if you know that the "owning" processor is also
5290:    always generating the correct matrix entries, so that PETSc need
5291:    not transfer duplicate entries generated on another processor.

5293:    MAT_USE_HASH_TABLE indicates that a hash table be used to improve the
5294:    searches during matrix assembly. When this flag is set, the hash table
5295:    is created during the first Matrix Assembly. This hash table is
5296:    used the next time through, during MatSetVaules()/MatSetVaulesBlocked()
5297:    to improve the searching of indices. MAT_NEW_NONZERO_LOCATIONS flag
5298:    should be used with MAT_USE_HASH_TABLE flag. This option is currently
5299:    supported by MATMPIBAIJ format only.

5301:    MAT_KEEP_NONZERO_PATTERN indicates when MatZeroRows() is called the zeroed entries
5302:    are kept in the nonzero structure

5304:    MAT_IGNORE_ZERO_ENTRIES - for AIJ/IS matrices this will stop zero values from creating
5305:    a zero location in the matrix

5307:    MAT_USE_INODES - indicates using inode version of the code - works with AIJ matrix types

5309:    MAT_NO_OFF_PROC_ZERO_ROWS - you know each process will only zero its own rows. This avoids all reductions in the
5310:         zero row routines and thus improves performance for very large process counts.

5312:    MAT_IGNORE_LOWER_TRIANGULAR - For SBAIJ matrices will ignore any insertions you make in the lower triangular
5313:         part of the matrix (since they should match the upper triangular part).

5315:    Notes: Can only be called after MatSetSizes() and MatSetType() have been set.

5317:    Level: intermediate

5319:    Concepts: matrices^setting options

5321: .seealso:  MatOption, Mat

5323: @*/
5324: PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg)
5325: {

5331:   if (op > 0) {
5334:   }

5336:   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);
5337:   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()");

5339:   switch (op) {
5340:   case MAT_NO_OFF_PROC_ENTRIES:
5341:     mat->nooffprocentries = flg;
5342:     return(0);
5343:     break;
5344:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5345:     mat->subsetoffprocentries = flg;
5346:     return(0);
5347:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5348:     mat->nooffproczerorows = flg;
5349:     return(0);
5350:     break;
5351:   case MAT_SPD:
5352:     mat->spd_set = PETSC_TRUE;
5353:     mat->spd     = flg;
5354:     if (flg) {
5355:       mat->symmetric                  = PETSC_TRUE;
5356:       mat->structurally_symmetric     = PETSC_TRUE;
5357:       mat->symmetric_set              = PETSC_TRUE;
5358:       mat->structurally_symmetric_set = PETSC_TRUE;
5359:     }
5360:     break;
5361:   case MAT_SYMMETRIC:
5362:     mat->symmetric = flg;
5363:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5364:     mat->symmetric_set              = PETSC_TRUE;
5365:     mat->structurally_symmetric_set = flg;
5366:     break;
5367:   case MAT_HERMITIAN:
5368:     mat->hermitian = flg;
5369:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5370:     mat->hermitian_set              = PETSC_TRUE;
5371:     mat->structurally_symmetric_set = flg;
5372:     break;
5373:   case MAT_STRUCTURALLY_SYMMETRIC:
5374:     mat->structurally_symmetric     = flg;
5375:     mat->structurally_symmetric_set = PETSC_TRUE;
5376:     break;
5377:   case MAT_SYMMETRY_ETERNAL:
5378:     mat->symmetric_eternal = flg;
5379:     break;
5380:   default:
5381:     break;
5382:   }
5383:   if (mat->ops->setoption) {
5384:     (*mat->ops->setoption)(mat,op,flg);
5385:   }
5386:   return(0);
5387: }

5389: /*@
5390:    MatGetOption - Gets a parameter option that has been set for a matrix.

5392:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5394:    Input Parameters:
5395: +  mat - the matrix
5396: -  option - the option, this only responds to certain options, check the code for which ones

5398:    Output Parameter:
5399: .  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5401:     Notes: Can only be called after MatSetSizes() and MatSetType() have been set.

5403:    Level: intermediate

5405:    Concepts: matrices^setting options

5407: .seealso:  MatOption, MatSetOption()

5409: @*/
5410: PetscErrorCode MatGetOption(Mat mat,MatOption op,PetscBool *flg)
5411: {

5416:   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);
5417:   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()");

5419:   switch (op) {
5420:   case MAT_NO_OFF_PROC_ENTRIES:
5421:     *flg = mat->nooffprocentries;
5422:     break;
5423:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5424:     *flg = mat->nooffproczerorows;
5425:     break;
5426:   case MAT_SYMMETRIC:
5427:     *flg = mat->symmetric;
5428:     break;
5429:   case MAT_HERMITIAN:
5430:     *flg = mat->hermitian;
5431:     break;
5432:   case MAT_STRUCTURALLY_SYMMETRIC:
5433:     *flg = mat->structurally_symmetric;
5434:     break;
5435:   case MAT_SYMMETRY_ETERNAL:
5436:     *flg = mat->symmetric_eternal;
5437:     break;
5438:   default:
5439:     break;
5440:   }
5441:   return(0);
5442: }

5444: /*@
5445:    MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
5446:    this routine retains the old nonzero structure.

5448:    Logically Collective on Mat

5450:    Input Parameters:
5451: .  mat - the matrix

5453:    Level: intermediate

5455:    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.
5456:    See the Performance chapter of the users manual for information on preallocating matrices.

5458:    Concepts: matrices^zeroing

5460: .seealso: MatZeroRows()
5461: @*/
5462: PetscErrorCode MatZeroEntries(Mat mat)
5463: {

5469:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5470:   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");
5471:   if (!mat->ops->zeroentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5472:   MatCheckPreallocated(mat,1);

5474:   PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);
5475:   (*mat->ops->zeroentries)(mat);
5476:   PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);
5477:   PetscObjectStateIncrease((PetscObject)mat);
5478: #if defined(PETSC_HAVE_CUSP)
5479:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5480:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5481:   }
5482: #elif defined(PETSC_HAVE_VIENNACL)
5483:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5484:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5485:   }
5486: #elif defined(PETSC_HAVE_VECCUDA)
5487:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5488:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5489:   }
5490: #endif
5491:   return(0);
5492: }

5494: /*@C
5495:    MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
5496:    of a set of rows and columns of a matrix.

5498:    Collective on Mat

5500:    Input Parameters:
5501: +  mat - the matrix
5502: .  numRows - the number of rows to remove
5503: .  rows - the global row indices
5504: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5505: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5506: -  b - optional vector of right hand side, that will be adjusted by provided solution

5508:    Notes:
5509:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5511:    The user can set a value in the diagonal entry (or for the AIJ and
5512:    row formats can optionally remove the main diagonal entry from the
5513:    nonzero structure as well, by passing 0.0 as the final argument).

5515:    For the parallel case, all processes that share the matrix (i.e.,
5516:    those in the communicator used for matrix creation) MUST call this
5517:    routine, regardless of whether any rows being zeroed are owned by
5518:    them.

5520:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5521:    list only rows local to itself).

5523:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5525:    Level: intermediate

5527:    Concepts: matrices^zeroing rows

5529: .seealso: MatZeroRowsIS(), MatZeroRows(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5530:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5531: @*/
5532: PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5533: {

5540:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5541:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5542:   if (!mat->ops->zerorowscolumns) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5543:   MatCheckPreallocated(mat,1);

5545:   (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);
5546:   MatViewFromOptions(mat,NULL,"-mat_view");
5547:   PetscObjectStateIncrease((PetscObject)mat);
5548: #if defined(PETSC_HAVE_CUSP)
5549:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5550:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5551:   }
5552: #elif defined(PETSC_HAVE_VIENNACL)
5553:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5554:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5555:   }
5556: #elif defined(PETSC_HAVE_VECCUDA)
5557:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5558:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5559:   }
5560: #endif
5561:   return(0);
5562: }

5564: /*@C
5565:    MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
5566:    of a set of rows and columns of a matrix.

5568:    Collective on Mat

5570:    Input Parameters:
5571: +  mat - the matrix
5572: .  is - the rows to zero
5573: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5574: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5575: -  b - optional vector of right hand side, that will be adjusted by provided solution

5577:    Notes:
5578:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5580:    The user can set a value in the diagonal entry (or for the AIJ and
5581:    row formats can optionally remove the main diagonal entry from the
5582:    nonzero structure as well, by passing 0.0 as the final argument).

5584:    For the parallel case, all processes that share the matrix (i.e.,
5585:    those in the communicator used for matrix creation) MUST call this
5586:    routine, regardless of whether any rows being zeroed are owned by
5587:    them.

5589:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5590:    list only rows local to itself).

5592:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5594:    Level: intermediate

5596:    Concepts: matrices^zeroing rows

5598: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5599:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRows(), MatZeroRowsColumnsStencil()
5600: @*/
5601: PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5602: {
5604:   PetscInt       numRows;
5605:   const PetscInt *rows;

5612:   ISGetLocalSize(is,&numRows);
5613:   ISGetIndices(is,&rows);
5614:   MatZeroRowsColumns(mat,numRows,rows,diag,x,b);
5615:   ISRestoreIndices(is,&rows);
5616:   return(0);
5617: }

5619: /*@C
5620:    MatZeroRows - Zeros all entries (except possibly the main diagonal)
5621:    of a set of rows of a matrix.

5623:    Collective on Mat

5625:    Input Parameters:
5626: +  mat - the matrix
5627: .  numRows - the number of rows to remove
5628: .  rows - the global row indices
5629: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5630: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5631: -  b - optional vector of right hand side, that will be adjusted by provided solution

5633:    Notes:
5634:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5635:    but does not release memory.  For the dense and block diagonal
5636:    formats this does not alter the nonzero structure.

5638:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5639:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5640:    merely zeroed.

5642:    The user can set a value in the diagonal entry (or for the AIJ and
5643:    row formats can optionally remove the main diagonal entry from the
5644:    nonzero structure as well, by passing 0.0 as the final argument).

5646:    For the parallel case, all processes that share the matrix (i.e.,
5647:    those in the communicator used for matrix creation) MUST call this
5648:    routine, regardless of whether any rows being zeroed are owned by
5649:    them.

5651:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5652:    list only rows local to itself).

5654:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5655:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5657:    Level: intermediate

5659:    Concepts: matrices^zeroing rows

5661: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5662:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5663: @*/
5664: PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5665: {

5672:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5673:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5674:   if (!mat->ops->zerorows) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5675:   MatCheckPreallocated(mat,1);

5677:   (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);
5678:   MatViewFromOptions(mat,NULL,"-mat_view");
5679:   PetscObjectStateIncrease((PetscObject)mat);
5680: #if defined(PETSC_HAVE_CUSP)
5681:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
5682:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
5683:   }
5684: #elif defined(PETSC_HAVE_VIENNACL)
5685:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
5686:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
5687:   }
5688: #elif defined(PETSC_HAVE_VECCUDA)
5689:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
5690:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
5691:   }
5692: #endif
5693:   return(0);
5694: }

5696: /*@C
5697:    MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
5698:    of a set of rows of a matrix.

5700:    Collective on Mat

5702:    Input Parameters:
5703: +  mat - the matrix
5704: .  is - index set of rows to remove
5705: .  diag - value put in all diagonals of eliminated rows
5706: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5707: -  b - optional vector of right hand side, that will be adjusted by provided solution

5709:    Notes:
5710:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5711:    but does not release memory.  For the dense and block diagonal
5712:    formats this does not alter the nonzero structure.

5714:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5715:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5716:    merely zeroed.

5718:    The user can set a value in the diagonal entry (or for the AIJ and
5719:    row formats can optionally remove the main diagonal entry from the
5720:    nonzero structure as well, by passing 0.0 as the final argument).

5722:    For the parallel case, all processes that share the matrix (i.e.,
5723:    those in the communicator used for matrix creation) MUST call this
5724:    routine, regardless of whether any rows being zeroed are owned by
5725:    them.

5727:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5728:    list only rows local to itself).

5730:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5731:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5733:    Level: intermediate

5735:    Concepts: matrices^zeroing rows

5737: .seealso: MatZeroRows(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5738:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5739: @*/
5740: PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5741: {
5742:   PetscInt       numRows;
5743:   const PetscInt *rows;

5750:   ISGetLocalSize(is,&numRows);
5751:   ISGetIndices(is,&rows);
5752:   MatZeroRows(mat,numRows,rows,diag,x,b);
5753:   ISRestoreIndices(is,&rows);
5754:   return(0);
5755: }

5757: /*@C
5758:    MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
5759:    of a set of rows of a matrix. These rows must be local to the process.

5761:    Collective on Mat

5763:    Input Parameters:
5764: +  mat - the matrix
5765: .  numRows - the number of rows to remove
5766: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5767: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5768: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5769: -  b - optional vector of right hand side, that will be adjusted by provided solution

5771:    Notes:
5772:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5773:    but does not release memory.  For the dense and block diagonal
5774:    formats this does not alter the nonzero structure.

