Actual source code: matrix.c

petsc-3.10.2 2018-10-09
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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,MAT_MatTrSolve;
 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_PartitioningND, 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_CUSPARSECopyToGPU, MAT_SetValuesBatch;
 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 Mat

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

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

 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 (!x->ops->setrandom) SETERRQ1(PetscObjectComm((PetscObject)x),PETSC_ERR_SUP,"Mat type %s",((PetscObject)x)->type_name);

 81:   if (!rctx) {
 82:     MPI_Comm comm;
 83:     PetscObjectGetComm((PetscObject)x,&comm);
 84:     PetscRandomCreate(comm,&randObj);
 85:     PetscRandomSetFromOptions(randObj);
 86:     rctx = randObj;
 87:   }

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

 93:   x->assembled = PETSC_TRUE;
 94:   PetscRandomDestroy(&randObj);
 95:   return(0);
 96: }

 98: /*@
 99:    MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

101:    Logically Collective on Mat

103:    Input Parameters:
104: .  mat - the factored matrix

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

111:    Level: advanced

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

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

119: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
120: @*/
121: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat,PetscReal *pivot,PetscInt *row)
122: {
125:   *pivot = mat->factorerror_zeropivot_value;
126:   *row   = mat->factorerror_zeropivot_row;
127:   return(0);
128: }

130: /*@
131:    MatFactorGetError - gets the error code from a factorization

133:    Logically Collective on Mat

135:    Input Parameters:
136: .  mat - the factored matrix

138:    Output Parameter:
139: .  err  - the error code

141:    Level: advanced

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

146: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
147: @*/
148: PetscErrorCode MatFactorGetError(Mat mat,MatFactorError *err)
149: {
152:   *err = mat->factorerrortype;
153:   return(0);
154: }

156: /*@
157:    MatFactorClearError - clears the error code in a factorization

159:    Logically Collective on Mat

161:    Input Parameter:
162: .  mat - the factored matrix

164:    Level: developer

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

169: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorGetError(), MatFactorGetErrorZeroPivot()
170: @*/
171: PetscErrorCode MatFactorClearError(Mat mat)
172: {
175:   mat->factorerrortype             = MAT_FACTOR_NOERROR;
176:   mat->factorerror_zeropivot_value = 0.0;
177:   mat->factorerror_zeropivot_row   = 0;
178:   return(0);
179: }

181: PETSC_INTERN PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat,PetscBool cols,PetscReal tol,IS *nonzero)
182: {
183:   PetscErrorCode    ierr;
184:   Vec               r,l;
185:   const PetscScalar *al;
186:   PetscInt          i,nz,gnz,N,n;

189:   MatCreateVecs(mat,&r,&l);
190:   if (!cols) { /* nonzero rows */
191:     MatGetSize(mat,&N,NULL);
192:     MatGetLocalSize(mat,&n,NULL);
193:     VecSet(l,0.0);
194:     VecSetRandom(r,NULL);
195:     MatMult(mat,r,l);
196:     VecGetArrayRead(l,&al);
197:   } else { /* nonzero columns */
198:     MatGetSize(mat,NULL,&N);
199:     MatGetLocalSize(mat,NULL,&n);
200:     VecSet(r,0.0);
201:     VecSetRandom(l,NULL);
202:     MatMultTranspose(mat,l,r);
203:     VecGetArrayRead(r,&al);
204:   }
205:   if (tol <= 0.0) { for (i=0,nz=0;i<n;i++) if (al[i] != 0.0) nz++; }
206:   else { for (i=0,nz=0;i<n;i++) if (PetscAbsScalar(al[i]) > tol) nz++; }
207:   MPIU_Allreduce(&nz,&gnz,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)mat));
208:   if (gnz != N) {
209:     PetscInt *nzr;
210:     PetscMalloc1(nz,&nzr);
211:     if (nz) {
212:       if (tol < 0) { for (i=0,nz=0;i<n;i++) if (al[i] != 0.0) nzr[nz++] = i; }
213:       else { for (i=0,nz=0;i<n;i++) if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i; }
214:     }
215:     ISCreateGeneral(PetscObjectComm((PetscObject)mat),nz,nzr,PETSC_OWN_POINTER,nonzero);
216:   } else *nonzero = NULL;
217:   if (!cols) { /* nonzero rows */
218:     VecRestoreArrayRead(l,&al);
219:   } else {
220:     VecRestoreArrayRead(r,&al);
221:   }
222:   VecDestroy(&l);
223:   VecDestroy(&r);
224:   return(0);
225: }

227: /*@
228:       MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

230:   Input Parameter:
231: .    A  - the matrix

233:   Output Parameter:
234: .    keptrows - the rows that are not completely zero

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

239:   Level: intermediate

241:  @*/
242: PetscErrorCode MatFindNonzeroRows(Mat mat,IS *keptrows)
243: {

250:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
251:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
252:   if (!mat->ops->findnonzerorows) {
253:     MatFindNonzeroRowsOrCols_Basic(mat,PETSC_FALSE,0.0,keptrows);
254:   } else {
255:     (*mat->ops->findnonzerorows)(mat,keptrows);
256:   }
257:   return(0);
258: }

260: /*@
261:       MatFindZeroRows - Locate all rows that are completely zero in the matrix

263:   Input Parameter:
264: .    A  - the matrix

266:   Output Parameter:
267: .    zerorows - the rows that are completely zero

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

272:   Level: intermediate

274:  @*/
275: PetscErrorCode MatFindZeroRows(Mat mat,IS *zerorows)
276: {
278:   IS keptrows;
279:   PetscInt m, n;


284:   MatFindNonzeroRows(mat, &keptrows);
285:   /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
286:      In keeping with this convention, we set zerorows to NULL if there are no zero
287:      rows. */
288:   if (keptrows == NULL) {
289:     *zerorows = NULL;
290:   } else {
291:     MatGetOwnershipRange(mat,&m,&n);
292:     ISComplement(keptrows,m,n,zerorows);
293:     ISDestroy(&keptrows);
294:   }
295:   return(0);
296: }

298: /*@
299:    MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

301:    Not Collective

303:    Input Parameters:
304: .   A - the matrix

306:    Output Parameters:
307: .   a - the diagonal part (which is a SEQUENTIAL matrix)

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

314:    Level: advanced

316: @*/
317: PetscErrorCode MatGetDiagonalBlock(Mat A,Mat *a)
318: {

325:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
326:   if (!A->ops->getdiagonalblock) {
327:     PetscMPIInt size;
328:     MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
329:     if (size == 1) {
330:       *a = A;
331:       return(0);
332:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not coded for this matrix type");
333:   }
334:   (*A->ops->getdiagonalblock)(A,a);
335:   return(0);
336: }

338: /*@
339:    MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

341:    Collective on Mat

343:    Input Parameters:
344: .  mat - the matrix

346:    Output Parameter:
347: .   trace - the sum of the diagonal entries

349:    Level: advanced

351: @*/
352: PetscErrorCode MatGetTrace(Mat mat,PetscScalar *trace)
353: {
355:   Vec            diag;

358:   MatCreateVecs(mat,&diag,NULL);
359:   MatGetDiagonal(mat,diag);
360:   VecSum(diag,trace);
361:   VecDestroy(&diag);
362:   return(0);
363: }

365: /*@
366:    MatRealPart - Zeros out the imaginary part of the matrix

368:    Logically Collective on Mat

370:    Input Parameters:
371: .  mat - the matrix

373:    Level: advanced


376: .seealso: MatImaginaryPart()
377: @*/
378: PetscErrorCode MatRealPart(Mat mat)
379: {

385:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
386:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
387:   if (!mat->ops->realpart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
388:   MatCheckPreallocated(mat,1);
389:   (*mat->ops->realpart)(mat);
390: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
391:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
392:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
393:   }
394: #endif
395:   return(0);
396: }

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

401:    Collective on Mat

403:    Input Parameter:
404: .  mat - the matrix

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

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

413:    Level: advanced

415: @*/
416: PetscErrorCode MatGetGhosts(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
417: {

423:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
424:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
425:   if (!mat->ops->getghosts) {
426:     if (nghosts) *nghosts = 0;
427:     if (ghosts) *ghosts = 0;
428:   } else {
429:     (*mat->ops->getghosts)(mat,nghosts,ghosts);
430:   }
431:   return(0);
432: }


435: /*@
436:    MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

438:    Logically Collective on Mat

440:    Input Parameters:
441: .  mat - the matrix

443:    Level: advanced


446: .seealso: MatRealPart()
447: @*/
448: PetscErrorCode MatImaginaryPart(Mat mat)
449: {

455:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
456:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
457:   if (!mat->ops->imaginarypart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
458:   MatCheckPreallocated(mat,1);
459:   (*mat->ops->imaginarypart)(mat);
460: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
461:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
462:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
463:   }
464: #endif
465:   return(0);
466: }

468: /*@
469:    MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for BAIJ matrices)

471:    Not Collective

473:    Input Parameter:
474: .  mat - the matrix

476:    Output Parameters:
477: +  missing - is any diagonal missing
478: -  dd - first diagonal entry that is missing (optional) on this process

480:    Level: advanced


483: .seealso: MatRealPart()
484: @*/
485: PetscErrorCode MatMissingDiagonal(Mat mat,PetscBool *missing,PetscInt *dd)
486: {

492:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
493:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
494:   if (!mat->ops->missingdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
495:   (*mat->ops->missingdiagonal)(mat,missing,dd);
496:   return(0);
497: }

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

504:    Not Collective

506:    Input Parameters:
507: +  mat - the matrix
508: -  row - the row to get

510:    Output Parameters:
511: +  ncols -  if not NULL, the number of nonzeros in the row
512: .  cols - if not NULL, the column numbers
513: -  vals - if not NULL, the values

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

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

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

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

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

537:    Fortran Notes:
538:    The calling sequence from Fortran is
539: .vb
540:    MatGetRow(matrix,row,ncols,cols,values,ierr)
541:          Mat     matrix (input)
542:          integer row    (input)
543:          integer ncols  (output)
544:          integer cols(maxcols) (output)
545:          double precision (or double complex) values(maxcols) output
546: .ve
547:    where maxcols >= maximum nonzeros in any row of the matrix.


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

554:    Level: advanced

556:    Concepts: matrices^row access

558: .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatCreateSubMatrices(), MatGetDiagonal()
559: @*/
560: PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
561: {
563:   PetscInt       incols;

568:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
569:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
570:   if (!mat->ops->getrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
571:   MatCheckPreallocated(mat,1);
572:   PetscLogEventBegin(MAT_GetRow,mat,0,0,0);
573:   (*mat->ops->getrow)(mat,row,&incols,(PetscInt**)cols,(PetscScalar**)vals);
574:   if (ncols) *ncols = incols;
575:   PetscLogEventEnd(MAT_GetRow,mat,0,0,0);
576:   return(0);
577: }

579: /*@
580:    MatConjugate - replaces the matrix values with their complex conjugates

582:    Logically Collective on Mat

584:    Input Parameters:
585: .  mat - the matrix

587:    Level: advanced

589: .seealso:  VecConjugate()
590: @*/
591: PetscErrorCode MatConjugate(Mat mat)
592: {
593: #if defined(PETSC_USE_COMPLEX)

598:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
599:   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");
600:   (*mat->ops->conjugate)(mat);
601: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
602:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
603:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
604:   }
605: #endif
606:   return(0);
607: #else
608:   return 0;
609: #endif
610: }

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

615:    Not Collective

617:    Input Parameters:
618: +  mat - the matrix
619: .  row - the row to get
620: .  ncols, cols - the number of nonzeros and their columns
621: -  vals - if nonzero the column values

623:    Notes:
624:    This routine should be called after you have finished examining the entries.

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

630:    Fortran Notes:
631:    The calling sequence from Fortran is
632: .vb
633:    MatRestoreRow(matrix,row,ncols,cols,values,ierr)
634:       Mat     matrix (input)
635:       integer row    (input)
636:       integer ncols  (output)
637:       integer cols(maxcols) (output)
638:       double precision (or double complex) values(maxcols) output
639: .ve
640:    Where maxcols >= maximum nonzeros in any row of the matrix.

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

645:    Level: advanced

647: .seealso:  MatGetRow()
648: @*/
649: PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
650: {

656:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
657:   if (!mat->ops->restorerow) return(0);
658:   (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);
659:   if (ncols) *ncols = 0;
660:   if (cols)  *cols = NULL;
661:   if (vals)  *vals = NULL;
662:   return(0);
663: }

665: /*@
666:    MatGetRowUpperTriangular - Sets a flag to enable calls to MatGetRow() for matrix in MATSBAIJ format.
667:    You should call MatRestoreRowUpperTriangular() after calling MatGetRow/MatRestoreRow() to disable the flag.

669:    Not Collective

671:    Input Parameters:
672: +  mat - the matrix

674:    Notes:
675:    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.

677:    Level: advanced

679:    Concepts: matrices^row access

681: .seealso: MatRestoreRowRowUpperTriangular()
682: @*/
683: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
684: {

690:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
691:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
692:   if (!mat->ops->getrowuppertriangular) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
693:   MatCheckPreallocated(mat,1);
694:   (*mat->ops->getrowuppertriangular)(mat);
695:   return(0);
696: }

698: /*@
699:    MatRestoreRowUpperTriangular - Disable calls to MatGetRow() for matrix in MATSBAIJ format.

701:    Not Collective

703:    Input Parameters:
704: +  mat - the matrix

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


710:    Level: advanced

712: .seealso:  MatGetRowUpperTriangular()
713: @*/
714: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
715: {

720:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
721:   if (!mat->ops->restorerowuppertriangular) return(0);
722:   (*mat->ops->restorerowuppertriangular)(mat);
723:   return(0);
724: }

726: /*@C
727:    MatSetOptionsPrefix - Sets the prefix used for searching for all
728:    Mat options in the database.

730:    Logically Collective on Mat

732:    Input Parameter:
733: +  A - the Mat context
734: -  prefix - the prefix to prepend to all option names

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

740:    Level: advanced

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

744: .seealso: MatSetFromOptions()
745: @*/
746: PetscErrorCode MatSetOptionsPrefix(Mat A,const char prefix[])
747: {

752:   PetscObjectSetOptionsPrefix((PetscObject)A,prefix);
753:   return(0);
754: }

756: /*@C
757:    MatAppendOptionsPrefix - Appends to the prefix used for searching for all
758:    Mat options in the database.

760:    Logically Collective on Mat

762:    Input Parameters:
763: +  A - the Mat context
764: -  prefix - the prefix to prepend to all option names

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

770:    Level: advanced

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

774: .seealso: MatGetOptionsPrefix()
775: @*/
776: PetscErrorCode MatAppendOptionsPrefix(Mat A,const char prefix[])
777: {

782:   PetscObjectAppendOptionsPrefix((PetscObject)A,prefix);
783:   return(0);
784: }

786: /*@C
787:    MatGetOptionsPrefix - Sets the prefix used for searching for all
788:    Mat options in the database.

790:    Not Collective

792:    Input Parameter:
793: .  A - the Mat context

795:    Output Parameter:
796: .  prefix - pointer to the prefix string used

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

802:    Level: advanced

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

806: .seealso: MatAppendOptionsPrefix()
807: @*/
808: PetscErrorCode MatGetOptionsPrefix(Mat A,const char *prefix[])
809: {

814:   PetscObjectGetOptionsPrefix((PetscObject)A,prefix);
815:   return(0);
816: }

818: /*@
819:    MatResetPreallocation - Reset mat to use the original nonzero pattern provided by users.

821:    Collective on Mat

823:    Input Parameters:
824: .  A - the Mat context

826:    Notes:
827:    The allocated memory will be shrunk after calling MatAssembly with MAT_FINAL_ASSEMBLY. Users can reset the preallocation to access the original memory.
828:    Currently support MPIAIJ and SEQAIJ.

830:    Level: beginner

832: .keywords: Mat, ResetPreallocation

834: .seealso: MatSeqAIJSetPreallocation(), MatMPIAIJSetPreallocation(), MatXAIJSetPreallocation()
835: @*/
836: PetscErrorCode MatResetPreallocation(Mat A)
837: {

843:   PetscUseMethod(A,"MatResetPreallocation_C",(Mat),(A));
844:   return(0);
845: }


848: /*@
849:    MatSetUp - Sets up the internal matrix data structures for the later use.

851:    Collective on Mat

853:    Input Parameters:
854: .  A - the Mat context

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

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

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

863:    Level: beginner

865: .keywords: Mat, setup

867: .seealso: MatCreate(), MatDestroy()
868: @*/
869: PetscErrorCode MatSetUp(Mat A)
870: {
871:   PetscMPIInt    size;

876:   if (!((PetscObject)A)->type_name) {
877:     MPI_Comm_size(PetscObjectComm((PetscObject)A), &size);
878:     if (size == 1) {
879:       MatSetType(A, MATSEQAIJ);
880:     } else {
881:       MatSetType(A, MATMPIAIJ);
882:     }
883:   }
884:   if (!A->preallocated && A->ops->setup) {
885:     PetscInfo(A,"Warning not preallocating matrix storage\n");
886:     (*A->ops->setup)(A);
887:   }
888:   PetscLayoutSetUp(A->rmap);
889:   PetscLayoutSetUp(A->cmap);
890:   A->preallocated = PETSC_TRUE;
891:   return(0);
892: }

894: #if defined(PETSC_HAVE_SAWS)
895:  #include <petscviewersaws.h>
896: #endif
897: /*@C
898:    MatView - Visualizes a matrix object.

900:    Collective on Mat

902:    Input Parameters:
903: +  mat - the matrix
904: -  viewer - visualization context

906:   Notes:
907:   The available visualization contexts include
908: +    PETSC_VIEWER_STDOUT_SELF - for sequential matrices
909: .    PETSC_VIEWER_STDOUT_WORLD - for parallel matrices created on PETSC_COMM_WORLD
910: .    PETSC_VIEWER_STDOUT_(comm) - for matrices created on MPI communicator comm
911: -     PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure

913:    The user can open alternative visualization contexts with
914: +    PetscViewerASCIIOpen() - Outputs matrix to a specified file
915: .    PetscViewerBinaryOpen() - Outputs matrix in binary to a
916:          specified file; corresponding input uses MatLoad()
917: .    PetscViewerDrawOpen() - Outputs nonzero matrix structure to
918:          an X window display
919: -    PetscViewerSocketOpen() - Outputs matrix to Socket viewer.
920:          Currently only the sequential dense and AIJ
921:          matrix types support the Socket viewer.

923:    The user can call PetscViewerPushFormat() to specify the output
924:    format of ASCII printed objects (when using PETSC_VIEWER_STDOUT_SELF,
925:    PETSC_VIEWER_STDOUT_WORLD and PetscViewerASCIIOpen).  Available formats include
926: +    PETSC_VIEWER_DEFAULT - default, prints matrix contents
927: .    PETSC_VIEWER_ASCII_MATLAB - prints matrix contents in Matlab format
928: .    PETSC_VIEWER_ASCII_DENSE - prints entire matrix including zeros
929: .    PETSC_VIEWER_ASCII_COMMON - prints matrix contents, using a sparse
930:          format common among all matrix types
931: .    PETSC_VIEWER_ASCII_IMPL - prints matrix contents, using an implementation-specific
932:          format (which is in many cases the same as the default)
933: .    PETSC_VIEWER_ASCII_INFO - prints basic information about the matrix
934:          size and structure (not the matrix entries)
935: .    PETSC_VIEWER_ASCII_INFO_DETAIL - prints more detailed information about
936:          the matrix structure

938:    Options Database Keys:
939: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of MatAssemblyEnd()
940: .  -mat_view ::ascii_info_detail - Prints more detailed info
941: .  -mat_view - Prints matrix in ASCII format
942: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
943: .  -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX().
944: .  -display <name> - Sets display name (default is host)
945: .  -draw_pause <sec> - Sets number of seconds to pause after display
946: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (see Users-Manual: Chapter 12 Using MATLAB with PETSc for details)
947: .  -viewer_socket_machine <machine> -
948: .  -viewer_socket_port <port> -
949: .  -mat_view binary - save matrix to file in binary format
950: -  -viewer_binary_filename <name> -
951:    Level: beginner

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

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

960:       One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure.
961:       And then use the following mouse functions:
962:           left mouse: zoom in
963:           middle mouse: zoom out
964:           right mouse: continue with the simulation

966:    Concepts: matrices^viewing
967:    Concepts: matrices^plotting
968:    Concepts: matrices^printing

970: .seealso: PetscViewerPushFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(),
971:           PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad()
972: @*/
973: PetscErrorCode MatView(Mat mat,PetscViewer viewer)
974: {
975:   PetscErrorCode    ierr;
976:   PetscInt          rows,cols,rbs,cbs;
977:   PetscBool         iascii,ibinary;
978:   PetscViewerFormat format;
979:   PetscMPIInt       size;
980: #if defined(PETSC_HAVE_SAWS)
981:   PetscBool         issaws;
982: #endif

987:   if (!viewer) {
988:     PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat),&viewer);
989:   }
992:   MatCheckPreallocated(mat,1);
993:   PetscViewerGetFormat(viewer,&format);
994:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
995:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) return(0);
996:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&ibinary);
997:   if (ibinary) {
998:     PetscBool mpiio;
999:     PetscViewerBinaryGetUseMPIIO(viewer,&mpiio);
1000:     if (mpiio) SETERRQ(PetscObjectComm((PetscObject)viewer),PETSC_ERR_SUP,"PETSc matrix viewers do not support using MPI-IO, turn off that flag");
1001:   }

1003:   PetscLogEventBegin(MAT_View,mat,viewer,0,0);
1004:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1005:   if ((!iascii || (format != PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) && mat->factortype) {
1006:     SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"No viewers for factored matrix except ASCII info or info_detailed");
1007:   }

1009: #if defined(PETSC_HAVE_SAWS)
1010:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSAWS,&issaws);
1011: #endif
1012:   if (iascii) {
1013:     if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix");
1014:     PetscObjectPrintClassNamePrefixType((PetscObject)mat,viewer);
1015:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1016:       MatNullSpace nullsp,transnullsp;

1018:       PetscViewerASCIIPushTab(viewer);
1019:       MatGetSize(mat,&rows,&cols);
1020:       MatGetBlockSizes(mat,&rbs,&cbs);
1021:       if (rbs != 1 || cbs != 1) {
1022:         if (rbs != cbs) {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, rbs=%D, cbs = %D\n",rows,cols,rbs,cbs);}
1023:         else            {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, bs=%D\n",rows,cols,rbs);}
1024:       } else {
1025:         PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D\n",rows,cols);
1026:       }
1027:       if (mat->factortype) {
1028:         MatSolverType solver;
1029:         MatFactorGetSolverType(mat,&solver);
1030:         PetscViewerASCIIPrintf(viewer,"package used to perform factorization: %s\n",solver);
1031:       }
1032:       if (mat->ops->getinfo) {
1033:         MatInfo info;
1034:         MatGetInfo(mat,MAT_GLOBAL_SUM,&info);
1035:         PetscViewerASCIIPrintf(viewer,"total: nonzeros=%.f, allocated nonzeros=%.f\n",info.nz_used,info.nz_allocated);
1036:         PetscViewerASCIIPrintf(viewer,"total number of mallocs used during MatSetValues calls =%D\n",(PetscInt)info.mallocs);
1037:       }
1038:       MatGetNullSpace(mat,&nullsp);
1039:       MatGetTransposeNullSpace(mat,&transnullsp);
1040:       if (nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached null space\n");}
1041:       if (transnullsp && transnullsp != nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached transposed null space\n");}
1042:       MatGetNearNullSpace(mat,&nullsp);
1043:       if (nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached near null space\n");}
1044:     }
1045: #if defined(PETSC_HAVE_SAWS)
1046:   } else if (issaws) {
1047:     PetscMPIInt rank;

1049:     PetscObjectName((PetscObject)mat);
1050:     MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
1051:     if (!((PetscObject)mat)->amsmem && !rank) {
1052:       PetscObjectViewSAWs((PetscObject)mat,viewer);
1053:     }
1054: #endif
1055:   }
1056:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1057:     PetscViewerASCIIPushTab(viewer);
1058:     (*mat->ops->viewnative)(mat,viewer);
1059:     PetscViewerASCIIPopTab(viewer);
1060:   } else if (mat->ops->view) {
1061:     PetscViewerASCIIPushTab(viewer);
1062:     (*mat->ops->view)(mat,viewer);
1063:     PetscViewerASCIIPopTab(viewer);
1064:   }
1065:   if (iascii) {
1066:     if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix");
1067:     PetscViewerGetFormat(viewer,&format);
1068:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1069:       PetscViewerASCIIPopTab(viewer);
1070:     }
1071:   }
1072:   PetscLogEventEnd(MAT_View,mat,viewer,0,0);
1073:   return(0);
1074: }

1076: #if defined(PETSC_USE_DEBUG)
1077: #include <../src/sys/totalview/tv_data_display.h>
1078: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1079: {
1080:   TV_add_row("Local rows", "int", &mat->rmap->n);
1081:   TV_add_row("Local columns", "int", &mat->cmap->n);
1082:   TV_add_row("Global rows", "int", &mat->rmap->N);
1083:   TV_add_row("Global columns", "int", &mat->cmap->N);
1084:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1085:   return TV_format_OK;
1086: }
1087: #endif

1089: /*@C
1090:    MatLoad - Loads a matrix that has been stored in binary format
1091:    with MatView().  The matrix format is determined from the options database.
1092:    Generates a parallel MPI matrix if the communicator has more than one
1093:    processor.  The default matrix type is AIJ.

1095:    Collective on PetscViewer

1097:    Input Parameters:
1098: +  newmat - the newly loaded matrix, this needs to have been created with MatCreate()
1099:             or some related function before a call to MatLoad()
1100: -  viewer - binary file viewer, created with PetscViewerBinaryOpen()

1102:    Options Database Keys:
1103:    Used with block matrix formats (MATSEQBAIJ,  ...) to specify
1104:    block size
1105: .    -matload_block_size <bs>

1107:    Level: beginner

1109:    Notes:
1110:    If the Mat type has not yet been given then MATAIJ is used, call MatSetFromOptions() on the
1111:    Mat before calling this routine if you wish to set it from the options database.

1113:    MatLoad() automatically loads into the options database any options
1114:    given in the file filename.info where filename is the name of the file
1115:    that was passed to the PetscViewerBinaryOpen(). The options in the info
1116:    file will be ignored if you use the -viewer_binary_skip_info option.

1118:    If the type or size of newmat is not set before a call to MatLoad, PETSc
1119:    sets the default matrix type AIJ and sets the local and global sizes.
1120:    If type and/or size is already set, then the same are used.

1122:    In parallel, each processor can load a subset of rows (or the
1123:    entire matrix).  This routine is especially useful when a large
1124:    matrix is stored on disk and only part of it is desired on each
1125:    processor.  For example, a parallel solver may access only some of
1126:    the rows from each processor.  The algorithm used here reads
1127:    relatively small blocks of data rather than reading the entire
1128:    matrix and then subsetting it.

1130:    Notes for advanced users:
1131:    Most users should not need to know the details of the binary storage
1132:    format, since MatLoad() and MatView() completely hide these details.
1133:    But for anyone who's interested, the standard binary matrix storage
1134:    format is

1136: $    int    MAT_FILE_CLASSID
1137: $    int    number of rows
1138: $    int    number of columns
1139: $    int    total number of nonzeros
1140: $    int    *number nonzeros in each row
1141: $    int    *column indices of all nonzeros (starting index is zero)
1142: $    PetscScalar *values of all nonzeros

1144:    PETSc automatically does the byte swapping for
1145: machines that store the bytes reversed, e.g.  DEC alpha, freebsd,
1146: linux, Windows and the paragon; thus if you write your own binary
1147: read/write routines you have to swap the bytes; see PetscBinaryRead()
1148: and PetscBinaryWrite() to see how this may be done.

1150: .keywords: matrix, load, binary, input

1152: .seealso: PetscViewerBinaryOpen(), MatView(), VecLoad()

1154:  @*/
1155: PetscErrorCode MatLoad(Mat newmat,PetscViewer viewer)
1156: {
1158:   PetscBool      isbinary,flg;

1163:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1164:   if (!isbinary) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid viewer; open viewer with PetscViewerBinaryOpen()");

1166:   if (!((PetscObject)newmat)->type_name) {
1167:     MatSetType(newmat,MATAIJ);
1168:   }

1170:   if (!newmat->ops->load) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatLoad is not supported for type");
1171:   PetscLogEventBegin(MAT_Load,viewer,0,0,0);
1172:   (*newmat->ops->load)(newmat,viewer);
1173:   PetscLogEventEnd(MAT_Load,viewer,0,0,0);

1175:   flg  = PETSC_FALSE;
1176:   PetscOptionsGetBool(((PetscObject)newmat)->options,((PetscObject)newmat)->prefix,"-matload_symmetric",&flg,NULL);
1177:   if (flg) {
1178:     MatSetOption(newmat,MAT_SYMMETRIC,PETSC_TRUE);
1179:     MatSetOption(newmat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE);
1180:   }
1181:   flg  = PETSC_FALSE;
1182:   PetscOptionsGetBool(((PetscObject)newmat)->options,((PetscObject)newmat)->prefix,"-matload_spd",&flg,NULL);
1183:   if (flg) {
1184:     MatSetOption(newmat,MAT_SPD,PETSC_TRUE);
1185:   }
1186:   return(0);
1187: }

1189: PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1190: {
1192:   Mat_Redundant  *redund = *redundant;
1193:   PetscInt       i;

1196:   if (redund){
1197:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1198:       ISDestroy(&redund->isrow);
1199:       ISDestroy(&redund->iscol);
1200:       MatDestroySubMatrices(1,&redund->matseq);
1201:     } else {
1202:       PetscFree2(redund->send_rank,redund->recv_rank);
1203:       PetscFree(redund->sbuf_j);
1204:       PetscFree(redund->sbuf_a);
1205:       for (i=0; i<redund->nrecvs; i++) {
1206:         PetscFree(redund->rbuf_j[i]);
1207:         PetscFree(redund->rbuf_a[i]);
1208:       }
1209:       PetscFree4(redund->sbuf_nz,redund->rbuf_nz,redund->rbuf_j,redund->rbuf_a);
1210:     }

1212:     if (redund->subcomm) {
1213:       PetscCommDestroy(&redund->subcomm);
1214:     }
1215:     PetscFree(redund);
1216:   }
1217:   return(0);
1218: }

1220: /*@
1221:    MatDestroy - Frees space taken by a matrix.

1223:    Collective on Mat

1225:    Input Parameter:
1226: .  A - the matrix

1228:    Level: beginner

1230: @*/
1231: PetscErrorCode MatDestroy(Mat *A)
1232: {

1236:   if (!*A) return(0);
1238:   if (--((PetscObject)(*A))->refct > 0) {*A = NULL; return(0);}

1240:   /* if memory was published with SAWs then destroy it */
1241:   PetscObjectSAWsViewOff((PetscObject)*A);
1242:   if ((*A)->ops->destroy) {
1243:     (*(*A)->ops->destroy)(*A);
1244:   }

1246:   PetscFree((*A)->defaultvectype);
1247:   PetscFree((*A)->bsizes);
1248:   PetscFree((*A)->solvertype);
1249:   MatDestroy_Redundant(&(*A)->redundant);
1250:   MatNullSpaceDestroy(&(*A)->nullsp);
1251:   MatNullSpaceDestroy(&(*A)->transnullsp);
1252:   MatNullSpaceDestroy(&(*A)->nearnullsp);
1253:   MatDestroy(&(*A)->schur);
1254:   PetscLayoutDestroy(&(*A)->rmap);
1255:   PetscLayoutDestroy(&(*A)->cmap);
1256:   PetscHeaderDestroy(A);
1257:   return(0);
1258: }

1260: /*@C
1261:    MatSetValues - Inserts or adds a block of values into a matrix.
1262:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
1263:    MUST be called after all calls to MatSetValues() have been completed.

1265:    Not Collective

1267:    Input Parameters:
1268: +  mat - the matrix
1269: .  v - a logically two-dimensional array of values
1270: .  m, idxm - the number of rows and their global indices
1271: .  n, idxn - the number of columns and their global indices
1272: -  addv - either ADD_VALUES or INSERT_VALUES, where
1273:    ADD_VALUES adds values to any existing entries, and
1274:    INSERT_VALUES replaces existing entries with new values

1276:    Notes:
1277:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
1278:       MatSetUp() before using this routine

1280:    By default the values, v, are row-oriented. See MatSetOption() for other options.

1282:    Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES
1283:    options cannot be mixed without intervening calls to the assembly
1284:    routines.

1286:    MatSetValues() uses 0-based row and column numbers in Fortran
1287:    as well as in C.

1289:    Negative indices may be passed in idxm and idxn, these rows and columns are
1290:    simply ignored. This allows easily inserting element stiffness matrices
1291:    with homogeneous Dirchlet boundary conditions that you don't want represented
1292:    in the matrix.

1294:    Efficiency Alert:
1295:    The routine MatSetValuesBlocked() may offer much better efficiency
1296:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1298:    Level: beginner

1300:    Developer Notes:
1301:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
1302:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1304:    Concepts: matrices^putting entries in

1306: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1307:           InsertMode, INSERT_VALUES, ADD_VALUES
1308: @*/
1309: PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1310: {
1312: #if defined(PETSC_USE_DEBUG)
1313:   PetscInt       i,j;
1314: #endif

1319:   if (!m || !n) return(0); /* no values to insert */
1323:   MatCheckPreallocated(mat,1);
1324:   if (mat->insertmode == NOT_SET_VALUES) {
1325:     mat->insertmode = addv;
1326:   }
1327: #if defined(PETSC_USE_DEBUG)
1328:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1329:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1330:   if (!mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);

1332:   for (i=0; i<m; i++) {
1333:     for (j=0; j<n; j++) {
1334:       if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i*n+j]))
1335: #if defined(PETSC_USE_COMPLEX)
1336:         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]);
1337: #else
1338:         SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %g at matrix entry (%D,%D)",(double)v[i*n+j],idxm[i],idxn[j]);
1339: #endif
1340:     }
1341:   }
1342: #endif

1344:   if (mat->assembled) {
1345:     mat->was_assembled = PETSC_TRUE;
1346:     mat->assembled     = PETSC_FALSE;
1347:   }
1348:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1349:   (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);
1350:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1351: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
1352:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
1353:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
1354:   }
1355: #endif
1356:   return(0);
1357: }


1360: /*@
1361:    MatSetValuesRowLocal - Inserts a row (block row for BAIJ matrices) of nonzero
1362:         values into a matrix

1364:    Not Collective

1366:    Input Parameters:
1367: +  mat - the matrix
1368: .  row - the (block) row to set
1369: -  v - a logically two-dimensional array of values

1371:    Notes:
1372:    By the values, v, are column-oriented (for the block version) and sorted

1374:    All the nonzeros in the row must be provided

1376:    The matrix must have previously had its column indices set

1378:    The row must belong to this process

1380:    Level: intermediate

1382:    Concepts: matrices^putting entries in

1384: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1385:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues(), MatSetValuesRow(), MatSetLocalToGlobalMapping()
1386: @*/
1387: PetscErrorCode MatSetValuesRowLocal(Mat mat,PetscInt row,const PetscScalar v[])
1388: {
1390:   PetscInt       globalrow;

1396:   ISLocalToGlobalMappingApply(mat->rmap->mapping,1,&row,&globalrow);
1397:   MatSetValuesRow(mat,globalrow,v);
1398: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
1399:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
1400:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
1401:   }
1402: #endif
1403:   return(0);
1404: }

1406: /*@
1407:    MatSetValuesRow - Inserts a row (block row for BAIJ matrices) of nonzero
1408:         values into a matrix

1410:    Not Collective

1412:    Input Parameters:
1413: +  mat - the matrix
1414: .  row - the (block) row to set
1415: -  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

1417:    Notes:
1418:    The values, v, are column-oriented for the block version.

1420:    All the nonzeros in the row must be provided

1422:    THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually MatSetValues() is used.

1424:    The row must belong to this process

1426:    Level: advanced

1428:    Concepts: matrices^putting entries in

1430: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1431:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
1432: @*/
1433: PetscErrorCode MatSetValuesRow(Mat mat,PetscInt row,const PetscScalar v[])
1434: {

1440:   MatCheckPreallocated(mat,1);
1442: #if defined(PETSC_USE_DEBUG)
1443:   if (mat->insertmode == ADD_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add and insert values");
1444:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1445: #endif
1446:   mat->insertmode = INSERT_VALUES;

1448:   if (mat->assembled) {
1449:     mat->was_assembled = PETSC_TRUE;
1450:     mat->assembled     = PETSC_FALSE;
1451:   }
1452:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1453:   if (!mat->ops->setvaluesrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1454:   (*mat->ops->setvaluesrow)(mat,row,v);
1455:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1456: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
1457:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
1458:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
1459:   }
1460: #endif
1461:   return(0);
1462: }

1464: /*@
1465:    MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1466:      Using structured grid indexing

1468:    Not Collective

1470:    Input Parameters:
1471: +  mat - the matrix
1472: .  m - number of rows being entered
1473: .  idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1474: .  n - number of columns being entered
1475: .  idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1476: .  v - a logically two-dimensional array of values
1477: -  addv - either ADD_VALUES or INSERT_VALUES, where
1478:    ADD_VALUES adds values to any existing entries, and
1479:    INSERT_VALUES replaces existing entries with new values

1481:    Notes:
1482:    By default the values, v, are row-oriented.  See MatSetOption() for other options.

1484:    Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES
1485:    options cannot be mixed without intervening calls to the assembly
1486:    routines.

1488:    The grid coordinates are across the entire grid, not just the local portion

1490:    MatSetValuesStencil() uses 0-based row and column numbers in Fortran
1491:    as well as in C.

1493:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1495:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1496:    or call MatSetLocalToGlobalMapping() and MatSetStencil() first.

1498:    The columns and rows in the stencil passed in MUST be contained within the
1499:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1500:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1501:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1502:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1504:    In Fortran idxm and idxn should be declared as
1505: $     MatStencil idxm(4,m),idxn(4,n)
1506:    and the values inserted using
1507: $    idxm(MatStencil_i,1) = i
1508: $    idxm(MatStencil_j,1) = j
1509: $    idxm(MatStencil_k,1) = k
1510: $    idxm(MatStencil_c,1) = c
1511:    etc

1513:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1514:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1515:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1516:    DM_BOUNDARY_PERIODIC boundary type.

1518:    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
1519:    a single value per point) you can skip filling those indices.

1521:    Inspired by the structured grid interface to the HYPRE package
1522:    (http://www.llnl.gov/CASC/hypre)

1524:    Efficiency Alert:
1525:    The routine MatSetValuesBlockedStencil() may offer much better efficiency
1526:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1528:    Level: beginner

1530:    Concepts: matrices^putting entries in

1532: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1533:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil
1534: @*/
1535: PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1536: {
1538:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1539:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1540:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1543:   if (!m || !n) return(0); /* no values to insert */

1550:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1551:     jdxm = buf; jdxn = buf+m;
1552:   } else {
1553:     PetscMalloc2(m,&bufm,n,&bufn);
1554:     jdxm = bufm; jdxn = bufn;
1555:   }
1556:   for (i=0; i<m; i++) {
1557:     for (j=0; j<3-sdim; j++) dxm++;
1558:     tmp = *dxm++ - starts[0];
1559:     for (j=0; j<dim-1; j++) {
1560:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1561:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1562:     }
1563:     if (mat->stencil.noc) dxm++;
1564:     jdxm[i] = tmp;
1565:   }
1566:   for (i=0; i<n; i++) {
1567:     for (j=0; j<3-sdim; j++) dxn++;
1568:     tmp = *dxn++ - starts[0];
1569:     for (j=0; j<dim-1; j++) {
1570:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1571:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1572:     }
1573:     if (mat->stencil.noc) dxn++;
1574:     jdxn[i] = tmp;
1575:   }
1576:   MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);
1577:   PetscFree2(bufm,bufn);
1578:   return(0);
1579: }

1581: /*@
1582:    MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1583:      Using structured grid indexing

1585:    Not Collective

1587:    Input Parameters:
1588: +  mat - the matrix
1589: .  m - number of rows being entered
1590: .  idxm - grid coordinates for matrix rows being entered
1591: .  n - number of columns being entered
1592: .  idxn - grid coordinates for matrix columns being entered
1593: .  v - a logically two-dimensional array of values
1594: -  addv - either ADD_VALUES or INSERT_VALUES, where
1595:    ADD_VALUES adds values to any existing entries, and
1596:    INSERT_VALUES replaces existing entries with new values

1598:    Notes:
1599:    By default the values, v, are row-oriented and unsorted.
1600:    See MatSetOption() for other options.