5776:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5777:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5778:    merely zeroed.

5780:    The user can set a value in the diagonal entry (or for the AIJ and
5781:    row formats can optionally remove the main diagonal entry from the
5782:    nonzero structure as well, by passing 0.0 as the final argument).

5784:    For the parallel case, all processes that share the matrix (i.e.,
5785:    those in the communicator used for matrix creation) MUST call this
5786:    routine, regardless of whether any rows being zeroed are owned by
5787:    them.

5789:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5790:    list only rows local to itself).

5792:    The grid coordinates are across the entire grid, not just the local portion

5794:    In Fortran idxm and idxn should be declared as
5795: $     MatStencil idxm(4,m)
5796:    and the values inserted using
5797: $    idxm(MatStencil_i,1) = i
5798: $    idxm(MatStencil_j,1) = j
5799: $    idxm(MatStencil_k,1) = k
5800: $    idxm(MatStencil_c,1) = c
5801:    etc

5803:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5804:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5805:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5806:    DM_BOUNDARY_PERIODIC boundary type.

5808:    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
5809:    a single value per point) you can skip filling those indices.

5811:    Level: intermediate

5813:    Concepts: matrices^zeroing rows

5815: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsl(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5816:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5817: @*/
5818: PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5819: {
5820:   PetscInt       dim     = mat->stencil.dim;
5821:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5822:   PetscInt       *dims   = mat->stencil.dims+1;
5823:   PetscInt       *starts = mat->stencil.starts;
5824:   PetscInt       *dxm    = (PetscInt*) rows;
5825:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


5833:   PetscMalloc1(numRows, &jdxm);
5834:   for (i = 0; i < numRows; ++i) {
5835:     /* Skip unused dimensions (they are ordered k, j, i, c) */
5836:     for (j = 0; j < 3-sdim; ++j) dxm++;
5837:     /* Local index in X dir */
5838:     tmp = *dxm++ - starts[0];
5839:     /* Loop over remaining dimensions */
5840:     for (j = 0; j < dim-1; ++j) {
5841:       /* If nonlocal, set index to be negative */
5842:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
5843:       /* Update local index */
5844:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
5845:     }
5846:     /* Skip component slot if necessary */
5847:     if (mat->stencil.noc) dxm++;
5848:     /* Local row number */
5849:     if (tmp >= 0) {
5850:       jdxm[numNewRows++] = tmp;
5851:     }
5852:   }
5853:   MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);
5854:   PetscFree(jdxm);
5855:   return(0);
5856: }

5858: /*@C
5859:    MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
5860:    of a set of rows and columns of a matrix.

5862:    Collective on Mat

5864:    Input Parameters:
5865: +  mat - the matrix
5866: .  numRows - the number of rows/columns to remove
5867: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5868: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5869: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5870: -  b - optional vector of right hand side, that will be adjusted by provided solution

5872:    Notes:
5873:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5874:    but does not release memory.  For the dense and block diagonal
5875:    formats this does not alter the nonzero structure.

5877:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5878:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5879:    merely zeroed.

5881:    The user can set a value in the diagonal entry (or for the AIJ and
5882:    row formats can optionally remove the main diagonal entry from the
5883:    nonzero structure as well, by passing 0.0 as the final argument).

5885:    For the parallel case, all processes that share the matrix (i.e.,
5886:    those in the communicator used for matrix creation) MUST call this
5887:    routine, regardless of whether any rows being zeroed are owned by
5888:    them.

5890:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5891:    list only rows local to itself, but the row/column numbers are given in local numbering).

5893:    The grid coordinates are across the entire grid, not just the local portion

5895:    In Fortran idxm and idxn should be declared as
5896: $     MatStencil idxm(4,m)
5897:    and the values inserted using
5898: $    idxm(MatStencil_i,1) = i
5899: $    idxm(MatStencil_j,1) = j
5900: $    idxm(MatStencil_k,1) = k
5901: $    idxm(MatStencil_c,1) = c
5902:    etc

5904:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5905:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5906:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5907:    DM_BOUNDARY_PERIODIC boundary type.

5909:    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
5910:    a single value per point) you can skip filling those indices.

5912:    Level: intermediate

5914:    Concepts: matrices^zeroing rows

5916: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5917:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRows()
5918: @*/
5919: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5920: {
5921:   PetscInt       dim     = mat->stencil.dim;
5922:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5923:   PetscInt       *dims   = mat->stencil.dims+1;
5924:   PetscInt       *starts = mat->stencil.starts;
5925:   PetscInt       *dxm    = (PetscInt*) rows;
5926:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


5934:   PetscMalloc1(numRows, &jdxm);
5935:   for (i = 0; i < numRows; ++i) {
5936:     /* Skip unused dimensions (they are ordered k, j, i, c) */
5937:     for (j = 0; j < 3-sdim; ++j) dxm++;
5938:     /* Local index in X dir */
5939:     tmp = *dxm++ - starts[0];
5940:     /* Loop over remaining dimensions */
5941:     for (j = 0; j < dim-1; ++j) {
5942:       /* If nonlocal, set index to be negative */
5943:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
5944:       /* Update local index */
5945:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
5946:     }
5947:     /* Skip component slot if necessary */
5948:     if (mat->stencil.noc) dxm++;
5949:     /* Local row number */
5950:     if (tmp >= 0) {
5951:       jdxm[numNewRows++] = tmp;
5952:     }
5953:   }
5954:   MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);
5955:   PetscFree(jdxm);
5956:   return(0);
5957: }

5959: /*@C
5960:    MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
5961:    of a set of rows of a matrix; using local numbering of rows.

5963:    Collective on Mat

5965:    Input Parameters:
5966: +  mat - the matrix
5967: .  numRows - the number of rows to remove
5968: .  rows - the global row indices
5969: .  diag - value put in all diagonals of eliminated rows
5970: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5971: -  b - optional vector of right hand side, that will be adjusted by provided solution

5973:    Notes:
5974:    Before calling MatZeroRowsLocal(), the user must first set the
5975:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

5977:    For the AIJ matrix formats this removes the old nonzero structure,
5978:    but does not release memory.  For the dense and block diagonal
5979:    formats this does not alter the nonzero structure.

5981:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5982:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5983:    merely zeroed.

5985:    The user can set a value in the diagonal entry (or for the AIJ and
5986:    row formats can optionally remove the main diagonal entry from the
5987:    nonzero structure as well, by passing 0.0 as the final argument).

5989:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5990:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5992:    Level: intermediate

5994:    Concepts: matrices^zeroing

5996: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRows(), MatSetOption(),
5997:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5998: @*/
5999: PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6000: {

6007:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6008:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6009:   MatCheckPreallocated(mat,1);

6011:   if (mat->ops->zerorowslocal) {
6012:     (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);
6013:   } else {
6014:     IS             is, newis;
6015:     const PetscInt *newRows;

6017:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6018:     ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6019:     ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);
6020:     ISGetIndices(newis,&newRows);
6021:     (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);
6022:     ISRestoreIndices(newis,&newRows);
6023:     ISDestroy(&newis);
6024:     ISDestroy(&is);
6025:   }
6026:   PetscObjectStateIncrease((PetscObject)mat);
6027: #if defined(PETSC_HAVE_CUSP)
6028:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
6029:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
6030:   }
6031: #elif defined(PETSC_HAVE_VIENNACL)
6032:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
6033:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
6034:   }
6035: #elif defined(PETSC_HAVE_VECCUDA)
6036:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
6037:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
6038:   }
6039: #endif
6040:   return(0);
6041: }

6043: /*@C
6044:    MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6045:    of a set of rows of a matrix; using local numbering of rows.

6047:    Collective on Mat

6049:    Input Parameters:
6050: +  mat - the matrix
6051: .  is - index set of rows to remove
6052: .  diag - value put in all diagonals of eliminated rows
6053: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6054: -  b - optional vector of right hand side, that will be adjusted by provided solution

6056:    Notes:
6057:    Before calling MatZeroRowsLocalIS(), the user must first set the
6058:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6060:    For the AIJ matrix formats this removes the old nonzero structure,
6061:    but does not release memory.  For the dense and block diagonal
6062:    formats this does not alter the nonzero structure.

6064:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6065:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6066:    merely zeroed.

6068:    The user can set a value in the diagonal entry (or for the AIJ and
6069:    row formats can optionally remove the main diagonal entry from the
6070:    nonzero structure as well, by passing 0.0 as the final argument).

6072:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6073:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6075:    Level: intermediate

6077:    Concepts: matrices^zeroing

6079: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6080:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6081: @*/
6082: PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6083: {
6085:   PetscInt       numRows;
6086:   const PetscInt *rows;

6092:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6093:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6094:   MatCheckPreallocated(mat,1);

6096:   ISGetLocalSize(is,&numRows);
6097:   ISGetIndices(is,&rows);
6098:   MatZeroRowsLocal(mat,numRows,rows,diag,x,b);
6099:   ISRestoreIndices(is,&rows);
6100:   return(0);
6101: }

6103: /*@C
6104:    MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6105:    of a set of rows and columns of a matrix; using local numbering of rows.

6107:    Collective on Mat

6109:    Input Parameters:
6110: +  mat - the matrix
6111: .  numRows - the number of rows to remove
6112: .  rows - the global row indices
6113: .  diag - value put in all diagonals of eliminated rows
6114: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6115: -  b - optional vector of right hand side, that will be adjusted by provided solution

6117:    Notes:
6118:    Before calling MatZeroRowsColumnsLocal(), the user must first set the
6119:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6121:    The user can set a value in the diagonal entry (or for the AIJ and
6122:    row formats can optionally remove the main diagonal entry from the
6123:    nonzero structure as well, by passing 0.0 as the final argument).

6125:    Level: intermediate

6127:    Concepts: matrices^zeroing

6129: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6130:           MatZeroRows(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6131: @*/
6132: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6133: {
6135:   IS             is, newis;
6136:   const PetscInt *newRows;

6142:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6143:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6144:   MatCheckPreallocated(mat,1);

6146:   if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6147:   ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6148:   ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);
6149:   ISGetIndices(newis,&newRows);
6150:   (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);
6151:   ISRestoreIndices(newis,&newRows);
6152:   ISDestroy(&newis);
6153:   ISDestroy(&is);
6154:   PetscObjectStateIncrease((PetscObject)mat);
6155: #if defined(PETSC_HAVE_CUSP)
6156:   if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) {
6157:     mat->valid_GPU_matrix = PETSC_CUSP_CPU;
6158:   }
6159: #elif defined(PETSC_HAVE_VIENNACL)
6160:   if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) {
6161:     mat->valid_GPU_matrix = PETSC_VIENNACL_CPU;
6162:   }
6163: #elif defined(PETSC_HAVE_VECCUDA)
6164:   if (mat->valid_GPU_matrix != PETSC_CUDA_UNALLOCATED) {
6165:     mat->valid_GPU_matrix = PETSC_CUDA_CPU;
6166:   }
6167: #endif
6168:   return(0);
6169: }

6171: /*@C
6172:    MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6173:    of a set of rows and columns of a matrix; using local numbering of rows.

6175:    Collective on Mat

6177:    Input Parameters:
6178: +  mat - the matrix
6179: .  is - index set of rows to remove
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 MatZeroRowsColumnsLocalIS(), 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:           MatZeroRowsColumnsLocal(), MatZeroRows(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6198: @*/
6199: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6200: {
6202:   PetscInt       numRows;
6203:   const PetscInt *rows;

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:   ISGetLocalSize(is,&numRows);
6214:   ISGetIndices(is,&rows);
6215:   MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);
6216:   ISRestoreIndices(is,&rows);
6217:   return(0);
6218: }

6220: /*@C
6221:    MatGetSize - Returns the numbers of rows and columns in a matrix.

6223:    Not Collective

6225:    Input Parameter:
6226: .  mat - the matrix

6228:    Output Parameters:
6229: +  m - the number of global rows
6230: -  n - the number of global columns

6232:    Note: both output parameters can be NULL on input.

6234:    Level: beginner

6236:    Concepts: matrices^size

6238: .seealso: MatGetLocalSize()
6239: @*/
6240: PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n)
6241: {
6244:   if (m) *m = mat->rmap->N;
6245:   if (n) *n = mat->cmap->N;
6246:   return(0);
6247: }

6249: /*@C
6250:    MatGetLocalSize - Returns the number of rows and columns in a matrix
6251:    stored locally.  This information may be implementation dependent, so
6252:    use with care.

6254:    Not Collective

6256:    Input Parameters:
6257: .  mat - the matrix

6259:    Output Parameters:
6260: +  m - the number of local rows
6261: -  n - the number of local columns

6263:    Note: both output parameters can be NULL on input.