1602:    Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES
1603:    options cannot be mixed without intervening calls to the assembly
1604:    routines.

1606:    The grid coordinates are across the entire grid, not just the local portion

1608:    MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran
1609:    as well as in C.

1611:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1613:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1614:    or call MatSetBlockSize(), MatSetLocalToGlobalMapping() and MatSetStencil() first.

1616:    The columns and rows in the stencil passed in MUST be contained within the
1617:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1618:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1619:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1620:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1622:    In Fortran idxm and idxn should be declared as
1623: $     MatStencil idxm(4,m),idxn(4,n)
1624:    and the values inserted using
1625: $    idxm(MatStencil_i,1) = i
1626: $    idxm(MatStencil_j,1) = j
1627: $    idxm(MatStencil_k,1) = k
1628:    etc

1630:    Negative indices may be passed in idxm and idxn, these rows and columns are
1631:    simply ignored. This allows easily inserting element stiffness matrices
1632:    with homogeneous Dirchlet boundary conditions that you don't want represented
1633:    in the matrix.

1635:    Inspired by the structured grid interface to the HYPRE package
1636:    (http://www.llnl.gov/CASC/hypre)

1638:    Level: beginner

1640:    Concepts: matrices^putting entries in

1642: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1643:           MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil,
1644:           MatSetBlockSize(), MatSetLocalToGlobalMapping()
1645: @*/
1646: PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1647: {
1649:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1650:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1651:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1654:   if (!m || !n) return(0); /* no values to insert */

1661:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1662:     jdxm = buf; jdxn = buf+m;
1663:   } else {
1664:     PetscMalloc2(m,&bufm,n,&bufn);
1665:     jdxm = bufm; jdxn = bufn;
1666:   }
1667:   for (i=0; i<m; i++) {
1668:     for (j=0; j<3-sdim; j++) dxm++;
1669:     tmp = *dxm++ - starts[0];
1670:     for (j=0; j<sdim-1; j++) {
1671:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1672:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1673:     }
1674:     dxm++;
1675:     jdxm[i] = tmp;
1676:   }
1677:   for (i=0; i<n; i++) {
1678:     for (j=0; j<3-sdim; j++) dxn++;
1679:     tmp = *dxn++ - starts[0];
1680:     for (j=0; j<sdim-1; j++) {
1681:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1682:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1683:     }
1684:     dxn++;
1685:     jdxn[i] = tmp;
1686:   }
1687:   MatSetValuesBlockedLocal(mat,m,jdxm,n,jdxn,v,addv);
1688:   PetscFree2(bufm,bufn);
1689: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
1690:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
1691:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
1692:   }
1693: #endif
1694:   return(0);
1695: }

1697: /*@
1698:    MatSetStencil - Sets the grid information for setting values into a matrix via
1699:         MatSetValuesStencil()

1701:    Not Collective

1703:    Input Parameters:
1704: +  mat - the matrix
1705: .  dim - dimension of the grid 1, 2, or 3
1706: .  dims - number of grid points in x, y, and z direction, including ghost points on your processor
1707: .  starts - starting point of ghost nodes on your processor in x, y, and z direction
1708: -  dof - number of degrees of freedom per node


1711:    Inspired by the structured grid interface to the HYPRE package
1712:    (www.llnl.gov/CASC/hyper)

1714:    For matrices generated with DMCreateMatrix() this routine is automatically called and so not needed by the
1715:    user.

1717:    Level: beginner

1719:    Concepts: matrices^putting entries in

1721: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1722:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil()
1723: @*/
1724: PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof)
1725: {
1726:   PetscInt i;


1733:   mat->stencil.dim = dim + (dof > 1);
1734:   for (i=0; i<dim; i++) {
1735:     mat->stencil.dims[i]   = dims[dim-i-1];      /* copy the values in backwards */
1736:     mat->stencil.starts[i] = starts[dim-i-1];
1737:   }
1738:   mat->stencil.dims[dim]   = dof;
1739:   mat->stencil.starts[dim] = 0;
1740:   mat->stencil.noc         = (PetscBool)(dof == 1);
1741:   return(0);
1742: }

1744: /*@C
1745:    MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1747:    Not Collective

1749:    Input Parameters:
1750: +  mat - the matrix
1751: .  v - a logically two-dimensional array of values
1752: .  m, idxm - the number of block rows and their global block indices
1753: .  n, idxn - the number of block columns and their global block indices
1754: -  addv - either ADD_VALUES or INSERT_VALUES, where
1755:    ADD_VALUES adds values to any existing entries, and
1756:    INSERT_VALUES replaces existing entries with new values

1758:    Notes:
1759:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call
1760:    MatXXXXSetPreallocation() or MatSetUp() before using this routine.

1762:    The m and n count the NUMBER of blocks in the row direction and column direction,
1763:    NOT the total number of rows/columns; for example, if the block size is 2 and
1764:    you are passing in values for rows 2,3,4,5  then m would be 2 (not 4).
1765:    The values in idxm would be 1 2; that is the first index for each block divided by
1766:    the block size.

1768:    Note that you must call MatSetBlockSize() when constructing this matrix (before
1769:    preallocating it).

1771:    By default the values, v, are row-oriented, so the layout of
1772:    v is the same as for MatSetValues(). See MatSetOption() for other options.

1774:    Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES
1775:    options cannot be mixed without intervening calls to the assembly
1776:    routines.

1778:    MatSetValuesBlocked() uses 0-based row and column numbers in Fortran
1779:    as well as in C.

1781:    Negative indices may be passed in idxm and idxn, these rows and columns are
1782:    simply ignored. This allows easily inserting element stiffness matrices
1783:    with homogeneous Dirchlet boundary conditions that you don't want represented
1784:    in the matrix.

1786:    Each time an entry is set within a sparse matrix via MatSetValues(),
1787:    internal searching must be done to determine where to place the
1788:    data in the matrix storage space.  By instead inserting blocks of
1789:    entries via MatSetValuesBlocked(), the overhead of matrix assembly is
1790:    reduced.

1792:    Example:
1793: $   Suppose m=n=2 and block size(bs) = 2 The array is
1794: $
1795: $   1  2  | 3  4
1796: $   5  6  | 7  8
1797: $   - - - | - - -
1798: $   9  10 | 11 12
1799: $   13 14 | 15 16
1800: $
1801: $   v[] should be passed in like
1802: $   v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
1803: $
1804: $  If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1805: $   v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]

1807:    Level: intermediate

1809:    Concepts: matrices^putting entries in blocked

1811: .seealso: MatSetBlockSize(), MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal()
1812: @*/
1813: PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1814: {

1820:   if (!m || !n) return(0); /* no values to insert */
1824:   MatCheckPreallocated(mat,1);
1825:   if (mat->insertmode == NOT_SET_VALUES) {
1826:     mat->insertmode = addv;
1827:   }
1828: #if defined(PETSC_USE_DEBUG)
1829:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1830:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1831:   if (!mat->ops->setvaluesblocked && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1832: #endif

1834:   if (mat->assembled) {
1835:     mat->was_assembled = PETSC_TRUE;
1836:     mat->assembled     = PETSC_FALSE;
1837:   }
1838:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1839:   if (mat->ops->setvaluesblocked) {
1840:     (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);
1841:   } else {
1842:     PetscInt buf[8192],*bufr=0,*bufc=0,*iidxm,*iidxn;
1843:     PetscInt i,j,bs,cbs;
1844:     MatGetBlockSizes(mat,&bs,&cbs);
1845:     if (m*bs+n*cbs <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1846:       iidxm = buf; iidxn = buf + m*bs;
1847:     } else {
1848:       PetscMalloc2(m*bs,&bufr,n*cbs,&bufc);
1849:       iidxm = bufr; iidxn = bufc;
1850:     }
1851:     for (i=0; i<m; i++) {
1852:       for (j=0; j<bs; j++) {
1853:         iidxm[i*bs+j] = bs*idxm[i] + j;
1854:       }
1855:     }
1856:     for (i=0; i<n; i++) {
1857:       for (j=0; j<cbs; j++) {
1858:         iidxn[i*cbs+j] = cbs*idxn[i] + j;
1859:       }
1860:     }
1861:     MatSetValues(mat,m*bs,iidxm,n*cbs,iidxn,v,addv);
1862:     PetscFree2(bufr,bufc);
1863:   }
1864:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1865: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
1866:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
1867:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
1868:   }
1869: #endif
1870:   return(0);
1871: }

1873: /*@
1874:    MatGetValues - Gets a block of values from a matrix.

1876:    Not Collective; currently only returns a local block

1878:    Input Parameters:
1879: +  mat - the matrix
1880: .  v - a logically two-dimensional array for storing the values
1881: .  m, idxm - the number of rows and their global indices
1882: -  n, idxn - the number of columns and their global indices

1884:    Notes:
1885:    The user must allocate space (m*n PetscScalars) for the values, v.
1886:    The values, v, are then returned in a row-oriented format,
1887:    analogous to that used by default in MatSetValues().

1889:    MatGetValues() uses 0-based row and column numbers in
1890:    Fortran as well as in C.

1892:    MatGetValues() requires that the matrix has been assembled
1893:    with MatAssemblyBegin()/MatAssemblyEnd().  Thus, calls to
1894:    MatSetValues() and MatGetValues() CANNOT be made in succession
1895:    without intermediate matrix assembly.

1897:    Negative row or column indices will be ignored and those locations in v[] will be
1898:    left unchanged.

1900:    Level: advanced

1902:    Concepts: matrices^accessing values

1904: .seealso: MatGetRow(), MatCreateSubMatrices(), MatSetValues()
1905: @*/
1906: PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
1907: {

1913:   if (!m || !n) return(0);
1917:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
1918:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1919:   if (!mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1920:   MatCheckPreallocated(mat,1);

1922:   PetscLogEventBegin(MAT_GetValues,mat,0,0,0);
1923:   (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);
1924:   PetscLogEventEnd(MAT_GetValues,mat,0,0,0);
1925:   return(0);
1926: }

1928: /*@
1929:   MatSetValuesBatch - Adds (ADD_VALUES) many blocks of values into a matrix at once. The blocks must all be square and
1930:   the same size. Currently, this can only be called once and creates the given matrix.

1932:   Not Collective

1934:   Input Parameters:
1935: + mat - the matrix
1936: . nb - the number of blocks
1937: . bs - the number of rows (and columns) in each block
1938: . rows - a concatenation of the rows for each block
1939: - v - a concatenation of logically two-dimensional arrays of values

1941:   Notes:
1942:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

1944:   Level: advanced

1946:   Concepts: matrices^putting entries in

1948: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1949:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
1950: @*/
1951: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
1952: {

1960: #if defined(PETSC_USE_DEBUG)
1961:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1962: #endif

1964:   PetscLogEventBegin(MAT_SetValuesBatch,mat,0,0,0);
1965:   if (mat->ops->setvaluesbatch) {
1966:     (*mat->ops->setvaluesbatch)(mat,nb,bs,rows,v);
1967:   } else {
1968:     PetscInt b;
1969:     for (b = 0; b < nb; ++b) {
1970:       MatSetValues(mat, bs, &rows[b*bs], bs, &rows[b*bs], &v[b*bs*bs], ADD_VALUES);
1971:     }
1972:   }
1973:   PetscLogEventEnd(MAT_SetValuesBatch,mat,0,0,0);
1974:   return(0);
1975: }

1977: /*@
1978:    MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
1979:    the routine MatSetValuesLocal() to allow users to insert matrix entries
1980:    using a local (per-processor) numbering.

1982:    Not Collective

1984:    Input Parameters:
1985: +  x - the matrix
1986: .  rmapping - row mapping created with ISLocalToGlobalMappingCreate()   or ISLocalToGlobalMappingCreateIS()
1987: - cmapping - column mapping

1989:    Level: intermediate

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

1994: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal()
1995: @*/
1996: PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping)
1997: {


2006:   if (x->ops->setlocaltoglobalmapping) {
2007:     (*x->ops->setlocaltoglobalmapping)(x,rmapping,cmapping);
2008:   } else {
2009:     PetscLayoutSetISLocalToGlobalMapping(x->rmap,rmapping);
2010:     PetscLayoutSetISLocalToGlobalMapping(x->cmap,cmapping);
2011:   }
2012:   return(0);
2013: }


2016: /*@
2017:    MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by MatSetLocalToGlobalMapping()

2019:    Not Collective

2021:    Input Parameters:
2022: .  A - the matrix

2024:    Output Parameters:
2025: + rmapping - row mapping
2026: - cmapping - column mapping

2028:    Level: advanced

2030:    Concepts: matrices^local to global mapping
2031:    Concepts: local to global mapping^for matrices

2033: .seealso:  MatSetValuesLocal()
2034: @*/
2035: PetscErrorCode MatGetLocalToGlobalMapping(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping)
2036: {
2042:   if (rmapping) *rmapping = A->rmap->mapping;
2043:   if (cmapping) *cmapping = A->cmap->mapping;
2044:   return(0);
2045: }

2047: /*@
2048:    MatGetLayouts - Gets the PetscLayout objects for rows and columns

2050:    Not Collective

2052:    Input Parameters:
2053: .  A - the matrix

2055:    Output Parameters:
2056: + rmap - row layout
2057: - cmap - column layout

2059:    Level: advanced

2061: .seealso:  MatCreateVecs(), MatGetLocalToGlobalMapping()
2062: @*/
2063: PetscErrorCode MatGetLayouts(Mat A,PetscLayout *rmap,PetscLayout *cmap)
2064: {
2070:   if (rmap) *rmap = A->rmap;
2071:   if (cmap) *cmap = A->cmap;
2072:   return(0);
2073: }

2075: /*@C
2076:    MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2077:    using a local ordering of the nodes.

2079:    Not Collective

2081:    Input Parameters:
2082: +  mat - the matrix
2083: .  nrow, irow - number of rows and their local indices
2084: .  ncol, icol - number of columns and their local indices
2085: .  y -  a logically two-dimensional array of values
2086: -  addv - either INSERT_VALUES or ADD_VALUES, where
2087:    ADD_VALUES adds values to any existing entries, and
2088:    INSERT_VALUES replaces existing entries with new values

2090:    Notes:
2091:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2092:       MatSetUp() before using this routine

2094:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine

2096:    Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES
2097:    options cannot be mixed without intervening calls to the assembly
2098:    routines.

2100:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2101:    MUST be called after all calls to MatSetValuesLocal() have been completed.

2103:    Level: intermediate

2105:    Concepts: matrices^putting entries in with local numbering

2107:    Developer Notes:
2108:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2109:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2111: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(),
2112:            MatSetValueLocal()
2113: @*/
2114: PetscErrorCode MatSetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv)
2115: {

2121:   MatCheckPreallocated(mat,1);
2122:   if (!nrow || !ncol) return(0); /* no values to insert */
2126:   if (mat->insertmode == NOT_SET_VALUES) {
2127:     mat->insertmode = addv;
2128:   }
2129: #if defined(PETSC_USE_DEBUG)
2130:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2131:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2132:   if (!mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2133: #endif

2135:   if (mat->assembled) {
2136:     mat->was_assembled = PETSC_TRUE;
2137:     mat->assembled     = PETSC_FALSE;
2138:   }
2139:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2140:   if (mat->ops->setvalueslocal) {
2141:     (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);
2142:   } else {
2143:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2144:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2145:       irowm = buf; icolm = buf+nrow;
2146:     } else {
2147:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2148:       irowm = bufr; icolm = bufc;
2149:     }
2150:     ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);
2151:     ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);
2152:     MatSetValues(mat,nrow,irowm,ncol,icolm,y,addv);
2153:     PetscFree2(bufr,bufc);
2154:   }
2155:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2156: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
2157:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
2158:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
2159:   }
2160: #endif
2161:   return(0);
2162: }

2164: /*@C
2165:    MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2166:    using a local ordering of the nodes a block at a time.

2168:    Not Collective

2170:    Input Parameters:
2171: +  x - the matrix
2172: .  nrow, irow - number of rows and their local indices
2173: .  ncol, icol - number of columns and their local indices
2174: .  y -  a logically two-dimensional array of values
2175: -  addv - either INSERT_VALUES or ADD_VALUES, where
2176:    ADD_VALUES adds values to any existing entries, and
2177:    INSERT_VALUES replaces existing entries with new values

2179:    Notes:
2180:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2181:       MatSetUp() before using this routine

2183:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetBlockSize() and MatSetLocalToGlobalMapping()
2184:       before using this routineBefore calling MatSetValuesLocal(), the user must first set the

2186:    Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES
2187:    options cannot be mixed without intervening calls to the assembly
2188:    routines.

2190:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2191:    MUST be called after all calls to MatSetValuesBlockedLocal() have been completed.

2193:    Level: intermediate

2195:    Developer Notes:
2196:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2197:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2199:    Concepts: matrices^putting blocked values in with local numbering

2201: .seealso:  MatSetBlockSize(), MatSetLocalToGlobalMapping(), MatAssemblyBegin(), MatAssemblyEnd(),
2202:            MatSetValuesLocal(),  MatSetValuesBlocked()
2203: @*/
2204: PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv)
2205: {

2211:   MatCheckPreallocated(mat,1);
2212:   if (!nrow || !ncol) return(0); /* no values to insert */
2216:   if (mat->insertmode == NOT_SET_VALUES) {
2217:     mat->insertmode = addv;
2218:   }
2219: #if defined(PETSC_USE_DEBUG)
2220:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2221:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2222:   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);
2223: #endif

2225:   if (mat->assembled) {
2226:     mat->was_assembled = PETSC_TRUE;
2227:     mat->assembled     = PETSC_FALSE;
2228:   }
2229:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2230:   if (mat->ops->setvaluesblockedlocal) {
2231:     (*mat->ops->setvaluesblockedlocal)(mat,nrow,irow,ncol,icol,y,addv);
2232:   } else {
2233:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2234:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2235:       irowm = buf; icolm = buf + nrow;
2236:     } else {
2237:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2238:       irowm = bufr; icolm = bufc;
2239:     }
2240:     ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping,nrow,irow,irowm);
2241:     ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping,ncol,icol,icolm);
2242:     MatSetValuesBlocked(mat,nrow,irowm,ncol,icolm,y,addv);
2243:     PetscFree2(bufr,bufc);
2244:   }
2245:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2246: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
2247:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
2248:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
2249:   }
2250: #endif
2251:   return(0);
2252: }

2254: /*@
2255:    MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal

2257:    Collective on Mat and Vec

2259:    Input Parameters:
2260: +  mat - the matrix
2261: -  x   - the vector to be multiplied

2263:    Output Parameters:
2264: .  y - the result

2266:    Notes:
2267:    The vectors x and y cannot be the same.  I.e., one cannot
2268:    call MatMult(A,y,y).

2270:    Level: developer

2272:    Concepts: matrix-vector product

2274: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2275: @*/
2276: PetscErrorCode MatMultDiagonalBlock(Mat mat,Vec x,Vec y)
2277: {


2286:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2287:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2288:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2289:   MatCheckPreallocated(mat,1);

2291:   if (!mat->ops->multdiagonalblock) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined");
2292:   (*mat->ops->multdiagonalblock)(mat,x,y);
2293:   PetscObjectStateIncrease((PetscObject)y);
2294:   return(0);
2295: }

2297: /* --------------------------------------------------------*/
2298: /*@
2299:    MatMult - Computes the matrix-vector product, y = Ax.

2301:    Neighbor-wise Collective on Mat and Vec

2303:    Input Parameters:
2304: +  mat - the matrix
2305: -  x   - the vector to be multiplied

2307:    Output Parameters:
2308: .  y - the result

2310:    Notes:
2311:    The vectors x and y cannot be the same.  I.e., one cannot
2312:    call MatMult(A,y,y).

2314:    Level: beginner

2316:    Concepts: matrix-vector product

2318: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2319: @*/
2320: PetscErrorCode MatMult(Mat mat,Vec x,Vec y)
2321: {

2329:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2330:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2331:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2332: #if !defined(PETSC_HAVE_CONSTRAINTS)
2333:   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);
2334:   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);
2335:   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);
2336: #endif
2337:   VecLocked(y,3);
2338:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2339:   MatCheckPreallocated(mat,1);

2341:   VecLockPush(x);
2342:   if (!mat->ops->mult) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined");
2343:   PetscLogEventBegin(MAT_Mult,mat,x,y,0);
2344:   (*mat->ops->mult)(mat,x,y);
2345:   PetscLogEventEnd(MAT_Mult,mat,x,y,0);
2346:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2347:   VecLockPop(x);
2348:   return(0);
2349: }

2351: /*@
2352:    MatMultTranspose - Computes matrix transpose times a vector y = A^T * x.