6265:    Level: beginner

6267:    Concepts: matrices^local size

6269: .seealso: MatGetSize()
6270: @*/
6271: PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n)
6272: {
6277:   if (m) *m = mat->rmap->n;
6278:   if (n) *n = mat->cmap->n;
6279:   return(0);
6280: }

6282: /*@
6283:    MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6284:    this processor. (The columns of the "diagonal block")

6286:    Not Collective, unless matrix has not been allocated, then collective on Mat

6288:    Input Parameters:
6289: .  mat - the matrix

6291:    Output Parameters:
6292: +  m - the global index of the first local column
6293: -  n - one more than the global index of the last local column

6295:    Notes: both output parameters can be NULL on input.

6297:    Level: developer

6299:    Concepts: matrices^column ownership

6301: .seealso:  MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn()

6303: @*/
6304: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n)
6305: {
6311:   MatCheckPreallocated(mat,1);
6312:   if (m) *m = mat->cmap->rstart;
6313:   if (n) *n = mat->cmap->rend;
6314:   return(0);
6315: }

6317: /*@
6318:    MatGetOwnershipRange - Returns the range of matrix rows owned by
6319:    this processor, assuming that the matrix is laid out with the first
6320:    n1 rows on the first processor, the next n2 rows on the second, etc.
6321:    For certain parallel layouts this range may not be well defined.

6323:    Not Collective

6325:    Input Parameters:
6326: .  mat - the matrix

6328:    Output Parameters:
6329: +  m - the global index of the first local row
6330: -  n - one more than the global index of the last local row

6332:    Note: Both output parameters can be NULL on input.
6333: $  This function requires that the matrix be preallocated. If you have not preallocated, consider using
6334: $    PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N)
6335: $  and then MPI_Scan() to calculate prefix sums of the local sizes.

6337:    Level: beginner

6339:    Concepts: matrices^row ownership

6341: .seealso:   MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock()

6343: @*/
6344: PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n)
6345: {
6351:   MatCheckPreallocated(mat,1);
6352:   if (m) *m = mat->rmap->rstart;
6353:   if (n) *n = mat->rmap->rend;
6354:   return(0);
6355: }

6357: /*@C
6358:    MatGetOwnershipRanges - Returns the range of matrix rows owned by
6359:    each process

6361:    Not Collective, unless matrix has not been allocated, then collective on Mat

6363:    Input Parameters:
6364: .  mat - the matrix

6366:    Output Parameters:
6367: .  ranges - start of each processors portion plus one more than the total length at the end

6369:    Level: beginner

6371:    Concepts: matrices^row ownership

6373: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn()

6375: @*/
6376: PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges)
6377: {

6383:   MatCheckPreallocated(mat,1);
6384:   PetscLayoutGetRanges(mat->rmap,ranges);
6385:   return(0);
6386: }

6388: /*@C
6389:    MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6390:    this processor. (The columns of the "diagonal blocks" for each process)

6392:    Not Collective, unless matrix has not been allocated, then collective on Mat

6394:    Input Parameters:
6395: .  mat - the matrix

6397:    Output Parameters:
6398: .  ranges - start of each processors portion plus one more then the total length at the end

6400:    Level: beginner

6402:    Concepts: matrices^column ownership

6404: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges()

6406: @*/
6407: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges)
6408: {

6414:   MatCheckPreallocated(mat,1);
6415:   PetscLayoutGetRanges(mat->cmap,ranges);
6416:   return(0);
6417: }

6419: /*@C
6420:    MatGetOwnershipIS - Get row and column ownership as index sets

6422:    Not Collective

6424:    Input Arguments:
6425: .  A - matrix of type Elemental

6427:    Output Arguments:
6428: +  rows - rows in which this process owns elements
6429: .  cols - columns in which this process owns elements

6431:    Level: intermediate

6433: .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL, MatSetValues()
6434: @*/
6435: PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols)
6436: {
6437:   PetscErrorCode ierr,(*f)(Mat,IS*,IS*);

6440:   MatCheckPreallocated(A,1);
6441:   PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",&f);
6442:   if (f) {
6443:     (*f)(A,rows,cols);
6444:   } else {   /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6445:     if (rows) {ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);}
6446:     if (cols) {ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);}
6447:   }
6448:   return(0);
6449: }

6451: /*@C
6452:    MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix.
6453:    Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric()
6454:    to complete the factorization.

6456:    Collective on Mat

6458:    Input Parameters:
6459: +  mat - the matrix
6460: .  row - row permutation
6461: .  column - column permutation
6462: -  info - structure containing
6463: $      levels - number of levels of fill.
6464: $      expected fill - as ratio of original fill.
6465: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6466:                 missing diagonal entries)

6468:    Output Parameters:
6469: .  fact - new matrix that has been symbolically factored

6471:    Notes: See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

6473:    Most users should employ the simplified KSP interface for linear solvers
6474:    instead of working directly with matrix algebra routines such as this.
6475:    See, e.g., KSPCreate().

6477:    Level: developer

6479:   Concepts: matrices^symbolic LU factorization
6480:   Concepts: matrices^factorization
6481:   Concepts: LU^symbolic factorization

6483: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()
6484:           MatGetOrdering(), MatFactorInfo

6486:     Developer Note: fortran interface is not autogenerated as the f90
6487:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6489: @*/
6490: PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
6491: {

6501:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels);
6502:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6503:   if (!(fact)->ops->ilufactorsymbolic) {
6504:     const MatSolverPackage spackage;
6505:     MatFactorGetSolverPackage(fact,&spackage);
6506:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage);
6507:   }
6508:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6509:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6510:   MatCheckPreallocated(mat,2);

6512:   PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);
6513:   (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);
6514:   PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);
6515:   return(0);
6516: }

6518: /*@C
6519:    MatICCFactorSymbolic - Performs symbolic incomplete
6520:    Cholesky factorization for a symmetric matrix.  Use
6521:    MatCholeskyFactorNumeric() to complete the factorization.

6523:    Collective on Mat

6525:    Input Parameters:
6526: +  mat - the matrix
6527: .  perm - row and column permutation
6528: -  info - structure containing
6529: $      levels - number of levels of fill.
6530: $      expected fill - as ratio of original fill.

6532:    Output Parameter:
6533: .  fact - the factored matrix

6535:    Notes:
6536:    Most users should employ the KSP interface for linear solvers
6537:    instead of working directly with matrix algebra routines such as this.
6538:    See, e.g., KSPCreate().

6540:    Level: developer

6542:   Concepts: matrices^symbolic incomplete Cholesky factorization
6543:   Concepts: matrices^factorization
6544:   Concepts: Cholsky^symbolic factorization

6546: .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

6548:     Developer Note: fortran interface is not autogenerated as the f90
6549:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6551: @*/
6552: PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
6553: {

6562:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6563:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels);
6564:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6565:   if (!(fact)->ops->iccfactorsymbolic) {
6566:     const MatSolverPackage spackage;
6567:     MatFactorGetSolverPackage(fact,&spackage);
6568:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage);
6569:   }
6570:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6571:   MatCheckPreallocated(mat,2);

6573:   PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);
6574:   (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);
6575:   PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);
6576:   return(0);
6577: }

6579: /*@C
6580:    MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
6581:    points to an array of valid matrices, they may be reused to store the new
6582:    submatrices.

6584:    Collective on Mat

6586:    Input Parameters:
6587: +  mat - the matrix
6588: .  n   - the number of submatrixes to be extracted (on this processor, may be zero)
6589: .  irow, icol - index sets of rows and columns to extract
6590: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6592:    Output Parameter:
6593: .  submat - the array of submatrices

6595:    Notes:
6596:    MatCreateSubMatrices() can extract ONLY sequential submatrices
6597:    (from both sequential and parallel matrices). Use MatCreateSubMatrix()
6598:    to extract a parallel submatrix.

6600:    Some matrix types place restrictions on the row and column
6601:    indices, such as that they be sorted or that they be equal to each other.

6603:    The index sets may not have duplicate entries.

6605:    When extracting submatrices from a parallel matrix, each processor can
6606:    form a different submatrix by setting the rows and columns of its
6607:    individual index sets according to the local submatrix desired.

6609:    When finished using the submatrices, the user should destroy
6610:    them with MatDestroyMatrices().

6612:    MAT_REUSE_MATRIX can only be used when the nonzero structure of the
6613:    original matrix has not changed from that last call to MatCreateSubMatrices().

6615:    This routine creates the matrices in submat; you should NOT create them before
6616:    calling it. It also allocates the array of matrix pointers submat.

6618:    For BAIJ matrices the index sets must respect the block structure, that is if they
6619:    request one row/column in a block, they must request all rows/columns that are in
6620:    that block. For example, if the block size is 2 you cannot request just row 0 and
6621:    column 0.

6623:    Fortran Note:
6624:    The Fortran interface is slightly different from that given below; it
6625:    requires one to pass in  as submat a Mat (integer) array of size at least m.

6627:    Level: advanced

6629:    Concepts: matrices^accessing submatrices
6630:    Concepts: submatrices

6632: .seealso: MatDestroyMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6633: @*/
6634: PetscErrorCode MatCreateSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6635: {
6637:   PetscInt       i;
6638:   PetscBool      eq;

6643:   if (n) {
6648:   }
6650:   if (n && scall == MAT_REUSE_MATRIX) {
6653:   }
6654:   if (!mat->ops->createsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6655:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6656:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6657:   MatCheckPreallocated(mat,1);

6659:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6660:   (*mat->ops->createsubmatrices)(mat,n,irow,icol,scall,submat);
6661:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6662:   for (i=0; i<n; i++) {
6663:     (*submat)[i]->factortype = MAT_FACTOR_NONE;  /* in case in place factorization was previously done on submatrix */
6664:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6665:       ISEqual(irow[i],icol[i],&eq);
6666:       if (eq) {
6667:         if (mat->symmetric) {
6668:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6669:         } else if (mat->hermitian) {
6670:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6671:         } else if (mat->structurally_symmetric) {
6672:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6673:         }
6674:       }
6675:     }
6676:   }
6677:   return(0);
6678: }

6680: /*@C
6681:    MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of IS that may live on subcomms).

6683:    Collective on Mat

6685:    Input Parameters:
6686: +  mat - the matrix
6687: .  n   - the number of submatrixes to be extracted
6688: .  irow, icol - index sets of rows and columns to extract
6689: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6691:    Output Parameter:
6692: .  submat - the array of submatrices

6694:    Level: advanced

6696:    Concepts: matrices^accessing submatrices
6697:    Concepts: submatrices

6699: .seealso: MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6700: @*/
6701: PetscErrorCode MatCreateSubMatricesMPI(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->createsubmatricesmpi) 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->createsubmatricesmpi)(mat,n,irow,icol,scall,submat);
6728:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6729:   for (i=0; i<n; i++) {
6730:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6731:       ISEqual(irow[i],icol[i],&eq);
6732:       if (eq) {
6733:         if (mat->symmetric) {
6734:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6735:         } else if (mat->hermitian) {
6736:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6737:         } else if (mat->structurally_symmetric) {
6738:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6739:         }
6740:       }
6741:     }
6742:   }
6743:   return(0);
6744: }

6746: /*@C
6747:    MatDestroyMatrices - Destroys an array of matrices.

6749:    Collective on Mat

6751:    Input Parameters:
6752: +  n - the number of local matrices
6753: -  mat - the matrices (note that this is a pointer to the array of matrices)

6755:    Level: advanced

6757:     Notes: Frees not only the matrices, but also the array that contains the matrices
6758:            In Fortran will not free the array.

6760: .seealso: MatCreateSubMatrices() MatDestroySubMatrices()
6761: @*/
6762: PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[])
6763: {
6765:   PetscInt       i;

6768:   if (!*mat) return(0);
6769:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6772:   for (i=0; i<n; i++) {
6773:     MatDestroy(&(*mat)[i]);
6774:   }

6776:   /* memory is allocated even if n = 0 */
6777:   PetscFree(*mat);
6778:   return(0);
6779: }

6781: /*@C
6782:    MatDestroySubMatrices - Destroys a set of matrices obtained with MatCreateSubMatrices().

6784:    Collective on Mat

6786:    Input Parameters:
6787: +  n - the number of local matrices
6788: -  mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling
6789:                        sequence of MatCreateSubMatrices())

6791:    Level: advanced

6793:     Notes: Frees not only the matrices, but also the array that contains the matrices
6794:            In Fortran will not free the array.

6796: .seealso: MatCreateSubMatrices()
6797: @*/
6798: PetscErrorCode MatDestroySubMatrices(PetscInt n,Mat *mat[])
6799: {

6803:   if (!*mat) return(0);
6804:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6807:   /* Destroy dummy submatrices (*mat)[n]...(*mat)[n+nstages-1] used for reuse struct Mat_SubSppt */
6808:   if ((*mat)[n]) {
6809:     PetscBool      isdummy;
6810:     PetscObjectTypeCompare((PetscObject)(*mat)[n],MATDUMMY,&isdummy);
6811:     if (isdummy) {
6812:       Mat_SubSppt* smat = (Mat_SubSppt*)((*mat)[n]->data); /* singleis and nstages are saved in (*mat)[n]->data */

6814:       if (smat && !smat->singleis) {
6815:         PetscInt i,nstages=smat->nstages;
6816:         for (i=0; i<nstages; i++) {
6817:           MatDestroy(&(*mat)[n+i]);
6818:         }
6819:       }
6820:     }
6821:   }

6823:   MatDestroyMatrices(n,mat);
6824:   return(0);
6825: }

6827: /*@C
6828:    MatGetSeqNonzeroStructure - Extracts the sequential nonzero structure from a matrix.

6830:    Collective on Mat

6832:    Input Parameters:
6833: .  mat - the matrix

6835:    Output Parameter:
6836: .  matstruct - the sequential matrix with the nonzero structure of mat

6838:   Level: intermediate

6840: .seealso: MatDestroySeqNonzeroStructure(), MatCreateSubMatrices(), MatDestroyMatrices()
6841: @*/
6842: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat,Mat *matstruct)
6843: {


6851:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6852:   MatCheckPreallocated(mat,1);

6854:   if (!mat->ops->getseqnonzerostructure) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name);
6855:   PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6856:   (*mat->ops->getseqnonzerostructure)(mat,matstruct);
6857:   PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6858:   return(0);
6859: }

6861: /*@C
6862:    MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure().

6864:    Collective on Mat

6866:    Input Parameters:
6867: .  mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling
6868:                        sequence of MatGetSequentialNonzeroStructure())

6870:    Level: advanced

6872:     Notes: Frees not only the matrices, but also the array that contains the matrices

6874: .seealso: MatGetSeqNonzeroStructure()
6875: @*/
6876: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
6877: {

6882:   MatDestroy(mat);
6883:   return(0);
6884: }

6886: /*@
6887:    MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
6888:    replaces the index sets by larger ones that represent submatrices with
6889:    additional overlap.

6891:    Collective on Mat

6893:    Input Parameters:
6894: +  mat - the matrix
6895: .  n   - the number of index sets
6896: .  is  - the array of index sets (these index sets will changed during the call)
6897: -  ov  - the additional overlap requested

6899:    Options Database:
6900: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

6902:    Level: developer

6904:    Concepts: overlap
6905:    Concepts: ASM^computing overlap

6907: .seealso: MatCreateSubMatrices()
6908: @*/
6909: PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov)
6910: {

6916:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
6917:   if (n) {
6920:   }
6921:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6922:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6923:   MatCheckPreallocated(mat,1);

6925:   if (!ov) return(0);
6926:   if (!mat->ops->increaseoverlap) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6927:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
6928:   (*mat->ops->increaseoverlap)(mat,n,is,ov);
6929:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
6930:   return(0);
6931: }


6934: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat,IS*,PetscInt);

6936: /*@
6937:    MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
6938:    a sub communicator, replaces the index sets by larger ones that represent submatrices with
6939:    additional overlap.

6941:    Collective on Mat

6943:    Input Parameters:
6944: +  mat - the matrix
6945: .  n   - the number of index sets
6946: .  is  - the array of index sets (these index sets will changed during the call)
6947: -  ov  - the additional overlap requested

6949:    Options Database:
6950: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

6952:    Level: developer

6954:    Concepts: overlap
6955:    Concepts: ASM^computing overlap

6957: .seealso: MatCreateSubMatrices()
6958: @*/
6959: PetscErrorCode MatIncreaseOverlapSplit(Mat mat,PetscInt n,IS is[],PetscInt ov)
6960: {
6961:   PetscInt       i;

6967:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
6968:   if (n) {
6971:   }
6972:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6973:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6974:   MatCheckPreallocated(mat,1);
6975:   if (!ov) return(0);
6976:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
6977:   for(i=0; i<n; i++){
6978:          MatIncreaseOverlapSplit_Single(mat,&is[i],ov);
6979:   }
6980:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
6981:   return(0);
6982: }




6987: /*@
6988:    MatGetBlockSize - Returns the matrix block size.

6990:    Not Collective

6992:    Input Parameter:
6993: .  mat - the matrix

6995:    Output Parameter:
6996: .  bs - block size

6998:    Notes:
6999:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.

7001:    If the block size has not been set yet this routine returns 1.

7003:    Level: intermediate

7005:    Concepts: matrices^block size

7007: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes()
7008: @*/
7009: PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs)
7010: {
7014:   *bs = PetscAbs(mat->rmap->bs);
7015:   return(0);
7016: }

7018: /*@
7019:    MatGetBlockSizes - Returns the matrix block row and column sizes.

7021:    Not Collective

7023:    Input Parameter:
7024: .  mat - the matrix

7026:    Output Parameter:
7027: .  rbs - row block size
7028: .  cbs - coumn block size

7030:    Notes:
7031:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7032:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7034:    If a block size has not been set yet this routine returns 1.

7036:    Level: intermediate

7038:    Concepts: matrices^block size

7040: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatSetBlockSizes()
7041: @*/
7042: PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs)
7043: {
7048:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7049:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7050:   return(0);
7051: }

7053: /*@
7054:    MatSetBlockSize - Sets the matrix block size.

7056:    Logically Collective on Mat

7058:    Input Parameters:
7059: +  mat - the matrix
7060: -  bs - block size

7062:    Notes:
7063:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7064:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7066:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block size
7067:     is compatible with the matrix local sizes.

7069:    Level: intermediate

7071:    Concepts: matrices^block size

7073: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes()
7074: @*/
7075: PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs)
7076: {

7082:   MatSetBlockSizes(mat,bs,bs);
7083:   return(0);
7084: }

7086: /*@
7087:    MatSetBlockSizes - Sets the matrix block row and column sizes.

7089:    Logically Collective on Mat

7091:    Input Parameters:
7092: +  mat - the matrix
7093: -  rbs - row block size
7094: -  cbs - column block size

7096:    Notes:
7097:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7098:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7099:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later

7101:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block sizes
7102:     are compatible with the matrix local sizes.

7104:     The row and column block size determine the blocksize of the "row" and "column" vectors returned by MatCreateVecs().

7106:    Level: intermediate

7108:    Concepts: matrices^block size

7110: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatGetBlockSizes()
7111: @*/
7112: PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs)
7113: {

7120:   if (mat->ops->setblocksizes) {
7121:     (*mat->ops->setblocksizes)(mat,rbs,cbs);
7122:   }
7123:   if (mat->rmap->refcnt) {
7124:     ISLocalToGlobalMapping l2g = NULL;
7125:     PetscLayout            nmap = NULL;

7127:     PetscLayoutDuplicate(mat->rmap,&nmap);
7128:     if (mat->rmap->mapping) {
7129:       ISLocalToGlobalMappingDuplicate(mat->rmap->mapping,&l2g);
7130:     }
7131:     PetscLayoutDestroy(&mat->rmap);
7132:     mat->rmap = nmap;
7133:     mat->rmap->mapping = l2g;
7134:   }
7135:   if (mat->cmap->refcnt) {
7136:     ISLocalToGlobalMapping l2g = NULL;
7137:     PetscLayout            nmap = NULL;

7139:     PetscLayoutDuplicate(mat->cmap,&nmap);
7140:     if (mat->cmap->mapping) {
7141:       ISLocalToGlobalMappingDuplicate(mat->cmap->mapping,&l2g);
7142:     }
7143:     PetscLayoutDestroy(&mat->cmap);
7144:     mat->cmap = nmap;
7145:     mat->cmap->mapping = l2g;
7146:   }
7147:   PetscLayoutSetBlockSize(mat->rmap,rbs);
7148:   PetscLayoutSetBlockSize(mat->cmap,cbs);
7149:   return(0);
7150: }

7152: /*@
7153:    MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7155:    Logically Collective on Mat

7157:    Input Parameters:
7158: +  mat - the matrix
7159: .  fromRow - matrix from which to copy row block size
7160: -  fromCol - matrix from which to copy column block size (can be same as fromRow)

7162:    Level: developer

7164:    Concepts: matrices^block size

7166: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes()
7167: @*/
7168: PetscErrorCode MatSetBlockSizesFromMats(Mat mat,Mat fromRow,Mat fromCol)
7169: {

7176:   if (fromRow->rmap->bs > 0) {PetscLayoutSetBlockSize(mat->rmap,fromRow->rmap->bs);}
7177:   if (fromCol->cmap->bs > 0) {PetscLayoutSetBlockSize(mat->cmap,fromCol->cmap->bs);}
7178:   return(0);
7179: }

7181: /*@
7182:    MatResidual - Default routine to calculate the residual.

7184:    Collective on Mat and Vec

7186:    Input Parameters:
7187: +  mat - the matrix
7188: .  b   - the right-hand-side
7189: -  x   - the approximate solution

7191:    Output Parameter:
7192: .  r - location to store the residual

7194:    Level: developer

7196: .keywords: MG, default, multigrid, residual

7198: .seealso: PCMGSetResidual()
7199: @*/
7200: PetscErrorCode MatResidual(Mat mat,Vec b,Vec x,Vec r)
7201: {

7210:   MatCheckPreallocated(mat,1);
7211:   PetscLogEventBegin(MAT_Residual,mat,0,0,0);
7212:   if (!mat->ops->residual) {
7213:     MatMult(mat,x,r);
7214:     VecAYPX(r,-1.0,b);
7215:   } else {
7216:     (*mat->ops->residual)(mat,b,x,r);
7217:   }
7218:   PetscLogEventEnd(MAT_Residual,mat,0,0,0);
7219:   return(0);
7220: }

7222: /*@C
7223:     MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices.

7225:    Collective on Mat

7227:     Input Parameters:
7228: +   mat - the matrix
7229: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7230: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be   symmetrized
7231: -   inodecompressed - PETSC_TRUE or PETSC_FALSE  indicating if the nonzero structure of the
7232:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7233:                  always used.

7235:     Output Parameters:
7236: +   n - number of rows in the (possibly compressed) matrix
7237: .   ia - the row pointers [of length n+1]
7238: .   ja - the column indices
7239: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7240:            are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set

7242:     Level: developer

7244:     Notes: You CANNOT change any of the ia[] or ja[] values.

7246:            Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values

7248:     Fortran Node

7250:            In Fortran use
7251: $           PetscInt ia(1), ja(1)
7252: $           PetscOffset iia, jja
7253: $      call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr)
7254: $      Acess the ith and jth entries via ia(iia + i) and ja(jja + j)
7255: $
7256: $          or
7257: $
7258: $           PetscInt, pointer :: ia(:),ja(:)
7259: $    call  MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7260: $      Acess the ith and jth entries via ia(i) and ja(j)



7264: .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray()
7265: @*/
7266: PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7267: {

7277:   MatCheckPreallocated(mat,1);
7278:   if (!mat->ops->getrowij) *done = PETSC_FALSE;
7279:   else {
7280:     *done = PETSC_TRUE;
7281:     PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);
7282:     (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7283:     PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);
7284:   }
7285:   return(0);
7286: }

7288: /*@C
7289:     MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7291:     Collective on Mat

7293:     Input Parameters:
7294: +   mat - the matrix
7295: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7296: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7297:                 symmetrized
7298: .   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7299:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7300:                  always used.
7301: .   n - number of columns in the (possibly compressed) matrix
7302: .   ia - the column pointers
7303: -   ja - the row indices

7305:     Output Parameters:
7306: .   done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned

7308:     Note:
7309:     This routine zeros out n, ia, and ja. This is to prevent accidental
7310:     us of the array after it has been restored. If you pass NULL, it will
7311:     not zero the pointers.  Use of ia or ja after MatRestoreColumnIJ() is invalid.

7313:     Level: developer

7315: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7316: @*/
7317: PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7318: {

7328:   MatCheckPreallocated(mat,1);
7329:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
7330:   else {
7331:     *done = PETSC_TRUE;
7332:     (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7333:   }
7334:   return(0);
7335: }

7337: /*@C
7338:     MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with
7339:     MatGetRowIJ().

7341:     Collective on Mat

7343:     Input Parameters:
7344: +   mat - the matrix
7345: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7346: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7347:                 symmetrized
7348: .   inodecompressed -  PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7349:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7350:                  always used.
7351: .   n - size of (possibly compressed) matrix
7352: .   ia - the row pointers
7353: -   ja - the column indices

7355:     Output Parameters:
7356: .   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7358:     Note:
7359:     This routine zeros out n, ia, and ja. This is to prevent accidental
7360:     us of the array after it has been restored. If you pass NULL, it will
7361:     not zero the pointers.  Use of ia or ja after MatRestoreRowIJ() is invalid.