2354:    Neighbor-wise Collective on Mat and Vec

2356:    Input Parameters:
2357: +  mat - the matrix
2358: -  x   - the vector to be multiplied

2360:    Output Parameters:
2361: .  y - the result

2363:    Notes:
2364:    The vectors x and y cannot be the same.  I.e., one cannot
2365:    call MatMultTranspose(A,y,y).

2367:    For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2368:    use MatMultHermitianTranspose()

2370:    Level: beginner

2372:    Concepts: matrix vector product^transpose

2374: .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd(), MatMultHermitianTranspose(), MatTranspose()
2375: @*/
2376: PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y)
2377: {


2386:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2387:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2388:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2389: #if !defined(PETSC_HAVE_CONSTRAINTS)
2390:   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);
2391:   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);
2392: #endif
2393:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2394:   MatCheckPreallocated(mat,1);

2396:   if (!mat->ops->multtranspose) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply transpose defined");
2397:   PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);
2398:   VecLockPush(x);
2399:   (*mat->ops->multtranspose)(mat,x,y);
2400:   VecLockPop(x);
2401:   PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);
2402:   PetscObjectStateIncrease((PetscObject)y);
2403:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2404:   return(0);
2405: }

2407: /*@
2408:    MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector.

2410:    Neighbor-wise Collective on Mat and Vec

2412:    Input Parameters:
2413: +  mat - the matrix
2414: -  x   - the vector to be multilplied

2416:    Output Parameters:
2417: .  y - the result

2419:    Notes:
2420:    The vectors x and y cannot be the same.  I.e., one cannot
2421:    call MatMultHermitianTranspose(A,y,y).

2423:    Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2425:    For real numbers MatMultTranspose() and MatMultHermitianTranspose() are identical.

2427:    Level: beginner

2429:    Concepts: matrix vector product^transpose

2431: .seealso: MatMult(), MatMultAdd(), MatMultHermitianTransposeAdd(), MatMultTranspose()
2432: @*/
2433: PetscErrorCode MatMultHermitianTranspose(Mat mat,Vec x,Vec y)
2434: {
2436:   Vec            w;


2444:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2445:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2446:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2447: #if !defined(PETSC_HAVE_CONSTRAINTS)
2448:   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);
2449:   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);
2450: #endif
2451:   MatCheckPreallocated(mat,1);

2453:   PetscLogEventBegin(MAT_MultHermitianTranspose,mat,x,y,0);
2454:   if (mat->ops->multhermitiantranspose) {
2455:     VecLockPush(x);
2456:     (*mat->ops->multhermitiantranspose)(mat,x,y);
2457:     VecLockPop(x);
2458:   } else {
2459:     VecDuplicate(x,&w);
2460:     VecCopy(x,w);
2461:     VecConjugate(w);
2462:     MatMultTranspose(mat,w,y);
2463:     VecDestroy(&w);
2464:     VecConjugate(y);
2465:   }
2466:   PetscLogEventEnd(MAT_MultHermitianTranspose,mat,x,y,0);
2467:   PetscObjectStateIncrease((PetscObject)y);
2468:   return(0);
2469: }

2471: /*@
2472:     MatMultAdd -  Computes v3 = v2 + A * v1.

2474:     Neighbor-wise Collective on Mat and Vec

2476:     Input Parameters:
2477: +   mat - the matrix
2478: -   v1, v2 - the vectors

2480:     Output Parameters:
2481: .   v3 - the result

2483:     Notes:
2484:     The vectors v1 and v3 cannot be the same.  I.e., one cannot
2485:     call MatMultAdd(A,v1,v2,v1).

2487:     Level: beginner

2489:     Concepts: matrix vector product^addition

2491: .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd()
2492: @*/
2493: PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2494: {


2504:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2505:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2506:   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);
2507:   /* 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);
2508:      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); */
2509:   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);
2510:   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);
2511:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2512:   MatCheckPreallocated(mat,1);

2514:   if (!mat->ops->multadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No MatMultAdd() for matrix type '%s'",((PetscObject)mat)->type_name);
2515:   PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);
2516:   VecLockPush(v1);
2517:   (*mat->ops->multadd)(mat,v1,v2,v3);
2518:   VecLockPop(v1);
2519:   PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);
2520:   PetscObjectStateIncrease((PetscObject)v3);
2521:   return(0);
2522: }

2524: /*@
2525:    MatMultTransposeAdd - Computes v3 = v2 + A' * v1.

2527:    Neighbor-wise Collective on Mat and Vec

2529:    Input Parameters:
2530: +  mat - the matrix
2531: -  v1, v2 - the vectors

2533:    Output Parameters:
2534: .  v3 - the result

2536:    Notes:
2537:    The vectors v1 and v3 cannot be the same.  I.e., one cannot
2538:    call MatMultTransposeAdd(A,v1,v2,v1).

2540:    Level: beginner

2542:    Concepts: matrix vector product^transpose and addition

2544: .seealso: MatMultTranspose(), MatMultAdd(), MatMult()
2545: @*/
2546: PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2547: {


2557:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2558:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2559:   if (!mat->ops->multtransposeadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2560:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2561:   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);
2562:   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);
2563:   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);
2564:   MatCheckPreallocated(mat,1);

2566:   PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);
2567:   VecLockPush(v1);
2568:   (*mat->ops->multtransposeadd)(mat,v1,v2,v3);
2569:   VecLockPop(v1);
2570:   PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);
2571:   PetscObjectStateIncrease((PetscObject)v3);
2572:   return(0);
2573: }

2575: /*@
2576:    MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1.

2578:    Neighbor-wise Collective on Mat and Vec

2580:    Input Parameters:
2581: +  mat - the matrix
2582: -  v1, v2 - the vectors

2584:    Output Parameters:
2585: .  v3 - the result

2587:    Notes:
2588:    The vectors v1 and v3 cannot be the same.  I.e., one cannot
2589:    call MatMultHermitianTransposeAdd(A,v1,v2,v1).

2591:    Level: beginner

2593:    Concepts: matrix vector product^transpose and addition

2595: .seealso: MatMultHermitianTranspose(), MatMultTranspose(), MatMultAdd(), MatMult()
2596: @*/
2597: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2598: {


2608:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2609:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2610:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2611:   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);
2612:   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);
2613:   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);
2614:   MatCheckPreallocated(mat,1);

2616:   PetscLogEventBegin(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2617:   VecLockPush(v1);
2618:   if (mat->ops->multhermitiantransposeadd) {
2619:     (*mat->ops->multhermitiantransposeadd)(mat,v1,v2,v3);
2620:    } else {
2621:     Vec w,z;
2622:     VecDuplicate(v1,&w);
2623:     VecCopy(v1,w);
2624:     VecConjugate(w);
2625:     VecDuplicate(v3,&z);
2626:     MatMultTranspose(mat,w,z);
2627:     VecDestroy(&w);
2628:     VecConjugate(z);
2629:     VecWAXPY(v3,1.0,v2,z);
2630:     VecDestroy(&z);
2631:   }
2632:   VecLockPop(v1);
2633:   PetscLogEventEnd(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2634:   PetscObjectStateIncrease((PetscObject)v3);
2635:   return(0);
2636: }

2638: /*@
2639:    MatMultConstrained - The inner multiplication routine for a
2640:    constrained matrix P^T A P.

2642:    Neighbor-wise Collective on Mat and Vec

2644:    Input Parameters:
2645: +  mat - the matrix
2646: -  x   - the vector to be multilplied

2648:    Output Parameters:
2649: .  y - the result

2651:    Notes:
2652:    The vectors x and y cannot be the same.  I.e., one cannot
2653:    call MatMult(A,y,y).

2655:    Level: beginner

2657: .keywords: matrix, multiply, matrix-vector product, constraint
2658: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2659: @*/
2660: PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y)
2661: {

2668:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2669:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2670:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2671:   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);
2672:   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);
2673:   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);

2675:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2676:   VecLockPush(x);
2677:   (*mat->ops->multconstrained)(mat,x,y);
2678:   VecLockPop(x);
2679:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2680:   PetscObjectStateIncrease((PetscObject)y);
2681:   return(0);
2682: }

2684: /*@
2685:    MatMultTransposeConstrained - The inner multiplication routine for a
2686:    constrained matrix P^T A^T P.

2688:    Neighbor-wise Collective on Mat and Vec

2690:    Input Parameters:
2691: +  mat - the matrix
2692: -  x   - the vector to be multilplied

2694:    Output Parameters:
2695: .  y - the result

2697:    Notes:
2698:    The vectors x and y cannot be the same.  I.e., one cannot
2699:    call MatMult(A,y,y).

2701:    Level: beginner

2703: .keywords: matrix, multiply, matrix-vector product, constraint
2704: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2705: @*/
2706: PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y)
2707: {

2714:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2715:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2716:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2717:   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);
2718:   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);

2720:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2721:   (*mat->ops->multtransposeconstrained)(mat,x,y);
2722:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2723:   PetscObjectStateIncrease((PetscObject)y);
2724:   return(0);
2725: }

2727: /*@C
2728:    MatGetFactorType - gets the type of factorization it is

2730:    Note Collective
2731:    as the flag

2733:    Input Parameters:
2734: .  mat - the matrix

2736:    Output Parameters:
2737: .  t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT

2739:     Level: intermediate

2741: .seealso:    MatFactorType, MatGetFactor()
2742: @*/
2743: PetscErrorCode MatGetFactorType(Mat mat,MatFactorType *t)
2744: {
2748:   *t = mat->factortype;
2749:   return(0);
2750: }

2752: /* ------------------------------------------------------------*/
2753: /*@C
2754:    MatGetInfo - Returns information about matrix storage (number of
2755:    nonzeros, memory, etc.).

2757:    Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used as the flag

2759:    Input Parameters:
2760: .  mat - the matrix

2762:    Output Parameters:
2763: +  flag - flag indicating the type of parameters to be returned
2764:    (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors,
2765:    MAT_GLOBAL_SUM - sum over all processors)
2766: -  info - matrix information context

2768:    Notes:
2769:    The MatInfo context contains a variety of matrix data, including
2770:    number of nonzeros allocated and used, number of mallocs during
2771:    matrix assembly, etc.  Additional information for factored matrices
2772:    is provided (such as the fill ratio, number of mallocs during
2773:    factorization, etc.).  Much of this info is printed to PETSC_STDOUT
2774:    when using the runtime options
2775: $       -info -mat_view ::ascii_info

2777:    Example for C/C++ Users:
2778:    See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2779:    data within the MatInfo context.  For example,
2780: .vb
2781:       MatInfo info;
2782:       Mat     A;
2783:       double  mal, nz_a, nz_u;

2785:       MatGetInfo(A,MAT_LOCAL,&info);
2786:       mal  = info.mallocs;
2787:       nz_a = info.nz_allocated;
2788: .ve

2790:    Example for Fortran Users:
2791:    Fortran users should declare info as a double precision
2792:    array of dimension MAT_INFO_SIZE, and then extract the parameters
2793:    of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2794:    a complete list of parameter names.
2795: .vb
2796:       double  precision info(MAT_INFO_SIZE)
2797:       double  precision mal, nz_a
2798:       Mat     A
2799:       integer ierr

2801:       call MatGetInfo(A,MAT_LOCAL,info,ierr)
2802:       mal = info(MAT_INFO_MALLOCS)
2803:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
2804: .ve

2806:     Level: intermediate

2808:     Concepts: matrices^getting information on

2810:     Developer Note: fortran interface is not autogenerated as the f90
2811:     interface defintion cannot be generated correctly [due to MatInfo]

2813: .seealso: MatStashGetInfo()

2815: @*/
2816: PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info)
2817: {

2824:   if (!mat->ops->getinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2825:   MatCheckPreallocated(mat,1);
2826:   (*mat->ops->getinfo)(mat,flag,info);
2827:   return(0);
2828: }

2830: /*
2831:    This is used by external packages where it is not easy to get the info from the actual
2832:    matrix factorization.
2833: */
2834: PetscErrorCode MatGetInfo_External(Mat A,MatInfoType flag,MatInfo *info)
2835: {

2839:   PetscMemzero(info,sizeof(MatInfo));
2840:   return(0);
2841: }

2843: /* ----------------------------------------------------------*/

2845: /*@C
2846:    MatLUFactor - Performs in-place LU factorization of matrix.

2848:    Collective on Mat

2850:    Input Parameters:
2851: +  mat - the matrix
2852: .  row - row permutation
2853: .  col - column permutation
2854: -  info - options for factorization, includes
2855: $          fill - expected fill as ratio of original fill.
2856: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
2857: $                   Run with the option -info to determine an optimal value to use

2859:    Notes:
2860:    Most users should employ the simplified KSP interface for linear solvers
2861:    instead of working directly with matrix algebra routines such as this.
2862:    See, e.g., KSPCreate().

2864:    This changes the state of the matrix to a factored matrix; it cannot be used
2865:    for example with MatSetValues() unless one first calls MatSetUnfactored().

2867:    Level: developer

2869:    Concepts: matrices^LU factorization

2871: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(),
2872:           MatGetOrdering(), MatSetUnfactored(), MatFactorInfo, MatGetFactor()

2874:     Developer Note: fortran interface is not autogenerated as the f90
2875:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2877: @*/
2878: PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
2879: {
2881:   MatFactorInfo  tinfo;

2889:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2890:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2891:   if (!mat->ops->lufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2892:   MatCheckPreallocated(mat,1);
2893:   if (!info) {
2894:     MatFactorInfoInitialize(&tinfo);
2895:     info = &tinfo;
2896:   }

2898:   PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);
2899:   (*mat->ops->lufactor)(mat,row,col,info);
2900:   PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);
2901:   PetscObjectStateIncrease((PetscObject)mat);
2902:   return(0);
2903: }

2905: /*@C
2906:    MatILUFactor - Performs in-place ILU factorization of matrix.

2908:    Collective on Mat

2910:    Input Parameters:
2911: +  mat - the matrix
2912: .  row - row permutation
2913: .  col - column permutation
2914: -  info - structure containing
2915: $      levels - number of levels of fill.
2916: $      expected fill - as ratio of original fill.
2917: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
2918:                 missing diagonal entries)

2920:    Notes:
2921:    Probably really in-place only when level of fill is zero, otherwise allocates
2922:    new space to store factored matrix and deletes previous memory.

2924:    Most users should employ the simplified KSP interface for linear solvers
2925:    instead of working directly with matrix algebra routines such as this.
2926:    See, e.g., KSPCreate().

2928:    Level: developer

2930:    Concepts: matrices^ILU factorization

2932: .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

2934:     Developer Note: fortran interface is not autogenerated as the f90
2935:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2937: @*/
2938: PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
2939: {

2948:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
2949:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2950:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2951:   if (!mat->ops->ilufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2952:   MatCheckPreallocated(mat,1);

2954:   PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);
2955:   (*mat->ops->ilufactor)(mat,row,col,info);
2956:   PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);
2957:   PetscObjectStateIncrease((PetscObject)mat);
2958:   return(0);
2959: }

2961: /*@C
2962:    MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
2963:    Call this routine before calling MatLUFactorNumeric().

2965:    Collective on Mat

2967:    Input Parameters:
2968: +  fact - the factor matrix obtained with MatGetFactor()
2969: .  mat - the matrix
2970: .  row, col - row and column permutations
2971: -  info - options for factorization, includes
2972: $          fill - expected fill as ratio of original fill.
2973: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
2974: $                   Run with the option -info to determine an optimal value to use


2977:    Notes:
2978:     See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

2980:    Most users should employ the simplified KSP interface for linear solvers
2981:    instead of working directly with matrix algebra routines such as this.
2982:    See, e.g., KSPCreate().

2984:    Level: developer

2986:    Concepts: matrices^LU symbolic factorization

2988: .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo, MatFactorInfoInitialize()

2990:     Developer Note: fortran interface is not autogenerated as the f90
2991:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2993: @*/
2994: PetscErrorCode MatLUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
2995: {

3005:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3006:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3007:   if (!(fact)->ops->lufactorsymbolic) {
3008:     MatSolverType spackage;
3009:     MatFactorGetSolverType(fact,&spackage);
3010:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic LU using solver package %s",((PetscObject)mat)->type_name,spackage);
3011:   }
3012:   MatCheckPreallocated(mat,2);

3014:   PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);
3015:   (fact->ops->lufactorsymbolic)(fact,mat,row,col,info);
3016:   PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);
3017:   PetscObjectStateIncrease((PetscObject)fact);
3018:   return(0);
3019: }

3021: /*@C
3022:    MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3023:    Call this routine after first calling MatLUFactorSymbolic().

3025:    Collective on Mat

3027:    Input Parameters:
3028: +  fact - the factor matrix obtained with MatGetFactor()
3029: .  mat - the matrix
3030: -  info - options for factorization

3032:    Notes:
3033:    See MatLUFactor() for in-place factorization.  See
3034:    MatCholeskyFactorNumeric() for the symmetric, positive definite case.

3036:    Most users should employ the simplified KSP interface for linear solvers
3037:    instead of working directly with matrix algebra routines such as this.
3038:    See, e.g., KSPCreate().

3040:    Level: developer

3042:    Concepts: matrices^LU numeric factorization

3044: .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor()

3046:     Developer Note: fortran interface is not autogenerated as the f90
3047:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3049: @*/
3050: PetscErrorCode MatLUFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3051: {

3059:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3060:   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);

3062:   if (!(fact)->ops->lufactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric LU",((PetscObject)mat)->type_name);
3063:   MatCheckPreallocated(mat,2);
3064:   PetscLogEventBegin(MAT_LUFactorNumeric,mat,fact,0,0);
3065:   (fact->ops->lufactornumeric)(fact,mat,info);
3066:   PetscLogEventEnd(MAT_LUFactorNumeric,mat,fact,0,0);
3067:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3068:   PetscObjectStateIncrease((PetscObject)fact);
3069:   return(0);
3070: }

3072: /*@C
3073:    MatCholeskyFactor - Performs in-place Cholesky factorization of a
3074:    symmetric matrix.

3076:    Collective on Mat

3078:    Input Parameters:
3079: +  mat - the matrix
3080: .  perm - row and column permutations
3081: -  f - expected fill as ratio of original fill

3083:    Notes:
3084:    See MatLUFactor() for the nonsymmetric case.  See also
3085:    MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric().

3087:    Most users should employ the simplified KSP interface for linear solvers
3088:    instead of working directly with matrix algebra routines such as this.
3089:    See, e.g., KSPCreate().

3091:    Level: developer

3093:    Concepts: matrices^Cholesky factorization

3095: .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric()
3096:           MatGetOrdering()

3098:     Developer Note: fortran interface is not autogenerated as the f90
3099:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3101: @*/
3102: PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,const MatFactorInfo *info)
3103: {

3111:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3112:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3113:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3114:   if (!mat->ops->choleskyfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"In-place factorization for Mat type %s is not supported, try out-of-place factorization. See MatCholeskyFactorSymbolic/Numeric",((PetscObject)mat)->type_name);
3115:   MatCheckPreallocated(mat,1);

3117:   PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);
3118:   (*mat->ops->choleskyfactor)(mat,perm,info);
3119:   PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);
3120:   PetscObjectStateIncrease((PetscObject)mat);
3121:   return(0);
3122: }

3124: /*@C
3125:    MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3126:    of a symmetric matrix.

3128:    Collective on Mat

3130:    Input Parameters:
3131: +  fact - the factor matrix obtained with MatGetFactor()
3132: .  mat - the matrix
3133: .  perm - row and column permutations
3134: -  info - options for factorization, includes
3135: $          fill - expected fill as ratio of original fill.
3136: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3137: $                   Run with the option -info to determine an optimal value to use

3139:    Notes:
3140:    See MatLUFactorSymbolic() for the nonsymmetric case.  See also
3141:    MatCholeskyFactor() and MatCholeskyFactorNumeric().

3143:    Most users should employ the simplified KSP interface for linear solvers
3144:    instead of working directly with matrix algebra routines such as this.
3145:    See, e.g., KSPCreate().

3147:    Level: developer

3149:    Concepts: matrices^Cholesky symbolic factorization

3151: .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric()
3152:           MatGetOrdering()

3154:     Developer Note: fortran interface is not autogenerated as the f90
3155:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3157: @*/
3158: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
3159: {

3168:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3169:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3170:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3171:   if (!(fact)->ops->choleskyfactorsymbolic) {
3172:     MatSolverType spackage;
3173:     MatFactorGetSolverType(fact,&spackage);
3174:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s symbolic factor Cholesky using solver package %s",((PetscObject)mat)->type_name,spackage);
3175:   }
3176:   MatCheckPreallocated(mat,2);

3178:   PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3179:   (fact->ops->choleskyfactorsymbolic)(fact,mat,perm,info);
3180:   PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3181:   PetscObjectStateIncrease((PetscObject)fact);
3182:   return(0);
3183: }

3185: /*@C
3186:    MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3187:    of a symmetric matrix. Call this routine after first calling
3188:    MatCholeskyFactorSymbolic().

3190:    Collective on Mat

3192:    Input Parameters:
3193: +  fact - the factor matrix obtained with MatGetFactor()
3194: .  mat - the initial matrix
3195: .  info - options for factorization
3196: -  fact - the symbolic factor of mat


3199:    Notes:
3200:    Most users should employ the simplified KSP interface for linear solvers
3201:    instead of working directly with matrix algebra routines such as this.
3202:    See, e.g., KSPCreate().

3204:    Level: developer

3206:    Concepts: matrices^Cholesky numeric factorization

3208: .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric()

3210:     Developer Note: fortran interface is not autogenerated as the f90
3211:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3213: @*/
3214: PetscErrorCode MatCholeskyFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3215: {

3223:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3224:   if (!(fact)->ops->choleskyfactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric factor Cholesky",((PetscObject)mat)->type_name);
3225:   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);
3226:   MatCheckPreallocated(mat,2);

3228:   PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3229:   (fact->ops->choleskyfactornumeric)(fact,mat,info);
3230:   PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3231:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3232:   PetscObjectStateIncrease((PetscObject)fact);
3233:   return(0);
3234: }

3236: /* ----------------------------------------------------------------*/
3237: /*@
3238:    MatSolve - Solves A x = b, given a factored matrix.

3240:    Neighbor-wise Collective on Mat and Vec

3242:    Input Parameters:
3243: +  mat - the factored matrix
3244: -  b - the right-hand-side vector

3246:    Output Parameter:
3247: .  x - the result vector

3249:    Notes:
3250:    The vectors b and x cannot be the same.  I.e., one cannot
3251:    call MatSolve(A,x,x).

3253:    Notes:
3254:    Most users should employ the simplified KSP interface for linear solvers
3255:    instead of working directly with matrix algebra routines such as this.
3256:    See, e.g., KSPCreate().

3258:    Level: developer

3260:    Concepts: matrices^triangular solves

3262: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd()
3263: @*/
3264: PetscErrorCode MatSolve(Mat mat,Vec b,Vec x)
3265: {

3275:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3276:   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);
3277:   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);
3278:   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);
3279:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3280:   if (!mat->ops->solve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3281:   MatCheckPreallocated(mat,1);

3283:   PetscLogEventBegin(MAT_Solve,mat,b,x,0);
3284:   if (mat->factorerrortype) {
3285:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3286:     VecSetInf(x);
3287:   } else {
3288:     if (!mat->ops->solve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3289:     (*mat->ops->solve)(mat,b,x);
3290:   }
3291:   PetscLogEventEnd(MAT_Solve,mat,b,x,0);
3292:   PetscObjectStateIncrease((PetscObject)x);
3293:   return(0);
3294: }

3296: static PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X, PetscBool trans)
3297: {
3299:   Vec            b,x;
3300:   PetscInt       m,N,i;
3301:   PetscScalar    *bb,*xx;
3302:   PetscBool      flg;

3305:   PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
3306:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
3307:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
3308:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");

3310:   MatDenseGetArray(B,&bb);
3311:   MatDenseGetArray(X,&xx);
3312:   MatGetLocalSize(B,&m,NULL);  /* number local rows */
3313:   MatGetSize(B,NULL,&N);       /* total columns in dense matrix */
3314:   MatCreateVecs(A,&x,&b);
3315:   for (i=0; i<N; i++) {
3316:     VecPlaceArray(b,bb + i*m);
3317:     VecPlaceArray(x,xx + i*m);
3318:     if (trans) {
3319:       MatSolveTranspose(A,b,x);
3320:     } else {
3321:       MatSolve(A,b,x);
3322:     }
3323:     VecResetArray(x);
3324:     VecResetArray(b);
3325:   }
3326:   VecDestroy(&b);
3327:   VecDestroy(&x);
3328:   MatDenseRestoreArray(B,&bb);
3329:   MatDenseRestoreArray(X,&xx);
3330:   return(0);
3331: }

3333: /*@
3334:    MatMatSolve - Solves A X = B, given a factored matrix.

3336:    Neighbor-wise Collective on Mat

3338:    Input Parameters:
3339: +  A - the factored matrix
3340: -  B - the right-hand-side matrix  (dense matrix)

3342:    Output Parameter:
3343: .  X - the result matrix (dense matrix)

3345:    Notes:
3346:    The matrices b and x cannot be the same.  I.e., one cannot
3347:    call MatMatSolve(A,x,x).

3349:    Notes:
3350:    Most users should usually employ the simplified KSP interface for linear solvers
3351:    instead of working directly with matrix algebra routines such as this.
3352:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3353:    at a time.

3355:    When using SuperLU_Dist as a parallel solver PETSc will use the SuperLU_Dist functionality to solve multiple right hand sides simultaneously. For MUMPS
3356:    it calls a separate solve for each right hand side since MUMPS does not yet support distributed right hand sides.

3358:    Since the resulting matrix X must always be dense we do not support sparse representation of the matrix B.

3360:    Level: developer

3362:    Concepts: matrices^triangular solves

3364: .seealso: MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3365: @*/
3366: PetscErrorCode MatMatSolve(Mat A,Mat B,Mat X)
3367: {

3377:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3378:   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);
3379:   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);
3380:   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");
3381:   if (!A->rmap->N && !A->cmap->N) return(0);
3382:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3383:   MatCheckPreallocated(A,1);

3385:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3386:   if (!A->ops->matsolve) {
3387:     PetscInfo1(A,"Mat type %s using basic MatMatSolve\n",((PetscObject)A)->type_name);
3388:     MatMatSolve_Basic(A,B,X,PETSC_FALSE);
3389:   } else {
3390:     (*A->ops->matsolve)(A,B,X);
3391:   }
3392:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3393:   PetscObjectStateIncrease((PetscObject)X);
3394:   return(0);
3395: }

3397: /*@
3398:    MatMatSolveTranspose - Solves A^T X = B, given a factored matrix.

3400:    Neighbor-wise Collective on Mat

3402:    Input Parameters:
3403: +  A - the factored matrix
3404: -  B - the right-hand-side matrix  (dense matrix)

3406:    Output Parameter:
3407: .  X - the result matrix (dense matrix)

3409:    Notes:
3410:    The matrices B and X cannot be the same.  I.e., one cannot
3411:    call MatMatSolveTranspose(A,X,X).

3413:    Notes:
3414:    Most users should usually employ the simplified KSP interface for linear solvers
3415:    instead of working directly with matrix algebra routines such as this.
3416:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3417:    at a time.

3419:    When using SuperLU_Dist or MUMPS as a parallel solver, PETSc will use their functionality to solve multiple right hand sides simultaneously.

3421:    Level: developer

3423:    Concepts: matrices^triangular solves

3425: .seealso: MatMatSolve(), MatLUFactor(), MatCholeskyFactor()
3426: @*/
3427: PetscErrorCode MatMatSolveTranspose(Mat A,Mat B,Mat X)
3428: {

3438:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3439:   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);
3440:   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);
3441:   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);
3442:   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");
3443:   if (!A->rmap->N && !A->cmap->N) return(0);
3444:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3445:   MatCheckPreallocated(A,1);

3447:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3448:   if (!A->ops->matsolvetranspose) {
3449:     PetscInfo1(A,"Mat type %s using basic MatMatSolveTranspose\n",((PetscObject)A)->type_name);
3450:     MatMatSolve_Basic(A,B,X,PETSC_TRUE);
3451:   } else {
3452:     (*A->ops->matsolvetranspose)(A,B,X);
3453:   }
3454:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3455:   PetscObjectStateIncrease((PetscObject)X);
3456:   return(0);
3457: }

3459: /*@
3460:    MatMatTransposeSolve - Solves A X = B^T, given a factored matrix.

3462:    Neighbor-wise Collective on Mat

3464:    Input Parameters:
3465: +  A - the factored matrix
3466: -  Bt - the transpose of right-hand-side matrix

3468:    Output Parameter:
3469: .  X - the result matrix (dense matrix)

3471:    Notes:
3472:    Most users should usually employ the simplified KSP interface for linear solvers
3473:    instead of working directly with matrix algebra routines such as this.
3474:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3475:    at a time.

3477:    For MUMPS, it only supports centralized sparse compressed column format on the host processor for right hand side matrix. User must create B^T in sparse compressed row format on the host processor and call MatMatTransposeSolve() to implement MUMPS' MatMatSolve().

3479:    Level: developer

3481:    Concepts: matrices^triangular solves

3483: .seealso: MatMatSolve(), MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3484: @*/
3485: PetscErrorCode MatMatTransposeSolve(Mat A,Mat Bt,Mat X)
3486: {


3497:   if (X == Bt) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3498:   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);
3499:   if (A->rmap->N != Bt->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat Bt: global dim %D %D",A->rmap->N,Bt->cmap->N);
3500:   if (X->cmap->N < Bt->rmap->N) SETERRQ(PetscObjectComm((PetscObject)X),PETSC_ERR_ARG_SIZ,"Solution matrix must have same number of columns as row number of the rhs matrix");
3501:   if (!A->rmap->N && !A->cmap->N) return(0);
3502:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3503:   MatCheckPreallocated(A,1);

3505:   if (!A->ops->mattransposesolve) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
3506:   PetscLogEventBegin(MAT_MatTrSolve,A,Bt,X,0);
3507:   (*A->ops->mattransposesolve)(A,Bt,X);
3508:   PetscLogEventEnd(MAT_MatTrSolve,A,Bt,X,0);
3509:   PetscObjectStateIncrease((PetscObject)X);
3510:   return(0);
3511: }

3513: /*@
3514:    MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3515:                             U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3517:    Neighbor-wise Collective on Mat and Vec

3519:    Input Parameters:
3520: +  mat - the factored matrix
3521: -  b - the right-hand-side vector

3523:    Output Parameter:
3524: .  x - the result vector

3526:    Notes:
3527:    MatSolve() should be used for most applications, as it performs
3528:    a forward solve followed by a backward solve.

3530:    The vectors b and x cannot be the same,  i.e., one cannot
3531:    call MatForwardSolve(A,x,x).

3533:    For matrix in seqsbaij format with block size larger than 1,
3534:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3535:    MatForwardSolve() solves U^T*D y = b, and
3536:    MatBackwardSolve() solves U x = y.
3537:    Thus they do not provide a symmetric preconditioner.

3539:    Most users should employ the simplified KSP interface for linear solvers
3540:    instead of working directly with matrix algebra routines such as this.
3541:    See, e.g., KSPCreate().

3543:    Level: developer

3545:    Concepts: matrices^forward solves

3547: .seealso: MatSolve(), MatBackwardSolve()
3548: @*/
3549: PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x)
3550: {

3560:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3561:   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);
3562:   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);
3563:   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);
3564:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3565:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3566:   MatCheckPreallocated(mat,1);

3568:   if (!mat->ops->forwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3569:   PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);
3570:   (*mat->ops->forwardsolve)(mat,b,x);
3571:   PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);
3572:   PetscObjectStateIncrease((PetscObject)x);
3573:   return(0);
3574: }

3576: /*@
3577:    MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3578:                              D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3580:    Neighbor-wise Collective on Mat and Vec

3582:    Input Parameters:
3583: +  mat - the factored matrix
3584: -  b - the right-hand-side vector

3586:    Output Parameter:
3587: .  x - the result vector

3589:    Notes:
3590:    MatSolve() should be used for most applications, as it performs
3591:    a forward solve followed by a backward solve.

3593:    The vectors b and x cannot be the same.  I.e., one cannot
3594:    call MatBackwardSolve(A,x,x).

3596:    For matrix in seqsbaij format with block size larger than 1,
3597:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3598:    MatForwardSolve() solves U^T*D y = b, and
3599:    MatBackwardSolve() solves U x = y.
3600:    Thus they do not provide a symmetric preconditioner.

3602:    Most users should employ the simplified KSP interface for linear solvers
3603:    instead of working directly with matrix algebra routines such as this.
3604:    See, e.g., KSPCreate().

3606:    Level: developer

3608:    Concepts: matrices^backward solves

3610: .seealso: MatSolve(), MatForwardSolve()
3611: @*/
3612: PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x)
3613: {

3623:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3624:   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);
3625:   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);
3626:   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);
3627:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3628:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3629:   MatCheckPreallocated(mat,1);

3631:   if (!mat->ops->backwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3632:   PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);
3633:   (*mat->ops->backwardsolve)(mat,b,x);
3634:   PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);
3635:   PetscObjectStateIncrease((PetscObject)x);
3636:   return(0);
3637: }

3639: /*@
3640:    MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3642:    Neighbor-wise Collective on Mat and Vec

3644:    Input Parameters:
3645: +  mat - the factored matrix
3646: .  b - the right-hand-side vector
3647: -  y - the vector to be added to

3649:    Output Parameter:
3650: .  x - the result vector

3652:    Notes:
3653:    The vectors b and x cannot be the same.  I.e., one cannot
3654:    call MatSolveAdd(A,x,y,x).

3656:    Most users should employ the simplified KSP interface for linear solvers
3657:    instead of working directly with matrix algebra routines such as this.
3658:    See, e.g., KSPCreate().

3660:    Level: developer

3662:    Concepts: matrices^triangular solves

3664: .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd()
3665: @*/
3666: PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x)
3667: {
3668:   PetscScalar    one = 1.0;
3669:   Vec            tmp;

3681:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3682:   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);
3683:   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);
3684:   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);
3685:   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);
3686:   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);
3687:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3688:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3689:   MatCheckPreallocated(mat,1);

3691:   PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);
3692:   if (mat->ops->solveadd) {
3693:     (*mat->ops->solveadd)(mat,b,y,x);
3694:   } else {
3695:     /* do the solve then the add manually */
3696:     if (x != y) {
3697:       MatSolve(mat,b,x);
3698:       VecAXPY(x,one,y);
3699:     } else {
3700:       VecDuplicate(x,&tmp);
3701:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3702:       VecCopy(x,tmp);
3703:       MatSolve(mat,b,x);
3704:       VecAXPY(x,one,tmp);
3705:       VecDestroy(&tmp);
3706:     }
3707:   }
3708:   PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);
3709:   PetscObjectStateIncrease((PetscObject)x);
3710:   return(0);
3711: }

3713: /*@
3714:    MatSolveTranspose - Solves A' x = b, given a factored matrix.

3716:    Neighbor-wise Collective on Mat and Vec

3718:    Input Parameters:
3719: +  mat - the factored matrix
3720: -  b - the right-hand-side vector

3722:    Output Parameter:
3723: .  x - the result vector

3725:    Notes:
3726:    The vectors b and x cannot be the same.  I.e., one cannot
3727:    call MatSolveTranspose(A,x,x).

3729:    Most users should employ the simplified KSP interface for linear solvers
3730:    instead of working directly with matrix algebra routines such as this.
3731:    See, e.g., KSPCreate().

3733:    Level: developer

3735:    Concepts: matrices^triangular solves

3737: .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd()
3738: @*/
3739: PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x)
3740: {

3750:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3751:   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);
3752:   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);
3753:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3754:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3755:   MatCheckPreallocated(mat,1);
3756:   PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);
3757:   if (mat->factorerrortype) {
3758:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3759:     VecSetInf(x);
3760:   } else {
3761:     if (!mat->ops->solvetranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name);
3762:     (*mat->ops->solvetranspose)(mat,b,x);
3763:   }
3764:   PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);
3765:   PetscObjectStateIncrease((PetscObject)x);
3766:   return(0);
3767: }

3769: /*@
3770:    MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
3771:                       factored matrix.

3773:    Neighbor-wise Collective on Mat and Vec

3775:    Input Parameters:
3776: +  mat - the factored matrix
3777: .  b - the right-hand-side vector
3778: -  y - the vector to be added to

3780:    Output Parameter:
3781: .  x - the result vector

3783:    Notes:
3784:    The vectors b and x cannot be the same.  I.e., one cannot
3785:    call MatSolveTransposeAdd(A,x,y,x).

3787:    Most users should employ the simplified KSP interface for linear solvers
3788:    instead of working directly with matrix algebra routines such as this.
3789:    See, e.g., KSPCreate().

3791:    Level: developer

3793:    Concepts: matrices^triangular solves

3795: .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose()
3796: @*/
3797: PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x)
3798: {
3799:   PetscScalar    one = 1.0;
3801:   Vec            tmp;

3812:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3813:   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);
3814:   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);
3815:   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);
3816:   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);
3817:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3818:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3819:   MatCheckPreallocated(mat,1);

3821:   PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);
3822:   if (mat->ops->solvetransposeadd) {
3823:     if (mat->factorerrortype) {
3824:       PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3825:       VecSetInf(x);
3826:     } else {
3827:       (*mat->ops->solvetransposeadd)(mat,b,y,x);
3828:     }
3829:   } else {
3830:     /* do the solve then the add manually */
3831:     if (x != y) {
3832:       MatSolveTranspose(mat,b,x);
3833:       VecAXPY(x,one,y);
3834:     } else {
3835:       VecDuplicate(x,&tmp);
3836:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3837:       VecCopy(x,tmp);
3838:       MatSolveTranspose(mat,b,x);
3839:       VecAXPY(x,one,tmp);
3840:       VecDestroy(&tmp);
3841:     }
3842:   }
3843:   PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);
3844:   PetscObjectStateIncrease((PetscObject)x);
3845:   return(0);
3846: }
3847: /* ----------------------------------------------------------------*/

3849: /*@
3850:    MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

3852:    Neighbor-wise Collective on Mat and Vec

3854:    Input Parameters:
3855: +  mat - the matrix
3856: .  b - the right hand side
3857: .  omega - the relaxation factor
3858: .  flag - flag indicating the type of SOR (see below)
3859: .  shift -  diagonal shift
3860: .  its - the number of iterations
3861: -  lits - the number of local iterations

3863:    Output Parameters:
3864: .  x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess)

3866:    SOR Flags:
3867: .     SOR_FORWARD_SWEEP - forward SOR
3868: .     SOR_BACKWARD_SWEEP - backward SOR
3869: .     SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR)
3870: .     SOR_LOCAL_FORWARD_SWEEP - local forward SOR
3871: .     SOR_LOCAL_BACKWARD_SWEEP - local forward SOR
3872: .     SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR
3873: .     SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies
3874:          upper/lower triangular part of matrix to
3875:          vector (with omega)
3876: .     SOR_ZERO_INITIAL_GUESS - zero initial guess

3878:    Notes:
3879:    SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and
3880:    SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings
3881:    on each processor.

3883:    Application programmers will not generally use MatSOR() directly,
3884:    but instead will employ the KSP/PC interface.

3886:    Notes:
3887:     for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

3889:    Notes for Advanced Users:
3890:    The flags are implemented as bitwise inclusive or operations.
3891:    For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP)
3892:    to specify a zero initial guess for SSOR.

3894:    Most users should employ the simplified KSP interface for linear solvers
3895:    instead of working directly with matrix algebra routines such as this.
3896:    See, e.g., KSPCreate().

3898:    Vectors x and b CANNOT be the same

3900:    Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes

3902:    Level: developer

3904:    Concepts: matrices^relaxation
3905:    Concepts: matrices^SOR
3906:    Concepts: matrices^Gauss-Seidel

3908: @*/
3909: PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x)
3910: {

3920:   if (!mat->ops->sor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3921:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3922:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3923:   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);
3924:   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);
3925:   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);
3926:   if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its);
3927:   if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits);
3928:   if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same");

3930:   MatCheckPreallocated(mat,1);
3931:   PetscLogEventBegin(MAT_SOR,mat,b,x,0);
3932:   ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);
3933:   PetscLogEventEnd(MAT_SOR,mat,b,x,0);
3934:   PetscObjectStateIncrease((PetscObject)x);
3935:   return(0);
3936: }

3938: /*
3939:       Default matrix copy routine.
3940: */
3941: PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str)
3942: {
3943:   PetscErrorCode    ierr;
3944:   PetscInt          i,rstart = 0,rend = 0,nz;
3945:   const PetscInt    *cwork;
3946:   const PetscScalar *vwork;

3949:   if (B->assembled) {
3950:     MatZeroEntries(B);
3951:   }
3952:   MatGetOwnershipRange(A,&rstart,&rend);
3953:   for (i=rstart; i<rend; i++) {
3954:     MatGetRow(A,i,&nz,&cwork,&vwork);
3955:     MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);
3956:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
3957:   }
3958:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3959:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3960:   return(0);
3961: }

3963: /*@
3964:    MatCopy - Copys a matrix to another matrix.