7363:     Level: developer

7365: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7366: @*/
7367: PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7368: {

7377:   MatCheckPreallocated(mat,1);

7379:   if (!mat->ops->restorerowij) *done = PETSC_FALSE;
7380:   else {
7381:     *done = PETSC_TRUE;
7382:     (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7383:     if (n)  *n = 0;
7384:     if (ia) *ia = NULL;
7385:     if (ja) *ja = NULL;
7386:   }
7387:   return(0);
7388: }

7390: /*@C
7391:     MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with
7392:     MatGetColumnIJ().

7394:     Collective on Mat

7396:     Input Parameters:
7397: +   mat - the matrix
7398: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7399: -   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7400:                 symmetrized
7401: -   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7402:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7403:                  always used.

7405:     Output Parameters:
7406: +   n - size of (possibly compressed) matrix
7407: .   ia - the column pointers
7408: .   ja - the row indices
7409: -   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7411:     Level: developer

7413: .seealso: MatGetColumnIJ(), MatRestoreRowIJ()
7414: @*/
7415: PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7416: {

7425:   MatCheckPreallocated(mat,1);

7427:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
7428:   else {
7429:     *done = PETSC_TRUE;
7430:     (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7431:     if (n)  *n = 0;
7432:     if (ia) *ia = NULL;
7433:     if (ja) *ja = NULL;
7434:   }
7435:   return(0);
7436: }

7438: /*@C
7439:     MatColoringPatch -Used inside matrix coloring routines that
7440:     use MatGetRowIJ() and/or MatGetColumnIJ().

7442:     Collective on Mat

7444:     Input Parameters:
7445: +   mat - the matrix
7446: .   ncolors - max color value
7447: .   n   - number of entries in colorarray
7448: -   colorarray - array indicating color for each column

7450:     Output Parameters:
7451: .   iscoloring - coloring generated using colorarray information

7453:     Level: developer

7455: .seealso: MatGetRowIJ(), MatGetColumnIJ()

7457: @*/
7458: PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring)
7459: {

7467:   MatCheckPreallocated(mat,1);

7469:   if (!mat->ops->coloringpatch) {
7470:     ISColoringCreate(PetscObjectComm((PetscObject)mat),ncolors,n,colorarray,PETSC_OWN_POINTER,iscoloring);
7471:   } else {
7472:     (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);
7473:   }
7474:   return(0);
7475: }


7478: /*@
7479:    MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

7481:    Logically Collective on Mat

7483:    Input Parameter:
7484: .  mat - the factored matrix to be reset

7486:    Notes:
7487:    This routine should be used only with factored matrices formed by in-place
7488:    factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE
7489:    format).  This option can save memory, for example, when solving nonlinear
7490:    systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
7491:    ILU(0) preconditioner.

7493:    Note that one can specify in-place ILU(0) factorization by calling
7494: .vb
7495:      PCType(pc,PCILU);
7496:      PCFactorSeUseInPlace(pc);
7497: .ve
7498:    or by using the options -pc_type ilu -pc_factor_in_place

7500:    In-place factorization ILU(0) can also be used as a local
7501:    solver for the blocks within the block Jacobi or additive Schwarz
7502:    methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
7503:    for details on setting local solver options.

7505:    Most users should employ the simplified KSP interface for linear solvers
7506:    instead of working directly with matrix algebra routines such as this.
7507:    See, e.g., KSPCreate().

7509:    Level: developer

7511: .seealso: PCFactorSetUseInPlace(), PCFactorGetUseInPlace()

7513:    Concepts: matrices^unfactored

7515: @*/
7516: PetscErrorCode MatSetUnfactored(Mat mat)
7517: {

7523:   MatCheckPreallocated(mat,1);
7524:   mat->factortype = MAT_FACTOR_NONE;
7525:   if (!mat->ops->setunfactored) return(0);
7526:   (*mat->ops->setunfactored)(mat);
7527:   return(0);
7528: }

7530: /*MC
7531:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran90.

7533:     Synopsis:
7534:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7536:     Not collective

7538:     Input Parameter:
7539: .   x - matrix

7541:     Output Parameters:
7542: +   xx_v - the Fortran90 pointer to the array
7543: -   ierr - error code

7545:     Example of Usage:
7546: .vb
7547:       PetscScalar, pointer xx_v(:,:)
7548:       ....
7549:       call MatDenseGetArrayF90(x,xx_v,ierr)
7550:       a = xx_v(3)
7551:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
7552: .ve

7554:     Level: advanced

7556: .seealso:  MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJGetArrayF90()

7558:     Concepts: matrices^accessing array

7560: M*/

7562: /*MC
7563:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
7564:     accessed with MatDenseGetArrayF90().

7566:     Synopsis:
7567:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7569:     Not collective

7571:     Input Parameters:
7572: +   x - matrix
7573: -   xx_v - the Fortran90 pointer to the array

7575:     Output Parameter:
7576: .   ierr - error code

7578:     Example of Usage:
7579: .vb
7580:        PetscScalar, pointer xx_v(:,:)
7581:        ....
7582:        call MatDenseGetArrayF90(x,xx_v,ierr)
7583:        a = xx_v(3)
7584:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
7585: .ve

7587:     Level: advanced

7589: .seealso:  MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJRestoreArrayF90()

7591: M*/


7594: /*MC
7595:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran90.

7597:     Synopsis:
7598:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7600:     Not collective

7602:     Input Parameter:
7603: .   x - matrix

7605:     Output Parameters:
7606: +   xx_v - the Fortran90 pointer to the array
7607: -   ierr - error code

7609:     Example of Usage:
7610: .vb
7611:       PetscScalar, pointer xx_v(:)
7612:       ....
7613:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7614:       a = xx_v(3)
7615:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7616: .ve

7618:     Level: advanced

7620: .seealso:  MatSeqAIJRestoreArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseGetArrayF90()

7622:     Concepts: matrices^accessing array

7624: M*/

7626: /*MC
7627:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
7628:     accessed with MatSeqAIJGetArrayF90().

7630:     Synopsis:
7631:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7633:     Not collective

7635:     Input Parameters:
7636: +   x - matrix
7637: -   xx_v - the Fortran90 pointer to the array

7639:     Output Parameter:
7640: .   ierr - error code

7642:     Example of Usage:
7643: .vb
7644:        PetscScalar, pointer xx_v(:)
7645:        ....
7646:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7647:        a = xx_v(3)
7648:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7649: .ve

7651:     Level: advanced

7653: .seealso:  MatSeqAIJGetArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseRestoreArrayF90()

7655: M*/


7658: /*@
7659:     MatCreateSubMatrix - Gets a single submatrix on the same number of processors
7660:                       as the original matrix.

7662:     Collective on Mat

7664:     Input Parameters:
7665: +   mat - the original matrix
7666: .   isrow - parallel IS containing the rows this processor should obtain
7667: .   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.
7668: -   cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

7670:     Output Parameter:
7671: .   newmat - the new submatrix, of the same type as the old

7673:     Level: advanced

7675:     Notes:
7676:     The submatrix will be able to be multiplied with vectors using the same layout as iscol.

7678:     Some matrix types place restrictions on the row and column indices, such
7679:     as that they be sorted or that they be equal to each other.

7681:     The index sets may not have duplicate entries.

7683:       The first time this is called you should use a cll of MAT_INITIAL_MATRIX,
7684:    the MatCreateSubMatrix() routine will create the newmat for you. Any additional calls
7685:    to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX
7686:    will reuse the matrix generated the first time.  You should call MatDestroy() on newmat when
7687:    you are finished using it.

7689:     The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
7690:     the input matrix.

7692:     If iscol is NULL then all columns are obtained (not supported in Fortran).

7694:    Example usage:
7695:    Consider the following 8x8 matrix with 34 non-zero values, that is
7696:    assembled across 3 processors. Let's assume that proc0 owns 3 rows,
7697:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
7698:    as follows:

7700: .vb
7701:             1  2  0  |  0  3  0  |  0  4
7702:     Proc0   0  5  6  |  7  0  0  |  8  0
7703:             9  0 10  | 11  0  0  | 12  0
7704:     -------------------------------------
7705:            13  0 14  | 15 16 17  |  0  0
7706:     Proc1   0 18  0  | 19 20 21  |  0  0
7707:             0  0  0  | 22 23  0  | 24  0
7708:     -------------------------------------
7709:     Proc2  25 26 27  |  0  0 28  | 29  0
7710:            30  0  0  | 31 32 33  |  0 34
7711: .ve

7713:     Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6].  The resulting submatrix is

7715: .vb
7716:             2  0  |  0  3  0  |  0
7717:     Proc0   5  6  |  7  0  0  |  8
7718:     -------------------------------
7719:     Proc1  18  0  | 19 20 21  |  0
7720:     -------------------------------
7721:     Proc2  26 27  |  0  0 28  | 29
7722:             0  0  | 31 32 33  |  0
7723: .ve


7726:     Concepts: matrices^submatrices

7728: .seealso: MatCreateSubMatrices()
7729: @*/
7730: PetscErrorCode MatCreateSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat)
7731: {
7733:   PetscMPIInt    size;
7734:   Mat            *local;
7735:   IS             iscoltmp;

7744:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7745:   if (cll == MAT_IGNORE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot use MAT_IGNORE_MATRIX");

7747:   MatCheckPreallocated(mat,1);
7748:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

7750:   if (!iscol || isrow == iscol) {
7751:     PetscBool   stride;
7752:     PetscMPIInt grabentirematrix = 0,grab;
7753:     PetscObjectTypeCompare((PetscObject)isrow,ISSTRIDE,&stride);
7754:     if (stride) {
7755:       PetscInt first,step,n,rstart,rend;
7756:       ISStrideGetInfo(isrow,&first,&step);
7757:       if (step == 1) {
7758:         MatGetOwnershipRange(mat,&rstart,&rend);
7759:         if (rstart == first) {
7760:           ISGetLocalSize(isrow,&n);
7761:           if (n == rend-rstart) {
7762:             grabentirematrix = 1;
7763:           }
7764:         }
7765:       }
7766:     }
7767:     MPIU_Allreduce(&grabentirematrix,&grab,1,MPI_INT,MPI_MIN,PetscObjectComm((PetscObject)mat));
7768:     if (grab) {
7769:       PetscInfo(mat,"Getting entire matrix as submatrix\n");
7770:       if (cll == MAT_INITIAL_MATRIX) {
7771:         *newmat = mat;
7772:         PetscObjectReference((PetscObject)mat);
7773:       }
7774:       return(0);
7775:     }
7776:   }

7778:   if (!iscol) {
7779:     ISCreateStride(PetscObjectComm((PetscObject)mat),mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);
7780:   } else {
7781:     iscoltmp = iscol;
7782:   }

7784:   /* if original matrix is on just one processor then use submatrix generated */
7785:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
7786:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);
7787:     if (!iscol) {ISDestroy(&iscoltmp);}
7788:     return(0);
7789:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
7790:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);
7791:     *newmat = *local;
7792:     PetscFree(local);
7793:     if (!iscol) {ISDestroy(&iscoltmp);}
7794:     return(0);
7795:   } else if (!mat->ops->createsubmatrix) {
7796:     /* Create a new matrix type that implements the operation using the full matrix */
7797:     PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7798:     switch (cll) {
7799:     case MAT_INITIAL_MATRIX:
7800:       MatCreateSubMatrixVirtual(mat,isrow,iscoltmp,newmat);
7801:       break;
7802:     case MAT_REUSE_MATRIX:
7803:       MatSubMatrixVirtualUpdate(*newmat,mat,isrow,iscoltmp);
7804:       break;
7805:     default: SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
7806:     }
7807:     PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
7808:     if (!iscol) {ISDestroy(&iscoltmp);}
7809:     return(0);
7810:   }

7812:   if (!mat->ops->createsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
7813:   PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7814:   (*mat->ops->createsubmatrix)(mat,isrow,iscoltmp,cll,newmat);
7815:   PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
7816:   if (!iscol) {ISDestroy(&iscoltmp);}
7817:   if (*newmat && cll == MAT_INITIAL_MATRIX) {PetscObjectStateIncrease((PetscObject)*newmat);}
7818:   return(0);
7819: }

7821: /*@
7822:    MatStashSetInitialSize - sets the sizes of the matrix stash, that is
7823:    used during the assembly process to store values that belong to
7824:    other processors.

7826:    Not Collective

7828:    Input Parameters:
7829: +  mat   - the matrix
7830: .  size  - the initial size of the stash.
7831: -  bsize - the initial size of the block-stash(if used).