3966:    Collective on Mat

3968:    Input Parameters:
3969: +  A - the matrix
3970: -  str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN

3972:    Output Parameter:
3973: .  B - where the copy is put

3975:    Notes:
3976:    If you use SAME_NONZERO_PATTERN then the two matrices had better have the
3977:    same nonzero pattern or the routine will crash.

3979:    MatCopy() copies the matrix entries of a matrix to another existing
3980:    matrix (after first zeroing the second matrix).  A related routine is
3981:    MatConvert(), which first creates a new matrix and then copies the data.

3983:    Level: intermediate

3985:    Concepts: matrices^copying

3987: .seealso: MatConvert(), MatDuplicate()

3989: @*/
3990: PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str)
3991: {
3993:   PetscInt       i;

4001:   MatCheckPreallocated(B,2);
4002:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4003:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4004:   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);
4005:   MatCheckPreallocated(A,1);
4006:   if (A == B) return(0);

4008:   PetscLogEventBegin(MAT_Copy,A,B,0,0);
4009:   if (A->ops->copy) {
4010:     (*A->ops->copy)(A,B,str);
4011:   } else { /* generic conversion */
4012:     MatCopy_Basic(A,B,str);
4013:   }

4015:   B->stencil.dim = A->stencil.dim;
4016:   B->stencil.noc = A->stencil.noc;
4017:   for (i=0; i<=A->stencil.dim; i++) {
4018:     B->stencil.dims[i]   = A->stencil.dims[i];
4019:     B->stencil.starts[i] = A->stencil.starts[i];
4020:   }

4022:   PetscLogEventEnd(MAT_Copy,A,B,0,0);
4023:   PetscObjectStateIncrease((PetscObject)B);
4024:   return(0);
4025: }

4027: /*@C
4028:    MatConvert - Converts a matrix to another matrix, either of the same
4029:    or different type.

4031:    Collective on Mat

4033:    Input Parameters:
4034: +  mat - the matrix
4035: .  newtype - new matrix type.  Use MATSAME to create a new matrix of the
4036:    same type as the original matrix.
4037: -  reuse - denotes if the destination matrix is to be created or reused.
4038:    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
4039:    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).

4041:    Output Parameter:
4042: .  M - pointer to place new matrix

4044:    Notes:
4045:    MatConvert() first creates a new matrix and then copies the data from
4046:    the first matrix.  A related routine is MatCopy(), which copies the matrix
4047:    entries of one matrix to another already existing matrix context.

4049:    Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4050:    the MPI communicator of the generated matrix is always the same as the communicator
4051:    of the input matrix.

4053:    Level: intermediate

4055:    Concepts: matrices^converting between storage formats

4057: .seealso: MatCopy(), MatDuplicate()
4058: @*/
4059: PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M)
4060: {
4062:   PetscBool      sametype,issame,flg;
4063:   char           convname[256],mtype[256];
4064:   Mat            B;

4070:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4071:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4072:   MatCheckPreallocated(mat,1);

4074:   PetscOptionsGetString(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);
4075:   if (flg) {
4076:     newtype = mtype;
4077:   }
4078:   PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);
4079:   PetscStrcmp(newtype,"same",&issame);
4080:   if ((reuse == MAT_INPLACE_MATRIX) && (mat != *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires same input and output matrix");
4081:   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");

4083:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) return(0);

4085:   if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4086:     (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);
4087:   } else {
4088:     PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=NULL;
4089:     const char     *prefix[3] = {"seq","mpi",""};
4090:     PetscInt       i;
4091:     /*
4092:        Order of precedence:
4093:        1) See if a specialized converter is known to the current matrix.
4094:        2) See if a specialized converter is known to the desired matrix class.
4095:        3) See if a good general converter is registered for the desired class
4096:           (as of 6/27/03 only MATMPIADJ falls into this category).
4097:        4) See if a good general converter is known for the current matrix.
4098:        5) Use a really basic converter.
4099:     */

4101:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4102:     for (i=0; i<3; i++) {
4103:       PetscStrncpy(convname,"MatConvert_",sizeof(convname));
4104:       PetscStrlcat(convname,((PetscObject)mat)->type_name,sizeof(convname));
4105:       PetscStrlcat(convname,"_",sizeof(convname));
4106:       PetscStrlcat(convname,prefix[i],sizeof(convname));
4107:       PetscStrlcat(convname,issame ? ((PetscObject)mat)->type_name : newtype,sizeof(convname));
4108:       PetscStrlcat(convname,"_C",sizeof(convname));
4109:       PetscObjectQueryFunction((PetscObject)mat,convname,&conv);
4110:       if (conv) goto foundconv;
4111:     }

4113:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4114:     MatCreate(PetscObjectComm((PetscObject)mat),&B);
4115:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);
4116:     MatSetType(B,newtype);
4117:     for (i=0; i<3; i++) {
4118:       PetscStrncpy(convname,"MatConvert_",sizeof(convname));
4119:       PetscStrlcat(convname,((PetscObject)mat)->type_name,sizeof(convname));
4120:       PetscStrlcat(convname,"_",sizeof(convname));
4121:       PetscStrlcat(convname,prefix[i],sizeof(convname));
4122:       PetscStrlcat(convname,newtype,sizeof(convname));
4123:       PetscStrlcat(convname,"_C",sizeof(convname));
4124:       PetscObjectQueryFunction((PetscObject)B,convname,&conv);
4125:       if (conv) {
4126:         MatDestroy(&B);
4127:         goto foundconv;
4128:       }
4129:     }

4131:     /* 3) See if a good general converter is registered for the desired class */
4132:     conv = B->ops->convertfrom;
4133:     MatDestroy(&B);
4134:     if (conv) goto foundconv;

4136:     /* 4) See if a good general converter is known for the current matrix */
4137:     if (mat->ops->convert) {
4138:       conv = mat->ops->convert;
4139:     }
4140:     if (conv) goto foundconv;

4142:     /* 5) Use a really basic converter. */
4143:     conv = MatConvert_Basic;

4145: foundconv:
4146:     PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4147:     (*conv)(mat,newtype,reuse,M);
4148:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4149:       /* the block sizes must be same if the mappings are copied over */
4150:       (*M)->rmap->bs = mat->rmap->bs;
4151:       (*M)->cmap->bs = mat->cmap->bs;
4152:       PetscObjectReference((PetscObject)mat->rmap->mapping);
4153:       PetscObjectReference((PetscObject)mat->cmap->mapping);
4154:       (*M)->rmap->mapping = mat->rmap->mapping;
4155:       (*M)->cmap->mapping = mat->cmap->mapping;
4156:     }
4157:     (*M)->stencil.dim = mat->stencil.dim;
4158:     (*M)->stencil.noc = mat->stencil.noc;
4159:     for (i=0; i<=mat->stencil.dim; i++) {
4160:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4161:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4162:     }
4163:     PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4164:   }
4165:   PetscObjectStateIncrease((PetscObject)*M);

4167:   /* Copy Mat options */
4168:   if (mat->symmetric) {MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);}
4169:   if (mat->hermitian) {MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);}
4170:   return(0);
4171: }

4173: /*@C
4174:    MatFactorGetSolverType - Returns name of the package providing the factorization routines

4176:    Not Collective

4178:    Input Parameter:
4179: .  mat - the matrix, must be a factored matrix

4181:    Output Parameter:
4182: .   type - the string name of the package (do not free this string)

4184:    Notes:
4185:       In Fortran you pass in a empty string and the package name will be copied into it.
4186:     (Make sure the string is long enough)

4188:    Level: intermediate

4190: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor()
4191: @*/
4192: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4193: {
4194:   PetscErrorCode ierr, (*conv)(Mat,MatSolverType*);

4199:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
4200:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverType_C",&conv);
4201:   if (!conv) {
4202:     *type = MATSOLVERPETSC;
4203:   } else {
4204:     (*conv)(mat,type);
4205:   }
4206:   return(0);
4207: }

4209: typedef struct _MatSolverTypeForSpecifcType* MatSolverTypeForSpecifcType;
4210: struct _MatSolverTypeForSpecifcType {
4211:   MatType                        mtype;
4212:   PetscErrorCode                 (*getfactor[4])(Mat,MatFactorType,Mat*);
4213:   MatSolverTypeForSpecifcType next;
4214: };

4216: typedef struct _MatSolverTypeHolder* MatSolverTypeHolder;
4217: struct _MatSolverTypeHolder {
4218:   char                           *name;
4219:   MatSolverTypeForSpecifcType handlers;
4220:   MatSolverTypeHolder         next;
4221: };

4223: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4225: /*@C
4226:    MatSolvePackageRegister - Registers a MatSolverType that works for a particular matrix type

4228:    Input Parameters:
4229: +    package - name of the package, for example petsc or superlu
4230: .    mtype - the matrix type that works with this package
4231: .    ftype - the type of factorization supported by the package
4232: -    getfactor - routine that will create the factored matrix ready to be used

4234:     Level: intermediate

4236: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4237: @*/
4238: PetscErrorCode MatSolverTypeRegister(MatSolverType package,MatType mtype,MatFactorType ftype,PetscErrorCode (*getfactor)(Mat,MatFactorType,Mat*))
4239: {
4240:   PetscErrorCode              ierr;
4241:   MatSolverTypeHolder         next = MatSolverTypeHolders,prev;
4242:   PetscBool                   flg;
4243:   MatSolverTypeForSpecifcType inext,iprev = NULL;

4246:   MatInitializePackage();
4247:   if (!next) {
4248:     PetscNew(&MatSolverTypeHolders);
4249:     PetscStrallocpy(package,&MatSolverTypeHolders->name);
4250:     PetscNew(&MatSolverTypeHolders->handlers);
4251:     PetscStrallocpy(mtype,(char **)&MatSolverTypeHolders->handlers->mtype);
4252:     MatSolverTypeHolders->handlers->getfactor[(int)ftype-1] = getfactor;
4253:     return(0);
4254:   }
4255:   while (next) {
4256:     PetscStrcasecmp(package,next->name,&flg);
4257:     if (flg) {
4258:       if (!next->handlers) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MatSolverTypeHolder is missing handlers");
4259:       inext = next->handlers;
4260:       while (inext) {
4261:         PetscStrcasecmp(mtype,inext->mtype,&flg);
4262:         if (flg) {
4263:           inext->getfactor[(int)ftype-1] = getfactor;
4264:           return(0);
4265:         }
4266:         iprev = inext;
4267:         inext = inext->next;
4268:       }
4269:       PetscNew(&iprev->next);
4270:       PetscStrallocpy(mtype,(char **)&iprev->next->mtype);
4271:       iprev->next->getfactor[(int)ftype-1] = getfactor;
4272:       return(0);
4273:     }
4274:     prev = next;
4275:     next = next->next;
4276:   }
4277:   PetscNew(&prev->next);
4278:   PetscStrallocpy(package,&prev->next->name);
4279:   PetscNew(&prev->next->handlers);
4280:   PetscStrallocpy(mtype,(char **)&prev->next->handlers->mtype);
4281:   prev->next->handlers->getfactor[(int)ftype-1] = getfactor;
4282:   return(0);
4283: }

4285: /*@C
4286:    MatSolvePackageGet - Get's the function that creates the factor matrix if it exist

4288:    Input Parameters:
4289: +    package - name of the package, for example petsc or superlu
4290: .    ftype - the type of factorization supported by the package
4291: -    mtype - the matrix type that works with this package

4293:    Output Parameters:
4294: +   foundpackage - PETSC_TRUE if the package was registered
4295: .   foundmtype - PETSC_TRUE if the package supports the requested mtype
4296: -   getfactor - routine that will create the factored matrix ready to be used or NULL if not found

4298:     Level: intermediate

4300: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4301: @*/
4302: PetscErrorCode MatSolverTypeGet(MatSolverType package,MatType mtype,MatFactorType ftype,PetscBool *foundpackage,PetscBool *foundmtype,PetscErrorCode (**getfactor)(Mat,MatFactorType,Mat*))
4303: {
4304:   PetscErrorCode                 ierr;
4305:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4306:   PetscBool                      flg;
4307:   MatSolverTypeForSpecifcType inext;

4310:   if (foundpackage) *foundpackage = PETSC_FALSE;
4311:   if (foundmtype)   *foundmtype   = PETSC_FALSE;
4312:   if (getfactor)    *getfactor    = NULL;

4314:   if (package) {
4315:     while (next) {
4316:       PetscStrcasecmp(package,next->name,&flg);
4317:       if (flg) {
4318:         if (foundpackage) *foundpackage = PETSC_TRUE;
4319:         inext = next->handlers;
4320:         while (inext) {
4321:           PetscStrbeginswith(mtype,inext->mtype,&flg);
4322:           if (flg) {
4323:             if (foundmtype) *foundmtype = PETSC_TRUE;
4324:             if (getfactor)  *getfactor  = inext->getfactor[(int)ftype-1];
4325:             return(0);
4326:           }
4327:           inext = inext->next;
4328:         }
4329:       }
4330:       next = next->next;
4331:     }
4332:   } else {
4333:     while (next) {
4334:       inext = next->handlers;
4335:       while (inext) {
4336:         PetscStrbeginswith(mtype,inext->mtype,&flg);
4337:         if (flg && inext->getfactor[(int)ftype-1]) {
4338:           if (foundpackage) *foundpackage = PETSC_TRUE;
4339:           if (foundmtype)   *foundmtype   = PETSC_TRUE;
4340:           if (getfactor)    *getfactor    = inext->getfactor[(int)ftype-1];
4341:           return(0);
4342:         }
4343:         inext = inext->next;
4344:       }
4345:       next = next->next;
4346:     }
4347:   }
4348:   return(0);
4349: }

4351: PetscErrorCode MatSolverTypeDestroy(void)
4352: {
4353:   PetscErrorCode              ierr;
4354:   MatSolverTypeHolder         next = MatSolverTypeHolders,prev;
4355:   MatSolverTypeForSpecifcType inext,iprev;

4358:   while (next) {
4359:     PetscFree(next->name);
4360:     inext = next->handlers;
4361:     while (inext) {
4362:       PetscFree(inext->mtype);
4363:       iprev = inext;
4364:       inext = inext->next;
4365:       PetscFree(iprev);
4366:     }
4367:     prev = next;
4368:     next = next->next;
4369:     PetscFree(prev);
4370:   }
4371:   MatSolverTypeHolders = NULL;
4372:   return(0);
4373: }

4375: /*@C
4376:    MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4378:    Collective on Mat

4380:    Input Parameters:
4381: +  mat - the matrix
4382: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4383: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4385:    Output Parameters:
4386: .  f - the factor matrix used with MatXXFactorSymbolic() calls

4388:    Notes:
4389:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4390:      such as pastix, superlu, mumps etc.

4392:       PETSc must have been ./configure to use the external solver, using the option --download-package

4394:    Level: intermediate

4396: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4397: @*/
4398: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type,MatFactorType ftype,Mat *f)
4399: {
4400:   PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*);
4401:   PetscBool      foundpackage,foundmtype;


4407:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4408:   MatCheckPreallocated(mat,1);

4410:   MatSolverTypeGet(type,((PetscObject)mat)->type_name,ftype,&foundpackage,&foundmtype,&conv);
4411:   if (!foundpackage) {
4412:     if (type) {
4413:       SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate solver package %s. Perhaps you must ./configure with --download-%s",type,type);
4414:     } else {
4415:       SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate a solver package. Perhaps you must ./configure with --download-<package>");
4416:     }
4417:   }

4419:   if (!foundmtype) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverType %s does not support matrix type %s",type,((PetscObject)mat)->type_name);
4420:   if (!conv) SETERRQ3(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverType %s does not support factorization type %s for  matrix type %s",type,MatFactorTypes[ftype],((PetscObject)mat)->type_name);

4422: #if defined(PETSC_USE_COMPLEX)
4423:   if (mat->hermitian && !mat->symmetric && (ftype == MAT_FACTOR_CHOLESKY||ftype == MAT_FACTOR_ICC)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Hermitian CHOLESKY or ICC Factor is not supported");
4424: #endif

4426:   (*conv)(mat,ftype,f);
4427:   return(0);
4428: }

4430: /*@C
4431:    MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type

4433:    Not Collective

4435:    Input Parameters:
4436: +  mat - the matrix
4437: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4438: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4440:    Output Parameter:
4441: .    flg - PETSC_TRUE if the factorization is available

4443:    Notes:
4444:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4445:      such as pastix, superlu, mumps etc.

4447:       PETSc must have been ./configure to use the external solver, using the option --download-package

4449:    Level: intermediate

4451: .seealso: MatCopy(), MatDuplicate(), MatGetFactor()
4452: @*/
4453: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type,MatFactorType ftype,PetscBool  *flg)
4454: {
4455:   PetscErrorCode ierr, (*gconv)(Mat,MatFactorType,Mat*);


4461:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4462:   MatCheckPreallocated(mat,1);

4464:   *flg = PETSC_FALSE;
4465:   MatSolverTypeGet(type,((PetscObject)mat)->type_name,ftype,NULL,NULL,&gconv);
4466:   if (gconv) {
4467:     *flg = PETSC_TRUE;
4468:   }
4469:   return(0);
4470: }

4472:  #include <petscdmtypes.h>

4474: /*@
4475:    MatDuplicate - Duplicates a matrix including the non-zero structure.

4477:    Collective on Mat

4479:    Input Parameters:
4480: +  mat - the matrix
4481: -  op - One of MAT_DO_NOT_COPY_VALUES, MAT_COPY_VALUES, or MAT_SHARE_NONZERO_PATTERN.
4482:         See the manual page for MatDuplicateOption for an explanation of these options.

4484:    Output Parameter:
4485: .  M - pointer to place new matrix

4487:    Level: intermediate

4489:    Concepts: matrices^duplicating

4491:    Notes:
4492:     You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN.

4494: .seealso: MatCopy(), MatConvert(), MatDuplicateOption
4495: @*/
4496: PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M)
4497: {
4499:   Mat            B;
4500:   PetscInt       i;
4501:   DM             dm;
4502:   void           (*viewf)(void);

4508:   if (op == MAT_COPY_VALUES && !mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MAT_COPY_VALUES not allowed for unassembled matrix");
4509:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4510:   MatCheckPreallocated(mat,1);

4512:   *M = 0;
4513:   if (!mat->ops->duplicate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not written for this matrix type");
4514:   PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4515:   (*mat->ops->duplicate)(mat,op,M);
4516:   B    = *M;

4518:   MatGetOperation(mat,MATOP_VIEW,&viewf);
4519:   if (viewf) {
4520:     MatSetOperation(B,MATOP_VIEW,viewf);
4521:   }

4523:   B->stencil.dim = mat->stencil.dim;
4524:   B->stencil.noc = mat->stencil.noc;
4525:   for (i=0; i<=mat->stencil.dim; i++) {
4526:     B->stencil.dims[i]   = mat->stencil.dims[i];
4527:     B->stencil.starts[i] = mat->stencil.starts[i];
4528:   }

4530:   B->nooffproczerorows = mat->nooffproczerorows;
4531:   B->nooffprocentries  = mat->nooffprocentries;

4533:   PetscObjectQuery((PetscObject) mat, "__PETSc_dm", (PetscObject*) &dm);
4534:   if (dm) {
4535:     PetscObjectCompose((PetscObject) B, "__PETSc_dm", (PetscObject) dm);
4536:   }
4537:   PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4538:   PetscObjectStateIncrease((PetscObject)B);
4539:   return(0);
4540: }

4542: /*@
4543:    MatGetDiagonal - Gets the diagonal of a matrix.

4545:    Logically Collective on Mat and Vec

4547:    Input Parameters:
4548: +  mat - the matrix
4549: -  v - the vector for storing the diagonal

4551:    Output Parameter:
4552: .  v - the diagonal of the matrix

4554:    Level: intermediate

4556:    Note:
4557:    Currently only correct in parallel for square matrices.

4559:    Concepts: matrices^accessing diagonals

4561: .seealso: MatGetRow(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs()
4562: @*/
4563: PetscErrorCode MatGetDiagonal(Mat mat,Vec v)
4564: {

4571:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4572:   if (!mat->ops->getdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4573:   MatCheckPreallocated(mat,1);

4575:   (*mat->ops->getdiagonal)(mat,v);
4576:   PetscObjectStateIncrease((PetscObject)v);
4577:   return(0);
4578: }

4580: /*@C
4581:    MatGetRowMin - Gets the minimum value (of the real part) of each
4582:         row of the matrix

4584:    Logically Collective on Mat and Vec

4586:    Input Parameters:
4587: .  mat - the matrix

4589:    Output Parameter:
4590: +  v - the vector for storing the maximums
4591: -  idx - the indices of the column found for each row (optional)

4593:    Level: intermediate

4595:    Notes:
4596:     The result of this call are the same as if one converted the matrix to dense format
4597:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4599:     This code is only implemented for a couple of matrix formats.

4601:    Concepts: matrices^getting row maximums

4603: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs(),
4604:           MatGetRowMax()
4605: @*/
4606: PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[])
4607: {

4614:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4615:   if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4616:   MatCheckPreallocated(mat,1);

4618:   (*mat->ops->getrowmin)(mat,v,idx);
4619:   PetscObjectStateIncrease((PetscObject)v);
4620:   return(0);
4621: }

4623: /*@C
4624:    MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4625:         row of the matrix

4627:    Logically Collective on Mat and Vec

4629:    Input Parameters:
4630: .  mat - the matrix

4632:    Output Parameter:
4633: +  v - the vector for storing the minimums
4634: -  idx - the indices of the column found for each row (or NULL if not needed)

4636:    Level: intermediate

4638:    Notes:
4639:     if a row is completely empty or has only 0.0 values then the idx[] value for that
4640:     row is 0 (the first column).

4642:     This code is only implemented for a couple of matrix formats.

4644:    Concepts: matrices^getting row maximums

4646: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin()
4647: @*/
4648: PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[])
4649: {

4656:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4657:   if (!mat->ops->getrowminabs) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4658:   MatCheckPreallocated(mat,1);
4659:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4661:   (*mat->ops->getrowminabs)(mat,v,idx);
4662:   PetscObjectStateIncrease((PetscObject)v);
4663:   return(0);
4664: }

4666: /*@C
4667:    MatGetRowMax - Gets the maximum value (of the real part) of each
4668:         row of the matrix

4670:    Logically Collective on Mat and Vec

4672:    Input Parameters:
4673: .  mat - the matrix

4675:    Output Parameter:
4676: +  v - the vector for storing the maximums
4677: -  idx - the indices of the column found for each row (optional)

4679:    Level: intermediate

4681:    Notes:
4682:     The result of this call are the same as if one converted the matrix to dense format
4683:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4685:     This code is only implemented for a couple of matrix formats.

4687:    Concepts: matrices^getting row maximums

4689: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs(), MatGetRowMin()
4690: @*/
4691: PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[])
4692: {

4699:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4700:   if (!mat->ops->getrowmax) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4701:   MatCheckPreallocated(mat,1);

4703:   (*mat->ops->getrowmax)(mat,v,idx);
4704:   PetscObjectStateIncrease((PetscObject)v);
4705:   return(0);
4706: }

4708: /*@C
4709:    MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
4710:         row of the matrix

4712:    Logically Collective on Mat and Vec

4714:    Input Parameters:
4715: .  mat - the matrix

4717:    Output Parameter:
4718: +  v - the vector for storing the maximums
4719: -  idx - the indices of the column found for each row (or NULL if not needed)

4721:    Level: intermediate

4723:    Notes:
4724:     if a row is completely empty or has only 0.0 values then the idx[] value for that
4725:     row is 0 (the first column).

4727:     This code is only implemented for a couple of matrix formats.

4729:    Concepts: matrices^getting row maximums

4731: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMin()
4732: @*/
4733: PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[])
4734: {

4741:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4742:   if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4743:   MatCheckPreallocated(mat,1);
4744:   if (idx) {PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));}

4746:   (*mat->ops->getrowmaxabs)(mat,v,idx);
4747:   PetscObjectStateIncrease((PetscObject)v);
4748:   return(0);
4749: }

4751: /*@
4752:    MatGetRowSum - Gets the sum of each row of the matrix

4754:    Logically or Neighborhood Collective on Mat and Vec

4756:    Input Parameters:
4757: .  mat - the matrix

4759:    Output Parameter:
4760: .  v - the vector for storing the sum of rows

4762:    Level: intermediate

4764:    Notes:
4765:     This code is slow since it is not currently specialized for different formats

4767:    Concepts: matrices^getting row sums

4769: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMin()
4770: @*/
4771: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
4772: {
4773:   Vec            ones;

4780:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4781:   MatCheckPreallocated(mat,1);
4782:   MatCreateVecs(mat,&ones,NULL);
4783:   VecSet(ones,1.);
4784:   MatMult(mat,ones,v);
4785:   VecDestroy(&ones);
4786:   return(0);
4787: }

4789: /*@
4790:    MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

4792:    Collective on Mat

4794:    Input Parameter:
4795: +  mat - the matrix to transpose
4796: -  reuse - either MAT_INITIAL_MATRIX, MAT_REUSE_MATRIX, or MAT_INPLACE_MATRIX

4798:    Output Parameters:
4799: .  B - the transpose

4801:    Notes:
4802:      If you use MAT_INPLACE_MATRIX then you must pass in &mat for B

4804:      MAT_REUSE_MATRIX causes the B matrix from a previous call to this function with MAT_INITIAL_MATRIX to be used

4806:      Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

4808:    Level: intermediate

4810:    Concepts: matrices^transposing

4812: .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4813: @*/
4814: PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B)
4815: {

4821:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4822:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4823:   if (!mat->ops->transpose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4824:   if (reuse == MAT_INPLACE_MATRIX && mat != *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires last matrix to match first");
4825:   if (reuse == MAT_REUSE_MATRIX && mat == *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Perhaps you mean MAT_INPLACE_MATRIX");
4826:   MatCheckPreallocated(mat,1);

4828:   PetscLogEventBegin(MAT_Transpose,mat,0,0,0);
4829:   (*mat->ops->transpose)(mat,reuse,B);
4830:   PetscLogEventEnd(MAT_Transpose,mat,0,0,0);
4831:   if (B) {PetscObjectStateIncrease((PetscObject)*B);}
4832:   return(0);
4833: }

4835: /*@
4836:    MatIsTranspose - Test whether a matrix is another one's transpose,
4837:         or its own, in which case it tests symmetry.

4839:    Collective on Mat

4841:    Input Parameter:
4842: +  A - the matrix to test
4843: -  B - the matrix to test against, this can equal the first parameter

4845:    Output Parameters:
4846: .  flg - the result

4848:    Notes:
4849:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4850:    has a running time of the order of the number of nonzeros; the parallel
4851:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4853:    Level: intermediate

4855:    Concepts: matrices^transposing, matrix^symmetry

4857: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian()
4858: @*/
4859: PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4860: {
4861:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4867:   PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",&f);
4868:   PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",&g);
4869:   *flg = PETSC_FALSE;
4870:   if (f && g) {
4871:     if (f == g) {
4872:       (*f)(A,B,tol,flg);
4873:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test");
4874:   } else {
4875:     MatType mattype;
4876:     if (!f) {
4877:       MatGetType(A,&mattype);
4878:     } else {
4879:       MatGetType(B,&mattype);
4880:     }
4881:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for transpose",mattype);
4882:   }
4883:   return(0);
4884: }

4886: /*@
4887:    MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate.

4889:    Collective on Mat

4891:    Input Parameter:
4892: +  mat - the matrix to transpose and complex conjugate
4893: -  reuse - MAT_INITIAL_MATRIX to create a new matrix, MAT_INPLACE_MATRIX to reuse the first argument to store the transpose

4895:    Output Parameters:
4896: .  B - the Hermitian

4898:    Level: intermediate

4900:    Concepts: matrices^transposing, complex conjugatex

4902: .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4903: @*/
4904: PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B)
4905: {

4909:   MatTranspose(mat,reuse,B);
4910: #if defined(PETSC_USE_COMPLEX)
4911:   MatConjugate(*B);
4912: #endif
4913:   return(0);
4914: }

4916: /*@
4917:    MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

4919:    Collective on Mat

4921:    Input Parameter:
4922: +  A - the matrix to test
4923: -  B - the matrix to test against, this can equal the first parameter

4925:    Output Parameters:
4926: .  flg - the result

4928:    Notes:
4929:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4930:    has a running time of the order of the number of nonzeros; the parallel
4931:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4933:    Level: intermediate

4935:    Concepts: matrices^transposing, matrix^symmetry

4937: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose()
4938: @*/
4939: PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4940: {
4941:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4947:   PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",&f);
4948:   PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",&g);
4949:   if (f && g) {
4950:     if (f==g) {
4951:       (*f)(A,B,tol,flg);
4952:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test");
4953:   }
4954:   return(0);
4955: }

4957: /*@
4958:    MatPermute - Creates a new matrix with rows and columns permuted from the
4959:    original.

4961:    Collective on Mat

4963:    Input Parameters:
4964: +  mat - the matrix to permute
4965: .  row - row permutation, each processor supplies only the permutation for its rows
4966: -  col - column permutation, each processor supplies only the permutation for its columns

4968:    Output Parameters:
4969: .  B - the permuted matrix

4971:    Level: advanced

4973:    Note:
4974:    The index sets map from row/col of permuted matrix to row/col of original matrix.
4975:    The index sets should be on the same communicator as Mat and have the same local sizes.

4977:    Concepts: matrices^permuting

4979: .seealso: MatGetOrdering(), ISAllGather()

4981: @*/
4982: PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B)
4983: {

4992:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4993:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4994:   if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name);
4995:   MatCheckPreallocated(mat,1);

4997:   (*mat->ops->permute)(mat,row,col,B);
4998:   PetscObjectStateIncrease((PetscObject)*B);
4999:   return(0);
5000: }

5002: /*@
5003:    MatEqual - Compares two matrices.

5005:    Collective on Mat

5007:    Input Parameters:
5008: +  A - the first matrix
5009: -  B - the second matrix

5011:    Output Parameter:
5012: .  flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise.

5014:    Level: intermediate

5016:    Concepts: matrices^equality between
5017: @*/
5018: PetscErrorCode MatEqual(Mat A,Mat B,PetscBool  *flg)
5019: {

5029:   MatCheckPreallocated(B,2);
5030:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5031:   if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5032:   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);
5033:   if (!A->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
5034:   if (!B->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name);
5035:   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);
5036:   MatCheckPreallocated(A,1);

5038:   (*A->ops->equal)(A,B,flg);
5039:   return(0);
5040: }

5042: /*@
5043:    MatDiagonalScale - Scales a matrix on the left and right by diagonal
5044:    matrices that are stored as vectors.  Either of the two scaling
5045:    matrices can be NULL.

5047:    Collective on Mat

5049:    Input Parameters:
5050: +  mat - the matrix to be scaled
5051: .  l - the left scaling vector (or NULL)
5052: -  r - the right scaling vector (or NULL)

5054:    Notes:
5055:    MatDiagonalScale() computes A = LAR, where
5056:    L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5057:    The L scales the rows of the matrix, the R scales the columns of the matrix.

5059:    Level: intermediate

5061:    Concepts: matrices^diagonal scaling
5062:    Concepts: diagonal scaling of matrices

5064: .seealso: MatScale(), MatShift(), MatDiagonalSet()
5065: @*/
5066: PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r)
5067: {

5073:   if (!mat->ops->diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5076:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5077:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5078:   MatCheckPreallocated(mat,1);

5080:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
5081:   (*mat->ops->diagonalscale)(mat,l,r);
5082:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
5083:   PetscObjectStateIncrease((PetscObject)mat);
5084: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5085:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5086:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5087:   }
5088: #endif
5089:   return(0);
5090: }

5092: /*@
5093:     MatScale - Scales all elements of a matrix by a given number.

5095:     Logically Collective on Mat

5097:     Input Parameters:
5098: +   mat - the matrix to be scaled
5099: -   a  - the scaling value

5101:     Output Parameter:
5102: .   mat - the scaled matrix

5104:     Level: intermediate

5106:     Concepts: matrices^scaling all entries

5108: .seealso: MatDiagonalScale()
5109: @*/
5110: PetscErrorCode MatScale(Mat mat,PetscScalar a)
5111: {

5117:   if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5118:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5119:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5121:   MatCheckPreallocated(mat,1);

5123:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
5124:   if (a != (PetscScalar)1.0) {
5125:     (*mat->ops->scale)(mat,a);
5126:     PetscObjectStateIncrease((PetscObject)mat);
5127: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5128:     if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5129:       mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5130:     }
5131: #endif
5132:   }
5133:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
5134:   return(0);
5135: }

5137: static PetscErrorCode MatNorm_Basic(Mat A,NormType type,PetscReal *nrm)
5138: {

5142:   if (type == NORM_1 || type == NORM_INFINITY) {
5143:     Vec l,r;

5145:     MatCreateVecs(A,&r,&l);
5146:     if (type == NORM_INFINITY) {
5147:       VecSet(r,1.);
5148:       MatMult(A,r,l);
5149:       VecNorm(l,NORM_INFINITY,nrm);
5150:     } else {
5151:       VecSet(l,1.);
5152:       MatMultTranspose(A,l,r);
5153:       VecNorm(r,NORM_INFINITY,nrm);
5154:     }
5155:     VecDestroy(&l);
5156:     VecDestroy(&r);
5157:   } else SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix class %s, norm type %d",((PetscObject)A)->type_name,type);
5158:   return(0);
5159: }

5161: /*@
5162:    MatNorm - Calculates various norms of a matrix.

5164:    Collective on Mat

5166:    Input Parameters:
5167: +  mat - the matrix
5168: -  type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY

5170:    Output Parameters:
5171: .  nrm - the resulting norm

5173:    Level: intermediate

5175:    Concepts: matrices^norm
5176:    Concepts: norm^of matrix
5177: @*/
5178: PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm)
5179: {


5188:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5189:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5190:   MatCheckPreallocated(mat,1);

5192:   if (!mat->ops->norm) {
5193:     MatNorm_Basic(mat,type,nrm);
5194:   } else {
5195:     (*mat->ops->norm)(mat,type,nrm);
5196:   }
5197:   return(0);
5198: }

5200: /*
5201:      This variable is used to prevent counting of MatAssemblyBegin() that
5202:    are called from within a MatAssemblyEnd().
5203: */
5204: static PetscInt MatAssemblyEnd_InUse = 0;
5205: /*@
5206:    MatAssemblyBegin - Begins assembling the matrix.  This routine should
5207:    be called after completing all calls to MatSetValues().

5209:    Collective on Mat

5211:    Input Parameters:
5212: +  mat - the matrix
5213: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5215:    Notes:
5216:    MatSetValues() generally caches the values.  The matrix is ready to
5217:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5218:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5219:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5220:    using the matrix.

5222:    ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the
5223:    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
5224:    a global collective operation requring all processes that share the matrix.

5226:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5227:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5228:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5230:    Level: beginner

5232:    Concepts: matrices^assembling

5234: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled()
5235: @*/
5236: PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type)
5237: {

5243:   MatCheckPreallocated(mat,1);
5244:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5245:   if (mat->assembled) {
5246:     mat->was_assembled = PETSC_TRUE;
5247:     mat->assembled     = PETSC_FALSE;
5248:   }
5249:   if (!MatAssemblyEnd_InUse) {
5250:     PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);
5251:     if (mat->ops->assemblybegin) {(*mat->ops->assemblybegin)(mat,type);}
5252:     PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);
5253:   } else if (mat->ops->assemblybegin) {
5254:     (*mat->ops->assemblybegin)(mat,type);
5255:   }
5256:   return(0);
5257: }

5259: /*@
5260:    MatAssembled - Indicates if a matrix has been assembled and is ready for
5261:      use; for example, in matrix-vector product.

5263:    Not Collective

5265:    Input Parameter:
5266: .  mat - the matrix

5268:    Output Parameter:
5269: .  assembled - PETSC_TRUE or PETSC_FALSE

5271:    Level: advanced

5273:    Concepts: matrices^assembled?

5275: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin()
5276: @*/
5277: PetscErrorCode MatAssembled(Mat mat,PetscBool  *assembled)
5278: {
5283:   *assembled = mat->assembled;
5284:   return(0);
5285: }

5287: /*@
5288:    MatAssemblyEnd - Completes assembling the matrix.  This routine should
5289:    be called after MatAssemblyBegin().

5291:    Collective on Mat

5293:    Input Parameters:
5294: +  mat - the matrix
5295: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5297:    Options Database Keys:
5298: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly()
5299: .  -mat_view ::ascii_info_detail - Prints more detailed info
5300: .  -mat_view - Prints matrix in ASCII format
5301: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
5302: .  -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX().
5303: .  -display <name> - Sets display name (default is host)
5304: .  -draw_pause <sec> - Sets number of seconds to pause after display
5305: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See Users-Manual: Chapter 12 Using MATLAB with PETSc )
5306: .  -viewer_socket_machine <machine> - Machine to use for socket
5307: .  -viewer_socket_port <port> - Port number to use for socket
5308: -  -mat_view binary:filename[:append] - Save matrix to file in binary format

5310:    Notes:
5311:    MatSetValues() generally caches the values.  The matrix is ready to
5312:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5313:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5314:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5315:    using the matrix.

5317:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5318:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5319:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5321:    Level: beginner

5323: .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), PetscDrawCreate(), MatView(), MatAssembled(), PetscViewerSocketOpen()
5324: @*/
5325: PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type)
5326: {
5327:   PetscErrorCode  ierr;
5328:   static PetscInt inassm = 0;
5329:   PetscBool       flg    = PETSC_FALSE;


5335:   inassm++;
5336:   MatAssemblyEnd_InUse++;
5337:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5338:     PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);
5339:     if (mat->ops->assemblyend) {
5340:       (*mat->ops->assemblyend)(mat,type);
5341:     }
5342:     PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);
5343:   } else if (mat->ops->assemblyend) {
5344:     (*mat->ops->assemblyend)(mat,type);
5345:   }

5347:   /* Flush assembly is not a true assembly */
5348:   if (type != MAT_FLUSH_ASSEMBLY) {
5349:     mat->assembled = PETSC_TRUE; mat->num_ass++;
5350:   }
5351:   mat->insertmode = NOT_SET_VALUES;
5352:   MatAssemblyEnd_InUse--;
5353:   PetscObjectStateIncrease((PetscObject)mat);
5354:   if (!mat->symmetric_eternal) {
5355:     mat->symmetric_set              = PETSC_FALSE;
5356:     mat->hermitian_set              = PETSC_FALSE;
5357:     mat->structurally_symmetric_set = PETSC_FALSE;
5358:   }
5359: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5360:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5361:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5362:   }
5363: #endif
5364:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5365:     MatViewFromOptions(mat,NULL,"-mat_view");

5367:     if (mat->checksymmetryonassembly) {
5368:       MatIsSymmetric(mat,mat->checksymmetrytol,&flg);
5369:       if (flg) {
5370:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5371:       } else {
5372:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is not symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5373:       }
5374:     }
5375:     if (mat->nullsp && mat->checknullspaceonassembly) {
5376:       MatNullSpaceTest(mat->nullsp,mat,NULL);
5377:     }
5378:   }
5379:   inassm--;
5380:   return(0);
5381: }

5383: /*@
5384:    MatSetOption - Sets a parameter option for a matrix. Some options
5385:    may be specific to certain storage formats.  Some options
5386:    determine how values will be inserted (or added). Sorted,
5387:    row-oriented input will generally assemble the fastest. The default
5388:    is row-oriented.

5390:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5392:    Input Parameters:
5393: +  mat - the matrix
5394: .  option - the option, one of those listed below (and possibly others),
5395: -  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5397:   Options Describing Matrix Structure:
5398: +    MAT_SPD - symmetric positive definite
5399: .    MAT_SYMMETRIC - symmetric in terms of both structure and value
5400: .    MAT_HERMITIAN - transpose is the complex conjugation
5401: .    MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure
5402: -    MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag
5403:                             you set to be kept with all future use of the matrix
5404:                             including after MatAssemblyBegin/End() which could
5405:                             potentially change the symmetry structure, i.e. you
5406:                             KNOW the matrix will ALWAYS have the property you set.