7833:    Options Database Keys:
7834: +   -matstash_initial_size <size> or <size0,size1,...sizep-1>
7835: -   -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1>

7837:    Level: intermediate

7839:    Notes:
7840:      The block-stash is used for values set with MatSetValuesBlocked() while
7841:      the stash is used for values set with MatSetValues()

7843:      Run with the option -info and look for output of the form
7844:      MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
7845:      to determine the appropriate value, MM, to use for size and
7846:      MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
7847:      to determine the value, BMM to use for bsize

7849:    Concepts: stash^setting matrix size
7850:    Concepts: matrices^stash

7852: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo()

7854: @*/
7855: PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize)
7856: {

7862:   MatStashSetInitialSize_Private(&mat->stash,size);
7863:   MatStashSetInitialSize_Private(&mat->bstash,bsize);
7864:   return(0);
7865: }

7867: /*@
7868:    MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
7869:      the matrix

7871:    Neighbor-wise Collective on Mat

7873:    Input Parameters:
7874: +  mat   - the matrix
7875: .  x,y - the vectors
7876: -  w - where the result is stored

7878:    Level: intermediate

7880:    Notes:
7881:     w may be the same vector as y.

7883:     This allows one to use either the restriction or interpolation (its transpose)
7884:     matrix to do the interpolation

7886:     Concepts: interpolation

7888: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

7890: @*/
7891: PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w)
7892: {
7894:   PetscInt       M,N,Ny;

7902:   MatCheckPreallocated(A,1);
7903:   MatGetSize(A,&M,&N);
7904:   VecGetSize(y,&Ny);
7905:   if (M == Ny) {
7906:     MatMultAdd(A,x,y,w);
7907:   } else {
7908:     MatMultTransposeAdd(A,x,y,w);
7909:   }
7910:   return(0);
7911: }

7913: /*@
7914:    MatInterpolate - y = A*x or A'*x depending on the shape of
7915:      the matrix

7917:    Neighbor-wise Collective on Mat

7919:    Input Parameters:
7920: +  mat   - the matrix
7921: -  x,y - the vectors

7923:    Level: intermediate

7925:    Notes:
7926:     This allows one to use either the restriction or interpolation (its transpose)
7927:     matrix to do the interpolation

7929:    Concepts: matrices^interpolation

7931: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

7933: @*/
7934: PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y)
7935: {
7937:   PetscInt       M,N,Ny;

7944:   MatCheckPreallocated(A,1);
7945:   MatGetSize(A,&M,&N);
7946:   VecGetSize(y,&Ny);
7947:   if (M == Ny) {
7948:     MatMult(A,x,y);
7949:   } else {
7950:     MatMultTranspose(A,x,y);
7951:   }
7952:   return(0);
7953: }

7955: /*@
7956:    MatRestrict - y = A*x or A'*x

7958:    Neighbor-wise Collective on Mat

7960:    Input Parameters:
7961: +  mat   - the matrix
7962: -  x,y - the vectors

7964:    Level: intermediate

7966:    Notes:
7967:     This allows one to use either the restriction or interpolation (its transpose)
7968:     matrix to do the restriction

7970:    Concepts: matrices^restriction

7972: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate()

7974: @*/
7975: PetscErrorCode MatRestrict(Mat A,Vec x,Vec y)
7976: {
7978:   PetscInt       M,N,Ny;

7985:   MatCheckPreallocated(A,1);

7987:   MatGetSize(A,&M,&N);
7988:   VecGetSize(y,&Ny);
7989:   if (M == Ny) {
7990:     MatMult(A,x,y);
7991:   } else {
7992:     MatMultTranspose(A,x,y);
7993:   }
7994:   return(0);
7995: }

7997: /*@
7998:    MatGetNullSpace - retrieves the null space to a matrix.

8000:    Logically Collective on Mat and MatNullSpace

8002:    Input Parameters:
8003: +  mat - the matrix
8004: -  nullsp - the null space object

8006:    Level: developer

8008:    Concepts: null space^attaching to matrix

8010: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetNullSpace()
8011: @*/
8012: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8013: {
8018:   *nullsp = mat->nullsp;
8019:   return(0);
8020: }

8022: /*@
8023:    MatSetNullSpace - attaches a null space to a matrix.

8025:    Logically Collective on Mat and MatNullSpace

8027:    Input Parameters:
8028: +  mat - the matrix
8029: -  nullsp - the null space object

8031:    Level: advanced

8033:    Notes:
8034:       This null space is used by the linear solvers. Overwrites any previous null space that may have been attached

8036:       For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) you also likely should
8037:       call MatSetTransposeNullSpace(). This allows the linear system to be solved in a least squares sense.

8039:       You can remove the null space by calling this routine with an nullsp of NULL


8042:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8043:    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).
8044:    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
8045:    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
8046:    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).

8048:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8050:     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
8051:     routine also automatically calls MatSetTransposeNullSpace().

8053:    Concepts: null space^attaching to matrix

8055: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetTransposeNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8056: @*/
8057: PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp)
8058: {

8065:   MatCheckPreallocated(mat,1);
8066:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8067:   MatNullSpaceDestroy(&mat->nullsp);
8068:   mat->nullsp = nullsp;
8069:   if (mat->symmetric_set && mat->symmetric) {
8070:     MatSetTransposeNullSpace(mat,nullsp);
8071:   }
8072:   return(0);
8073: }

8075: /*@
8076:    MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8078:    Logically Collective on Mat and MatNullSpace

8080:    Input Parameters:
8081: +  mat - the matrix
8082: -  nullsp - the null space object

8084:    Level: developer

8086:    Concepts: null space^attaching to matrix

8088: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetTransposeNullSpace(), MatSetNullSpace(), MatGetNullSpace()
8089: @*/
8090: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8091: {
8096:   *nullsp = mat->transnullsp;
8097:   return(0);
8098: }

8100: /*@
8101:    MatSetTransposeNullSpace - attaches a null space to a matrix.

8103:    Logically Collective on Mat and MatNullSpace

8105:    Input Parameters:
8106: +  mat - the matrix
8107: -  nullsp - the null space object

8109:    Level: advanced

8111:    Notes:
8112:       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.
8113:       You must also call MatSetNullSpace()


8116:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8117:    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).
8118:    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
8119:    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
8120:    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).

8122:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8124:    Concepts: null space^attaching to matrix

8126: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8127: @*/
8128: PetscErrorCode MatSetTransposeNullSpace(Mat mat,MatNullSpace nullsp)
8129: {

8136:   MatCheckPreallocated(mat,1);
8137:   PetscObjectReference((PetscObject)nullsp);
8138:   MatNullSpaceDestroy(&mat->transnullsp);
8139:   mat->transnullsp = nullsp;
8140:   return(0);
8141: }

8143: /*@
8144:    MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8145:         This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8147:    Logically Collective on Mat and MatNullSpace

8149:    Input Parameters:
8150: +  mat - the matrix
8151: -  nullsp - the null space object

8153:    Level: advanced

8155:    Notes:
8156:       Overwrites any previous near null space that may have been attached

8158:       You can remove the null space by calling this routine with an nullsp of NULL

8160:    Concepts: null space^attaching to matrix

8162: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace(), MatNullSpaceCreateRigidBody(), MatGetNearNullSpace()
8163: @*/
8164: PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp)
8165: {

8172:   MatCheckPreallocated(mat,1);
8173:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8174:   MatNullSpaceDestroy(&mat->nearnullsp);
8175:   mat->nearnullsp = nullsp;
8176:   return(0);
8177: }

8179: /*@
8180:    MatGetNearNullSpace -Get null space attached with MatSetNearNullSpace()

8182:    Not Collective

8184:    Input Parameters:
8185: .  mat - the matrix

8187:    Output Parameters:
8188: .  nullsp - the null space object, NULL if not set

8190:    Level: developer

8192:    Concepts: null space^attaching to matrix

8194: .seealso: MatSetNearNullSpace(), MatGetNullSpace(), MatNullSpaceCreate()
8195: @*/
8196: PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp)
8197: {
8202:   MatCheckPreallocated(mat,1);
8203:   *nullsp = mat->nearnullsp;
8204:   return(0);
8205: }

8207: /*@C
8208:    MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8210:    Collective on Mat

8212:    Input Parameters:
8213: +  mat - the matrix
8214: .  row - row/column permutation
8215: .  fill - expected fill factor >= 1.0
8216: -  level - level of fill, for ICC(k)

8218:    Notes:
8219:    Probably really in-place only when level of fill is zero, otherwise allocates
8220:    new space to store factored matrix and deletes previous memory.

8222:    Most users should employ the simplified KSP interface for linear solvers
8223:    instead of working directly with matrix algebra routines such as this.
8224:    See, e.g., KSPCreate().

8226:    Level: developer

8228:    Concepts: matrices^incomplete Cholesky factorization
8229:    Concepts: Cholesky factorization

8231: .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()

8233:     Developer Note: fortran interface is not autogenerated as the f90
8234:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8236: @*/
8237: PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info)
8238: {

8246:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
8247:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
8248:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
8249:   if (!mat->ops->iccfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8250:   MatCheckPreallocated(mat,1);
8251:   (*mat->ops->iccfactor)(mat,row,info);
8252:   PetscObjectStateIncrease((PetscObject)mat);
8253:   return(0);
8254: }

8256: /*@
8257:    MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
8258:          ghosted ones.

8260:    Not Collective

8262:    Input Parameters:
8263: +  mat - the matrix
8264: -  diag = the diagonal values, including ghost ones

8266:    Level: developer

8268:    Notes: Works only for MPIAIJ and MPIBAIJ matrices

8270: .seealso: MatDiagonalScale()
8271: @*/
8272: PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag)
8273: {
8275:   PetscMPIInt    size;


8282:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled");
8283:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
8284:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
8285:   if (size == 1) {
8286:     PetscInt n,m;
8287:     VecGetSize(diag,&n);
8288:     MatGetSize(mat,0,&m);
8289:     if (m == n) {
8290:       MatDiagonalScale(mat,0,diag);
8291:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions");
8292:   } else {
8293:     PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));
8294:   }
8295:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
8296:   PetscObjectStateIncrease((PetscObject)mat);
8297:   return(0);
8298: }

8300: /*@
8301:    MatGetInertia - Gets the inertia from a factored matrix

8303:    Collective on Mat

8305:    Input Parameter:
8306: .  mat - the matrix

8308:    Output Parameters:
8309: +   nneg - number of negative eigenvalues
8310: .   nzero - number of zero eigenvalues
8311: -   npos - number of positive eigenvalues

8313:    Level: advanced

8315:    Notes: Matrix must have been factored by MatCholeskyFactor()


8318: @*/
8319: PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
8320: {

8326:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8327:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled");
8328:   if (!mat->ops->getinertia) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8329:   (*mat->ops->getinertia)(mat,nneg,nzero,npos);
8330:   return(0);
8331: }

8333: /* ----------------------------------------------------------------*/
8334: /*@C
8335:    MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

8337:    Neighbor-wise Collective on Mat and Vecs

8339:    Input Parameters:
8340: +  mat - the factored matrix
8341: -  b - the right-hand-side vectors

8343:    Output Parameter:
8344: .  x - the result vectors

8346:    Notes:
8347:    The vectors b and x cannot be the same.  I.e., one cannot
8348:    call MatSolves(A,x,x).

8350:    Notes:
8351:    Most users should employ the simplified KSP interface for linear solvers
8352:    instead of working directly with matrix algebra routines such as this.
8353:    See, e.g., KSPCreate().

8355:    Level: developer

8357:    Concepts: matrices^triangular solves

8359: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve()
8360: @*/
8361: PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x)
8362: {

8368:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
8369:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8370:   if (!mat->rmap->N && !mat->cmap->N) return(0);

8372:   if (!mat->ops->solves) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8373:   MatCheckPreallocated(mat,1);
8374:   PetscLogEventBegin(MAT_Solves,mat,0,0,0);
8375:   (*mat->ops->solves)(mat,b,x);
8376:   PetscLogEventEnd(MAT_Solves,mat,0,0,0);
8377:   return(0);
8378: }

8380: /*@
8381:    MatIsSymmetric - Test whether a matrix is symmetric

8383:    Collective on Mat

8385:    Input Parameter:
8386: +  A - the matrix to test
8387: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

8389:    Output Parameters:
8390: .  flg - the result

8392:    Notes: For real numbers MatIsSymmetric() and MatIsHermitian() return identical results

8394:    Level: intermediate

8396:    Concepts: matrix^symmetry

8398: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown()
8399: @*/
8400: PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool  *flg)
8401: {


8408:   if (!A->symmetric_set) {
8409:     if (!A->ops->issymmetric) {
8410:       MatType mattype;
8411:       MatGetType(A,&mattype);
8412:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8413:     }
8414:     (*A->ops->issymmetric)(A,tol,flg);
8415:     if (!tol) {
8416:       A->symmetric_set = PETSC_TRUE;
8417:       A->symmetric     = *flg;
8418:       if (A->symmetric) {
8419:         A->structurally_symmetric_set = PETSC_TRUE;
8420:         A->structurally_symmetric     = PETSC_TRUE;
8421:       }
8422:     }
8423:   } else if (A->symmetric) {
8424:     *flg = PETSC_TRUE;
8425:   } else if (!tol) {
8426:     *flg = PETSC_FALSE;
8427:   } else {
8428:     if (!A->ops->issymmetric) {
8429:       MatType mattype;
8430:       MatGetType(A,&mattype);
8431:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8432:     }
8433:     (*A->ops->issymmetric)(A,tol,flg);
8434:   }
8435:   return(0);
8436: }

8438: /*@
8439:    MatIsHermitian - Test whether a matrix is Hermitian

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 Hermitian)

8447:    Output Parameters:
8448: .  flg - the result

8450:    Level: intermediate

8452:    Concepts: matrix^symmetry

8454: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(),
8455:           MatIsSymmetricKnown(), MatIsSymmetric()
8456: @*/
8457: PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool  *flg)
8458: {


8465:   if (!A->hermitian_set) {
8466:     if (!A->ops->ishermitian) {
8467:       MatType mattype;
8468:       MatGetType(A,&mattype);
8469:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8470:     }
8471:     (*A->ops->ishermitian)(A,tol,flg);
8472:     if (!tol) {
8473:       A->hermitian_set = PETSC_TRUE;
8474:       A->hermitian     = *flg;
8475:       if (A->hermitian) {
8476:         A->structurally_symmetric_set = PETSC_TRUE;
8477:         A->structurally_symmetric     = PETSC_TRUE;
8478:       }
8479:     }
8480:   } else if (A->hermitian) {
8481:     *flg = PETSC_TRUE;
8482:   } else if (!tol) {
8483:     *flg = PETSC_FALSE;
8484:   } else {
8485:     if (!A->ops->ishermitian) {
8486:       MatType mattype;
8487:       MatGetType(A,&mattype);
8488:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8489:     }
8490:     (*A->ops->ishermitian)(A,tol,flg);
8491:   }
8492:   return(0);
8493: }

8495: /*@
8496:    MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric.

8498:    Not Collective

8500:    Input Parameter:
8501: .  A - the matrix to check

8503:    Output Parameters:
8504: +  set - if the symmetric flag is set (this tells you if the next flag is valid)
8505: -  flg - the result

8507:    Level: advanced

8509:    Concepts: matrix^symmetry

8511:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric()
8512:          if you want it explicitly checked

8514: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8515: @*/
8516: PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8517: {
8522:   if (A->symmetric_set) {
8523:     *set = PETSC_TRUE;
8524:     *flg = A->symmetric;
8525:   } else {
8526:     *set = PETSC_FALSE;
8527:   }
8528:   return(0);
8529: }

8531: /*@
8532:    MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian.

8534:    Not Collective

8536:    Input Parameter:
8537: .  A - the matrix to check

8539:    Output Parameters:
8540: +  set - if the hermitian flag is set (this tells you if the next flag is valid)
8541: -  flg - the result

8543:    Level: advanced

8545:    Concepts: matrix^symmetry

8547:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian()
8548:          if you want it explicitly checked

8550: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8551: @*/
8552: PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8553: {
8558:   if (A->hermitian_set) {
8559:     *set = PETSC_TRUE;
8560:     *flg = A->hermitian;
8561:   } else {
8562:     *set = PETSC_FALSE;
8563:   }
8564:   return(0);
8565: }

8567: /*@
8568:    MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

8570:    Collective on Mat

8572:    Input Parameter:
8573: .  A - the matrix to test

8575:    Output Parameters:
8576: .  flg - the result

8578:    Level: intermediate

8580:    Concepts: matrix^symmetry

8582: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption()
8583: @*/
8584: PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool  *flg)
8585: {

8591:   if (!A->structurally_symmetric_set) {
8592:     if (!A->ops->isstructurallysymmetric) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric");
8593:     (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);

8595:     A->structurally_symmetric_set = PETSC_TRUE;
8596:   }
8597:   *flg = A->structurally_symmetric;
8598:   return(0);
8599: }

8601: /*@
8602:    MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
8603:        to be communicated to other processors during the MatAssemblyBegin/End() process

8605:     Not collective

8607:    Input Parameter:
8608: .   vec - the vector

8610:    Output Parameters:
8611: +   nstash   - the size of the stash
8612: .   reallocs - the number of additional mallocs incurred.
8613: .   bnstash   - the size of the block stash
8614: -   breallocs - the number of additional mallocs incurred.in the block stash

8616:    Level: advanced

8618: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize()

8620: @*/
8621: PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs)
8622: {

8626:   MatStashGetInfo_Private(&mat->stash,nstash,reallocs);
8627:   MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);
8628:   return(0);
8629: }

8631: /*@C
8632:    MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
8633:      parallel layout

8635:    Collective on Mat

8637:    Input Parameter:
8638: .  mat - the matrix

8640:    Output Parameter:
8641: +   right - (optional) vector that the matrix can be multiplied against
8642: -   left - (optional) vector that the matrix vector product can be stored in

8644:    Notes:
8645:     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().

8647:   Notes: These are new vectors which are not owned by the Mat, they should be destroyed in VecDestroy() when no longer needed

8649:   Level: advanced

8651: .seealso: MatCreate(), VecDestroy()
8652: @*/
8653: PetscErrorCode MatCreateVecs(Mat mat,Vec *right,Vec *left)
8654: {

8660:   if (mat->ops->getvecs) {
8661:     (*mat->ops->getvecs)(mat,right,left);
8662:   } else {
8663:     PetscInt rbs,cbs;
8664:     MatGetBlockSizes(mat,&rbs,&cbs);
8665:     if (right) {
8666:       if (mat->cmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for columns not yet setup");
8667:       VecCreate(PetscObjectComm((PetscObject)mat),right);
8668:       VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
8669:       VecSetBlockSize(*right,cbs);
8670:       VecSetType(*right,VECSTANDARD);
8671:       PetscLayoutReference(mat->cmap,&(*right)->map);
8672:     }
8673:     if (left) {
8674:       if (mat->rmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for rows not yet setup");
8675:       VecCreate(PetscObjectComm((PetscObject)mat),left);
8676:       VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
8677:       VecSetBlockSize(*left,rbs);
8678:       VecSetType(*left,VECSTANDARD);
8679:       PetscLayoutReference(mat->rmap,&(*left)->map);
8680:     }
8681:   }
8682:   return(0);
8683: }

8685: /*@C
8686:    MatFactorInfoInitialize - Initializes a MatFactorInfo data structure
8687:      with default values.

8689:    Not Collective

8691:    Input Parameters:
8692: .    info - the MatFactorInfo data structure


8695:    Notes: The solvers are generally used through the KSP and PC objects, for example
8696:           PCLU, PCILU, PCCHOLESKY, PCICC

8698:    Level: developer

8700: .seealso: MatFactorInfo

8702:     Developer Note: fortran interface is not autogenerated as the f90
8703:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8705: @*/

8707: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
8708: {

8712:   PetscMemzero(info,sizeof(MatFactorInfo));
8713:   return(0);
8714: }

8716: /*@
8717:    MatFactorSetSchurIS - Set indices corresponding to the Schur complement

8719:    Collective on Mat

8721:    Input Parameters:
8722: +  mat - the factored matrix
8723: -  is - the index set defining the Schur indices (0-based)

8725:    Notes:

8727:    Level: developer

8729:    Concepts:

8731: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement()

8733: @*/
8734: PetscErrorCode MatFactorSetSchurIS(Mat mat,IS is)
8735: {
8736:   PetscErrorCode ierr,(*f)(Mat,IS);

8744:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
8745:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorSetSchurIS_C",&f);
8746:   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");
8747:   (*f)(mat,is);
8748:   return(0);
8749: }

8751: /*@
8752:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

8754:    Logically Collective on Mat

8756:    Input Parameters:
8757: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
8758: .  *S - location where to return the Schur complement (MATDENSE)

8760:    Notes:
8761:    The routine provides a copy of the Schur data stored within solver's data strutures. The caller must destroy the object when it is no longer needed.
8762:    If MatFactorInvertSchurComplement has been called, the routine gets back the inverse

8764:    Level: advanced

8766:    References:

8768: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement()
8769: @*/
8770: PetscErrorCode MatFactorCreateSchurComplement(Mat F,Mat* S)
8771: {

8776:   PetscUseMethod(F,"MatFactorCreateSchurComplement_C",(Mat,Mat*),(F,S));
8777:   return(0);
8778: }

8780: /*@
8781:   MatFactorGetSchurComplement - Get a Schur complement matrix object using the current Schur data

8783:    Logically Collective on Mat

8785:    Input Parameters:
8786: +  F - the factored matrix obtained by calling MatGetFactor()
8787: .  *S - location where to return the Schur complement (in MATDENSE format)

8789:    Notes:
8790:    Schur complement mode is currently implemented for sequential matrices.
8791:    The routine returns a dense matrix pointing to the raw data of the Schur Complement stored within the data strutures of the solver; e.g. if MatFactorInvertSchurComplement has been called, the returned matrix is actually the inverse of the Schur complement.
8792:    The caller should call MatFactorRestoreSchurComplement when the object is no longer needed.

8794:    Level: advanced

8796:    References:

8798: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement()
8799: @*/
8800: PetscErrorCode MatFactorGetSchurComplement(Mat F,Mat* S)
8801: {

8806:   PetscUseMethod(F,"MatFactorGetSchurComplement_C",(Mat,Mat*),(F,S));
8807:   return(0);
8808: }

8810: /*@
8811:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to MatFactorGetSchurComplement

8813:    Logically Collective on Mat

8815:    Input Parameters:
8816: +  F - the factored matrix obtained by calling MatGetFactor()
8817: .  *S - location where the Schur complement is stored

8819:    Notes:

8821:    Level: advanced

8823:    References:

8825: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement()
8826: @*/
8827: PetscErrorCode MatFactorRestoreSchurComplement(Mat F,Mat* S)
8828: {

8834:   MatDestroy(S);
8835:   return(0);
8836: }

8838: /*@
8839:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

8841:    Logically Collective on Mat

8843:    Input Parameters:
8844: +  F - the factored matrix obtained by calling MatGetFactor()
8845: .  rhs - location where the right hand side of the Schur complement system is stored
8846: -  sol - location where the solution of the Schur complement system has to be returned

8848:    Notes:
8849:    The sizes of the vectors should match the size of the Schur complement

8851:    Level: advanced

8853:    References:

8855: .seealso: MatGetFactor(), MatFactorSetSchurIS()
8856: @*/
8857: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
8858: {

8867:   PetscUseMethod(F,"MatFactorSolveSchurComplementTranspose_C",(Mat,Vec,Vec),(F,rhs,sol));
8868:   return(0);
8869: }

8871: /*@
8872:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

8874:    Logically Collective on Mat

8876:    Input Parameters:
8877: +  F - the factored matrix obtained by calling MatGetFactor()
8878: .  rhs - location where the right hand side of the Schur complement system is stored
8879: -  sol - location where the solution of the Schur complement system has to be returned

8881:    Notes:
8882:    The sizes of the vectors should match the size of the Schur complement

8884:    Level: advanced

8886:    References:

8888: .seealso: MatGetFactor(), MatFactorSetSchurIS()
8889: @*/
8890: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
8891: {

8900:   PetscUseMethod(F,"MatFactorSolveSchurComplement_C",(Mat,Vec,Vec),(F,rhs,sol));
8901:   return(0);
8902: }

8904: /*@
8905:   MatFactorInvertSchurComplement - Invert the raw Schur data computed during the factorization step

8907:    Logically Collective on Mat

8909:    Input Parameters:
8910: +  F - the factored matrix obtained by calling MatGetFactor()

8912:    Notes:

8914:    Level: advanced

8916:    References:

8918: .seealso: MatGetFactor(), MatFactorSetSchurIS()
8919: @*/
8920: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
8921: {

8926:   PetscUseMethod(F,"MatFactorInvertSchurComplement_C",(Mat),(F));
8927:   return(0);
8928: }

8930: /*@
8931:   MatFactorFactorizeSchurComplement - Factorize the raw Schur data computed during the factorization step

8933:    Logically Collective on Mat

8935:    Input Parameters:
8936: +  F - the factored matrix obtained by calling MatGetFactor()

8938:    Notes:
8939:    The routine uses the pointer to the raw data of the Schur Complement stored within the solver.

8941:    Level: advanced

8943:    References:

8945: .seealso: MatGetFactor(), MatMumpsSetSchurIS()
8946: @*/
8947: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
8948: {

8953:   PetscUseMethod(F,"MatFactorFactorizeSchurComplement_C",(Mat),(F));
8954:   return(0);
8955: }

8957: /*@
8958:   MatFactorSetSchurComplementSolverType - Set type of solver for Schur complement

8960:    Logically Collective on Mat

8962:    Input Parameters:
8963: +  F - the factored matrix obtained by calling MatGetFactor()
8964: -  type - either 0 (non-symmetric), 1 (symmetric positive definite) or 2 (symmetric indefinite)

8966:    Notes:
8967:    The parameter is used to compute the correct factorization of the Schur complement matrices
8968:    This could be useful in case the nature of the Schur complement is different from that of the matrix to be factored

8970:    Level: advanced

8972:    References:

8974: .seealso: MatGetFactor(), MatFactorSetSchurIS()
8975: @*/
8976: PetscErrorCode MatFactorSetSchurComplementSolverType(Mat F, PetscInt type)
8977: {

8983:   PetscTryMethod(F,"MatFactorSetSchurComplementSolverType_C",(Mat,PetscInt),(F,type));
8984:   return(0);
8985: }

8987: /*@
8988:    MatPtAP - Creates the matrix product C = P^T * A * P

8990:    Neighbor-wise Collective on Mat

8992:    Input Parameters:
8993: +  A - the matrix
8994: .  P - the projection matrix
8995: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
8996: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use PETSC_DEFAULT if you do not have a good estimate
8997:           if the result is a dense matrix this is irrelevent

8999:    Output Parameters:
9000: .  C - the product matrix

9002:    Notes:
9003:    C will be created and must be destroyed by the user with MatDestroy().

9005:    This routine is currently only implemented for pairs of AIJ matrices and classes
9006:    which inherit from AIJ.

9008:    Level: intermediate

9010: .seealso: MatPtAPSymbolic(), MatPtAPNumeric(), MatMatMult(), MatRARt()
9011: @*/
9012: PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C)
9013: {
9015:   PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*);
9016:   PetscErrorCode (*fP)(Mat,Mat,MatReuse,PetscReal,Mat*);
9017:   PetscErrorCode (*ptap)(Mat,Mat,MatReuse,PetscReal,Mat*)=NULL;
9018:   PetscBool      viatranspose=PETSC_FALSE,viamatmatmatmult=PETSC_FALSE;

9021:   PetscOptionsGetBool(((PetscObject)A)->options,((PetscObject)A)->prefix,"-matptap_viatranspose",&viatranspose,NULL);
9022:   PetscOptionsGetBool(((PetscObject)A)->options,((PetscObject)A)->prefix,"-matptap_viamatmatmatmult",&viamatmatmatmult,NULL);

9026:   MatCheckPreallocated(A,1);
9027:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
9028:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9029:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9032:   MatCheckPreallocated(P,2);
9033:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9034:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9036:   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);
9037:   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);
9038:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9039:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9041:   if (scall == MAT_REUSE_MATRIX) {
9044:     if (viatranspose || viamatmatmatmult) {
9045:       Mat Pt;
9046:       MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);
9047:       if (viamatmatmatmult) {
9048:         MatMatMatMult(Pt,A,P,scall,fill,C);
9049:       } else {
9050:         Mat AP;
9051:         MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);
9052:         MatMatMult(Pt,AP,scall,fill,C);
9053:         MatDestroy(&AP);
9054:       }
9055:       MatDestroy(&Pt);
9056:     } else {
9057:       PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9058:       PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9059:       (*(*C)->ops->ptapnumeric)(A,P,*C);
9060:       PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9061:       PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9062:     }
9063:     return(0);
9064:   }

9066:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9067:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9069:   fA = A->ops->ptap;
9070:   fP = P->ops->ptap;
9071:   if (fP == fA) {
9072:     if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",((PetscObject)A)->type_name);
9073:     ptap = fA;
9074:   } else {
9075:     /* dispatch based on the type of A and P from their PetscObject's PetscFunctionLists. */
9076:     char ptapname[256];
9077:     PetscStrcpy(ptapname,"MatPtAP_");
9078:     PetscStrcat(ptapname,((PetscObject)A)->type_name);
9079:     PetscStrcat(ptapname,"_");
9080:     PetscStrcat(ptapname,((PetscObject)P)->type_name);
9081:     PetscStrcat(ptapname,"_C"); /* e.g., ptapname = "MatPtAP_seqdense_seqaij_C" */
9082:     PetscObjectQueryFunction((PetscObject)P,ptapname,&ptap);
9083:     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);
9084:   }

9086:   if (viatranspose || viamatmatmatmult) {
9087:     Mat Pt;
9088:     MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);
9089:     if (viamatmatmatmult) {
9090:       MatMatMatMult(Pt,A,P,scall,fill,C);
9091:       PetscInfo(*C,"MatPtAP via MatMatMatMult\n");
9092:     } else {
9093:       Mat AP;
9094:       MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);
9095:       MatMatMult(Pt,AP,scall,fill,C);
9096:       MatDestroy(&AP);
9097:       PetscInfo(*C,"MatPtAP via MatTranspose and MatMatMult\n");
9098:     }
9099:     MatDestroy(&Pt);
9100:   } else {
9101:     PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9102:     (*ptap)(A,P,scall,fill,C);
9103:     PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9104:   }
9105:   return(0);
9106: }

9108: /*@
9109:    MatPtAPNumeric - Computes the matrix product C = P^T * A * P

9111:    Neighbor-wise Collective on Mat

9113:    Input Parameters:
9114: +  A - the matrix
9115: -  P - the projection matrix

9117:    Output Parameters:
9118: .  C - the product matrix

9120:    Notes:
9121:    C must have been created by calling MatPtAPSymbolic and must be destroyed by
9122:    the user using MatDeatroy().

9124:    This routine is currently only implemented for pairs of AIJ matrices and classes
9125:    which inherit from AIJ.  C will be of type MATAIJ.

9127:    Level: intermediate

9129: .seealso: MatPtAP(), MatPtAPSymbolic(), MatMatMultNumeric()
9130: @*/
9131: PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C)
9132: {

9138:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9139:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9142:   MatCheckPreallocated(P,2);
9143:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9144:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9147:   MatCheckPreallocated(C,3);
9148:   if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9149:   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);
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 (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);
9152:   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);
9153:   MatCheckPreallocated(A,1);

9155:   PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9156:   (*C->ops->ptapnumeric)(A,P,C);
9157:   PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9158:   return(0);
9159: }

9161: /*@
9162:    MatPtAPSymbolic - Creates the (i,j) structure of the matrix product C = P^T * A * P

9164:    Neighbor-wise Collective on Mat

9166:    Input Parameters:
9167: +  A - the matrix
9168: -  P - the projection matrix

9170:    Output Parameters:
9171: .  C - the (i,j) structure of the product matrix

9173:    Notes:
9174:    C will be created and must be destroyed by the user with MatDestroy().

9176:    This routine is currently only implemented for pairs of SeqAIJ matrices and classes
9177:    which inherit from SeqAIJ.  C will be of type MATSEQAIJ.  The product is computed using
9178:    this (i,j) structure by calling MatPtAPNumeric().

9180:    Level: intermediate

9182: .seealso: MatPtAP(), MatPtAPNumeric(), MatMatMultSymbolic()
9183: @*/
9184: PetscErrorCode MatPtAPSymbolic(Mat A,Mat P,PetscReal fill,Mat *C)
9185: {

9191:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9192:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9193:   if (fill <1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);
9196:   MatCheckPreallocated(P,2);
9197:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9198:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9201:   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);
9202:   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);
9203:   MatCheckPreallocated(A,1);
9204:   PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);
9205:   (*A->ops->ptapsymbolic)(A,P,fill,C);
9206:   PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);

9208:   /* MatSetBlockSize(*C,A->rmap->bs); NO! this is not always true -ma */
9209:   return(0);
9210: }

9212: /*@
9213:    MatRARt - Creates the matrix product C = R * A * R^T

9215:    Neighbor-wise Collective on Mat

9217:    Input Parameters:
9218: +  A - the matrix
9219: .  R - the projection matrix
9220: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9221: -  fill - expected fill as ratio of nnz(C)/nnz(A), use PETSC_DEFAULT if you do not have a good estimate
9222:           if the result is a dense matrix this is irrelevent

9224:    Output Parameters:
9225: .  C - the product matrix

9227:    Notes:
9228:    C will be created and must be destroyed by the user with MatDestroy().

9230:    This routine is currently only implemented for pairs of AIJ matrices and classes
9231:    which inherit from AIJ.

9233:    Level: intermediate

9235: .seealso: MatRARtSymbolic(), MatRARtNumeric(), MatMatMult(), MatPtAP()
9236: @*/
9237: PetscErrorCode MatRARt(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C)
9238: {

9244:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
9245:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9246:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9249:   MatCheckPreallocated(R,2);
9250:   if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9251:   if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9253:   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);

9255:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9256:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);
9257:   MatCheckPreallocated(A,1);

9259:   if (!A->ops->rart) {
9260:     MatType mattype;
9261:     MatGetType(A,&mattype);
9262:     SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix of type <%s> does not support RARt",mattype);
9263:   }
9264:   PetscLogEventBegin(MAT_RARt,A,R,0,0);
9265:   (*A->ops->rart)(A,R,scall,fill,C);
9266:   PetscLogEventEnd(MAT_RARt,A,R,0,0);
9267:   return(0);
9268: }

9270: /*@
9271:    MatRARtNumeric - Computes the matrix product C = R * A * R^T

9273:    Neighbor-wise Collective on Mat

9275:    Input Parameters:
9276: +  A - the matrix
9277: -  R - the projection matrix

9279:    Output Parameters:
9280: .  C - the product matrix

9282:    Notes:
9283:    C must have been created by calling MatRARtSymbolic and must be destroyed by
9284:    the user using MatDestroy().

9286:    This routine is currently only implemented for pairs of AIJ matrices and classes
9287:    which inherit from AIJ.  C will be of type MATAIJ.

9289:    Level: intermediate

9291: .seealso: MatRARt(), MatRARtSymbolic(), MatMatMultNumeric()
9292: @*/
9293: PetscErrorCode MatRARtNumeric(Mat A,Mat R,Mat C)
9294: {

9300:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9301:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9304:   MatCheckPreallocated(R,2);
9305:   if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9306:   if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9309:   MatCheckPreallocated(C,3);
9310:   if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9311:   if (R->rmap->N!=C->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->rmap->N);
9312:   if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N);
9313:   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);
9314:   if (R->rmap->N!=C->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->cmap->N);
9315:   MatCheckPreallocated(A,1);

9317:   PetscLogEventBegin(MAT_RARtNumeric,A,R,0,0);
9318:   (*A->ops->rartnumeric)(A,R,C);
9319:   PetscLogEventEnd(MAT_RARtNumeric,A,R,0,0);
9320:   return(0);
9321: }

9323: /*@
9324:    MatRARtSymbolic - Creates the (i,j) structure of the matrix product C = R * A * R^T

9326:    Neighbor-wise Collective on Mat

9328:    Input Parameters:
9329: +  A - the matrix
9330: -  R - the projection matrix

9332:    Output Parameters:
9333: .  C - the (i,j) structure of the product matrix

9335:    Notes:
9336:    C will be created and must be destroyed by the user with MatDestroy().

9338:    This routine is currently only implemented for pairs of SeqAIJ matrices and classes
9339:    which inherit from SeqAIJ.  C will be of type MATSEQAIJ.  The product is computed using
9340:    this (i,j) structure by calling MatRARtNumeric().

9342:    Level: intermediate

9344: .seealso: MatRARt(), MatRARtNumeric(), MatMatMultSymbolic()
9345: @*/
9346: PetscErrorCode MatRARtSymbolic(Mat A,Mat R,PetscReal fill,Mat *C)
9347: {

9353:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9354:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9355:   if (fill <1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);
9358:   MatCheckPreallocated(R,2);
9359:   if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9360:   if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9363:   if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N);
9364:   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);
9365:   MatCheckPreallocated(A,1);
9366:   PetscLogEventBegin(MAT_RARtSymbolic,A,R,0,0);
9367:   (*A->ops->rartsymbolic)(A,R,fill,C);
9368:   PetscLogEventEnd(MAT_RARtSymbolic,A,R,0,0);

9370:   MatSetBlockSizes(*C,PetscAbs(R->rmap->bs),PetscAbs(R->rmap->bs));
9371:   return(0);
9372: }

9374: /*@
9375:    MatMatMult - Performs Matrix-Matrix Multiplication C=A*B.

9377:    Neighbor-wise Collective on Mat

9379:    Input Parameters:
9380: +  A - the left matrix
9381: .  B - the right matrix
9382: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9383: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate
9384:           if the result is a dense matrix this is irrelevent

9386:    Output Parameters:
9387: .  C - the product matrix

9389:    Notes:
9390:    Unless scall is MAT_REUSE_MATRIX C will be created.

9392:    MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call

9394:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
9395:    actually needed.

9397:    If you have many matrices with the same non-zero structure to multiply, you
9398:    should either
9399: $   1) use MAT_REUSE_MATRIX in all calls but the first or
9400: $   2) call MatMatMultSymbolic() once and then MatMatMultNumeric() for each product needed
9401:    In the special case where matrix B (and hence C) are dense you can create the correctly sized matrix C yourself and then call this routine
9402:    with MAT_REUSE_MATRIX, rather than first having MatMatMult() create it for you. You can NEVER do this if the matrix C is