5409:    Options For Use with MatSetValues():
5410:    Insert a logically dense subblock, which can be
5411: .    MAT_ROW_ORIENTED - row-oriented (default)

5413:    Note these options reflect the data you pass in with MatSetValues(); it has
5414:    nothing to do with how the data is stored internally in the matrix
5415:    data structure.

5417:    When (re)assembling a matrix, we can restrict the input for
5418:    efficiency/debugging purposes.  These options include:
5419: +    MAT_NEW_NONZERO_LOCATIONS - additional insertions will be allowed if they generate a new nonzero (slow)
5420: .    MAT_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only)
5421: .    MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries
5422: .    MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry
5423: .    MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly
5424: .    MAT_NO_OFF_PROC_ENTRIES - you know each process will only set values for its own rows, will generate an error if
5425:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5426:         performance for very large process counts.
5427: -    MAT_SUBSET_OFF_PROC_ENTRIES - you know that the first assembly after setting this flag will set a superset
5428:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5429:         functions, instead sending only neighbor messages.

5431:    Notes:
5432:    Except for MAT_UNUSED_NONZERO_LOCATION_ERR and  MAT_ROW_ORIENTED all processes that share the matrix must pass the same value in flg!

5434:    Some options are relevant only for particular matrix types and
5435:    are thus ignored by others.  Other options are not supported by
5436:    certain matrix types and will generate an error message if set.

5438:    If using a Fortran 77 module to compute a matrix, one may need to
5439:    use the column-oriented option (or convert to the row-oriented
5440:    format).

5442:    MAT_NEW_NONZERO_LOCATIONS set to PETSC_FALSE indicates that any add or insertion
5443:    that would generate a new entry in the nonzero structure is instead
5444:    ignored.  Thus, if memory has not alredy been allocated for this particular
5445:    data, then the insertion is ignored. For dense matrices, in which
5446:    the entire array is allocated, no entries are ever ignored.
5447:    Set after the first MatAssemblyEnd(). If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5449:    MAT_NEW_NONZERO_LOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5450:    that would generate a new entry in the nonzero structure instead produces
5451:    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

5453:    MAT_NEW_NONZERO_ALLOCATION_ERR set to PETSC_TRUE indicates that any add or insertion
5454:    that would generate a new entry that has not been preallocated will
5455:    instead produce an error. (Currently supported for AIJ and BAIJ formats
5456:    only.) This is a useful flag when debugging matrix memory preallocation.
5457:    If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5459:    MAT_IGNORE_OFF_PROC_ENTRIES set to PETSC_TRUE indicates entries destined for
5460:    other processors should be dropped, rather than stashed.
5461:    This is useful if you know that the "owning" processor is also
5462:    always generating the correct matrix entries, so that PETSc need
5463:    not transfer duplicate entries generated on another processor.

5465:    MAT_USE_HASH_TABLE indicates that a hash table be used to improve the
5466:    searches during matrix assembly. When this flag is set, the hash table
5467:    is created during the first Matrix Assembly. This hash table is
5468:    used the next time through, during MatSetVaules()/MatSetVaulesBlocked()
5469:    to improve the searching of indices. MAT_NEW_NONZERO_LOCATIONS flag
5470:    should be used with MAT_USE_HASH_TABLE flag. This option is currently
5471:    supported by MATMPIBAIJ format only.

5473:    MAT_KEEP_NONZERO_PATTERN indicates when MatZeroRows() is called the zeroed entries
5474:    are kept in the nonzero structure

5476:    MAT_IGNORE_ZERO_ENTRIES - for AIJ/IS matrices this will stop zero values from creating
5477:    a zero location in the matrix

5479:    MAT_USE_INODES - indicates using inode version of the code - works with AIJ matrix types

5481:    MAT_NO_OFF_PROC_ZERO_ROWS - you know each process will only zero its own rows. This avoids all reductions in the
5482:         zero row routines and thus improves performance for very large process counts.

5484:    MAT_IGNORE_LOWER_TRIANGULAR - For SBAIJ matrices will ignore any insertions you make in the lower triangular
5485:         part of the matrix (since they should match the upper triangular part).

5487:    Notes:
5488:     Can only be called after MatSetSizes() and MatSetType() have been set.

5490:    Level: intermediate

5492:    Concepts: matrices^setting options

5494: .seealso:  MatOption, Mat

5496: @*/
5497: PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg)
5498: {

5504:   if (op > 0) {
5507:   }

5509:   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);
5510:   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()");

5512:   switch (op) {
5513:   case MAT_NO_OFF_PROC_ENTRIES:
5514:     mat->nooffprocentries = flg;
5515:     return(0);
5516:     break;
5517:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5518:     mat->subsetoffprocentries = flg;
5519:     return(0);
5520:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5521:     mat->nooffproczerorows = flg;
5522:     return(0);
5523:     break;
5524:   case MAT_SPD:
5525:     mat->spd_set = PETSC_TRUE;
5526:     mat->spd     = flg;
5527:     if (flg) {
5528:       mat->symmetric                  = PETSC_TRUE;
5529:       mat->structurally_symmetric     = PETSC_TRUE;
5530:       mat->symmetric_set              = PETSC_TRUE;
5531:       mat->structurally_symmetric_set = PETSC_TRUE;
5532:     }
5533:     break;
5534:   case MAT_SYMMETRIC:
5535:     mat->symmetric = flg;
5536:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5537:     mat->symmetric_set              = PETSC_TRUE;
5538:     mat->structurally_symmetric_set = flg;
5539: #if !defined(PETSC_USE_COMPLEX)
5540:     mat->hermitian     = flg;
5541:     mat->hermitian_set = PETSC_TRUE;
5542: #endif
5543:     break;
5544:   case MAT_HERMITIAN:
5545:     mat->hermitian = flg;
5546:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5547:     mat->hermitian_set              = PETSC_TRUE;
5548:     mat->structurally_symmetric_set = flg;
5549: #if !defined(PETSC_USE_COMPLEX)
5550:     mat->symmetric     = flg;
5551:     mat->symmetric_set = PETSC_TRUE;
5552: #endif
5553:     break;
5554:   case MAT_STRUCTURALLY_SYMMETRIC:
5555:     mat->structurally_symmetric     = flg;
5556:     mat->structurally_symmetric_set = PETSC_TRUE;
5557:     break;
5558:   case MAT_SYMMETRY_ETERNAL:
5559:     mat->symmetric_eternal = flg;
5560:     break;
5561:   case MAT_STRUCTURE_ONLY:
5562:     mat->structure_only = flg;
5563:     break;
5564:   default:
5565:     break;
5566:   }
5567:   if (mat->ops->setoption) {
5568:     (*mat->ops->setoption)(mat,op,flg);
5569:   }
5570:   return(0);
5571: }

5573: /*@
5574:    MatGetOption - Gets a parameter option that has been set for a matrix.

5576:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5578:    Input Parameters:
5579: +  mat - the matrix
5580: -  option - the option, this only responds to certain options, check the code for which ones

5582:    Output Parameter:
5583: .  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5585:     Notes:
5586:     Can only be called after MatSetSizes() and MatSetType() have been set.

5588:    Level: intermediate

5590:    Concepts: matrices^setting options

5592: .seealso:  MatOption, MatSetOption()

5594: @*/
5595: PetscErrorCode MatGetOption(Mat mat,MatOption op,PetscBool *flg)
5596: {

5601:   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);
5602:   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()");

5604:   switch (op) {
5605:   case MAT_NO_OFF_PROC_ENTRIES:
5606:     *flg = mat->nooffprocentries;
5607:     break;
5608:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5609:     *flg = mat->nooffproczerorows;
5610:     break;
5611:   case MAT_SYMMETRIC:
5612:     *flg = mat->symmetric;
5613:     break;
5614:   case MAT_HERMITIAN:
5615:     *flg = mat->hermitian;
5616:     break;
5617:   case MAT_STRUCTURALLY_SYMMETRIC:
5618:     *flg = mat->structurally_symmetric;
5619:     break;
5620:   case MAT_SYMMETRY_ETERNAL:
5621:     *flg = mat->symmetric_eternal;
5622:     break;
5623:   case MAT_SPD:
5624:     *flg = mat->spd;
5625:     break;
5626:   default:
5627:     break;
5628:   }
5629:   return(0);
5630: }

5632: /*@
5633:    MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
5634:    this routine retains the old nonzero structure.

5636:    Logically Collective on Mat

5638:    Input Parameters:
5639: .  mat - the matrix

5641:    Level: intermediate

5643:    Notes:
5644:     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.
5645:    See the Performance chapter of the users manual for information on preallocating matrices.

5647:    Concepts: matrices^zeroing

5649: .seealso: MatZeroRows()
5650: @*/
5651: PetscErrorCode MatZeroEntries(Mat mat)
5652: {

5658:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5659:   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");
5660:   if (!mat->ops->zeroentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5661:   MatCheckPreallocated(mat,1);

5663:   PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);
5664:   (*mat->ops->zeroentries)(mat);
5665:   PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);
5666:   PetscObjectStateIncrease((PetscObject)mat);
5667: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5668:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5669:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5670:   }
5671: #endif
5672:   return(0);
5673: }

5675: /*@
5676:    MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
5677:    of a set of rows and columns of a matrix.

5679:    Collective on Mat

5681:    Input Parameters:
5682: +  mat - the matrix
5683: .  numRows - the number of rows to remove
5684: .  rows - the global row indices
5685: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5686: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5687: -  b - optional vector of right hand side, that will be adjusted by provided solution

5689:    Notes:
5690:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5692:    The user can set a value in the diagonal entry (or for the AIJ and
5693:    row formats can optionally remove the main diagonal entry from the
5694:    nonzero structure as well, by passing 0.0 as the final argument).

5696:    For the parallel case, all processes that share the matrix (i.e.,
5697:    those in the communicator used for matrix creation) MUST call this
5698:    routine, regardless of whether any rows being zeroed are owned by
5699:    them.

5701:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5702:    list only rows local to itself).

5704:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5706:    Level: intermediate

5708:    Concepts: matrices^zeroing rows

5710: .seealso: MatZeroRowsIS(), MatZeroRows(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5711:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5712: @*/
5713: PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5714: {

5721:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5722:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5723:   if (!mat->ops->zerorowscolumns) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5724:   MatCheckPreallocated(mat,1);

5726:   (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);
5727:   MatViewFromOptions(mat,NULL,"-mat_view");
5728:   PetscObjectStateIncrease((PetscObject)mat);
5729: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5730:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5731:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5732:   }
5733: #endif
5734:   return(0);
5735: }

5737: /*@
5738:    MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
5739:    of a set of rows and columns of a matrix.

5741:    Collective on Mat

5743:    Input Parameters:
5744: +  mat - the matrix
5745: .  is - the rows to zero
5746: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5747: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5748: -  b - optional vector of right hand side, that will be adjusted by provided solution

5750:    Notes:
5751:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5753:    The user can set a value in the diagonal entry (or for the AIJ and
5754:    row formats can optionally remove the main diagonal entry from the
5755:    nonzero structure as well, by passing 0.0 as the final argument).

5757:    For the parallel case, all processes that share the matrix (i.e.,
5758:    those in the communicator used for matrix creation) MUST call this
5759:    routine, regardless of whether any rows being zeroed are owned by
5760:    them.

5762:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5763:    list only rows local to itself).

5765:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5767:    Level: intermediate

5769:    Concepts: matrices^zeroing rows

5771: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5772:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRows(), MatZeroRowsColumnsStencil()
5773: @*/
5774: PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5775: {
5777:   PetscInt       numRows;
5778:   const PetscInt *rows;

5785:   ISGetLocalSize(is,&numRows);
5786:   ISGetIndices(is,&rows);
5787:   MatZeroRowsColumns(mat,numRows,rows,diag,x,b);
5788:   ISRestoreIndices(is,&rows);
5789:   return(0);
5790: }

5792: /*@
5793:    MatZeroRows - Zeros all entries (except possibly the main diagonal)
5794:    of a set of rows of a matrix.

5796:    Collective on Mat

5798:    Input Parameters:
5799: +  mat - the matrix
5800: .  numRows - the number of rows to remove
5801: .  rows - the global row indices
5802: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5803: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5804: -  b - optional vector of right hand side, that will be adjusted by provided solution

5806:    Notes:
5807:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5808:    but does not release memory.  For the dense and block diagonal
5809:    formats this does not alter the nonzero structure.

5811:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5812:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5813:    merely zeroed.

5815:    The user can set a value in the diagonal entry (or for the AIJ and
5816:    row formats can optionally remove the main diagonal entry from the
5817:    nonzero structure as well, by passing 0.0 as the final argument).

5819:    For the parallel case, all processes that share the matrix (i.e.,
5820:    those in the communicator used for matrix creation) MUST call this
5821:    routine, regardless of whether any rows being zeroed are owned by
5822:    them.

5824:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5825:    list only rows local to itself).

5827:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5828:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5830:    Level: intermediate

5832:    Concepts: matrices^zeroing rows

5834: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5835:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5836: @*/
5837: PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5838: {

5845:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5846:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5847:   if (!mat->ops->zerorows) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5848:   MatCheckPreallocated(mat,1);

5850:   (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);
5851:   MatViewFromOptions(mat,NULL,"-mat_view");
5852:   PetscObjectStateIncrease((PetscObject)mat);
5853: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
5854:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
5855:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
5856:   }
5857: #endif
5858:   return(0);
5859: }

5861: /*@
5862:    MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
5863:    of a set of rows of a matrix.

5865:    Collective on Mat

5867:    Input Parameters:
5868: +  mat - the matrix
5869: .  is - index set of rows to remove
5870: .  diag - value put in all diagonals of eliminated rows
5871: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5872: -  b - optional vector of right hand side, that will be adjusted by provided solution

5874:    Notes:
5875:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5876:    but does not release memory.  For the dense and block diagonal
5877:    formats this does not alter the nonzero structure.

5879:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5880:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5881:    merely zeroed.

5883:    The user can set a value in the diagonal entry (or for the AIJ and
5884:    row formats can optionally remove the main diagonal entry from the
5885:    nonzero structure as well, by passing 0.0 as the final argument).

5887:    For the parallel case, all processes that share the matrix (i.e.,
5888:    those in the communicator used for matrix creation) MUST call this
5889:    routine, regardless of whether any rows being zeroed are owned by
5890:    them.

5892:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5893:    list only rows local to itself).

5895:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5896:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5898:    Level: intermediate

5900:    Concepts: matrices^zeroing rows

5902: .seealso: MatZeroRows(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5903:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5904: @*/
5905: PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5906: {
5907:   PetscInt       numRows;
5908:   const PetscInt *rows;

5915:   ISGetLocalSize(is,&numRows);
5916:   ISGetIndices(is,&rows);
5917:   MatZeroRows(mat,numRows,rows,diag,x,b);
5918:   ISRestoreIndices(is,&rows);
5919:   return(0);
5920: }

5922: /*@
5923:    MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
5924:    of a set of rows of a matrix. These rows must be local to the process.

5926:    Collective on Mat

5928:    Input Parameters:
5929: +  mat - the matrix
5930: .  numRows - the number of rows to remove
5931: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5932: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5933: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5934: -  b - optional vector of right hand side, that will be adjusted by provided solution

5936:    Notes:
5937:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5938:    but does not release memory.  For the dense and block diagonal
5939:    formats this does not alter the nonzero structure.

5941:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5942:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5943:    merely zeroed.

5945:    The user can set a value in the diagonal entry (or for the AIJ and
5946:    row formats can optionally remove the main diagonal entry from the
5947:    nonzero structure as well, by passing 0.0 as the final argument).

5949:    For the parallel case, all processes that share the matrix (i.e.,
5950:    those in the communicator used for matrix creation) MUST call this
5951:    routine, regardless of whether any rows being zeroed are owned by
5952:    them.

5954:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5955:    list only rows local to itself).

5957:    The grid coordinates are across the entire grid, not just the local portion

5959:    In Fortran idxm and idxn should be declared as
5960: $     MatStencil idxm(4,m)
5961:    and the values inserted using
5962: $    idxm(MatStencil_i,1) = i
5963: $    idxm(MatStencil_j,1) = j
5964: $    idxm(MatStencil_k,1) = k
5965: $    idxm(MatStencil_c,1) = c
5966:    etc

5968:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5969:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5970:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5971:    DM_BOUNDARY_PERIODIC boundary type.

5973:    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
5974:    a single value per point) you can skip filling those indices.

5976:    Level: intermediate

5978:    Concepts: matrices^zeroing rows

5980: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsl(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5981:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5982: @*/
5983: PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5984: {
5985:   PetscInt       dim     = mat->stencil.dim;
5986:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5987:   PetscInt       *dims   = mat->stencil.dims+1;
5988:   PetscInt       *starts = mat->stencil.starts;
5989:   PetscInt       *dxm    = (PetscInt*) rows;
5990:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


5998:   PetscMalloc1(numRows, &jdxm);
5999:   for (i = 0; i < numRows; ++i) {
6000:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6001:     for (j = 0; j < 3-sdim; ++j) dxm++;
6002:     /* Local index in X dir */
6003:     tmp = *dxm++ - starts[0];
6004:     /* Loop over remaining dimensions */
6005:     for (j = 0; j < dim-1; ++j) {
6006:       /* If nonlocal, set index to be negative */
6007:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6008:       /* Update local index */
6009:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
6010:     }
6011:     /* Skip component slot if necessary */
6012:     if (mat->stencil.noc) dxm++;
6013:     /* Local row number */
6014:     if (tmp >= 0) {
6015:       jdxm[numNewRows++] = tmp;
6016:     }
6017:   }
6018:   MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);
6019:   PetscFree(jdxm);
6020:   return(0);
6021: }

6023: /*@
6024:    MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6025:    of a set of rows and columns of a matrix.

6027:    Collective on Mat

6029:    Input Parameters:
6030: +  mat - the matrix
6031: .  numRows - the number of rows/columns to remove
6032: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
6033: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6034: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6035: -  b - optional vector of right hand side, that will be adjusted by provided solution

6037:    Notes:
6038:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
6039:    but does not release memory.  For the dense and block diagonal
6040:    formats this does not alter the nonzero structure.

6042:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6043:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6044:    merely zeroed.

6046:    The user can set a value in the diagonal entry (or for the AIJ and
6047:    row formats can optionally remove the main diagonal entry from the
6048:    nonzero structure as well, by passing 0.0 as the final argument).

6050:    For the parallel case, all processes that share the matrix (i.e.,
6051:    those in the communicator used for matrix creation) MUST call this
6052:    routine, regardless of whether any rows being zeroed are owned by
6053:    them.

6055:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6056:    list only rows local to itself, but the row/column numbers are given in local numbering).

6058:    The grid coordinates are across the entire grid, not just the local portion

6060:    In Fortran idxm and idxn should be declared as
6061: $     MatStencil idxm(4,m)
6062:    and the values inserted using
6063: $    idxm(MatStencil_i,1) = i
6064: $    idxm(MatStencil_j,1) = j
6065: $    idxm(MatStencil_k,1) = k
6066: $    idxm(MatStencil_c,1) = c
6067:    etc

6069:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6070:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6071:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6072:    DM_BOUNDARY_PERIODIC boundary type.

6074:    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
6075:    a single value per point) you can skip filling those indices.

6077:    Level: intermediate

6079:    Concepts: matrices^zeroing rows

6081: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6082:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRows()
6083: @*/
6084: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
6085: {
6086:   PetscInt       dim     = mat->stencil.dim;
6087:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
6088:   PetscInt       *dims   = mat->stencil.dims+1;
6089:   PetscInt       *starts = mat->stencil.starts;
6090:   PetscInt       *dxm    = (PetscInt*) rows;
6091:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


6099:   PetscMalloc1(numRows, &jdxm);
6100:   for (i = 0; i < numRows; ++i) {
6101:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6102:     for (j = 0; j < 3-sdim; ++j) dxm++;
6103:     /* Local index in X dir */
6104:     tmp = *dxm++ - starts[0];
6105:     /* Loop over remaining dimensions */
6106:     for (j = 0; j < dim-1; ++j) {
6107:       /* If nonlocal, set index to be negative */
6108:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6109:       /* Update local index */
6110:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
6111:     }
6112:     /* Skip component slot if necessary */
6113:     if (mat->stencil.noc) dxm++;
6114:     /* Local row number */
6115:     if (tmp >= 0) {
6116:       jdxm[numNewRows++] = tmp;
6117:     }
6118:   }
6119:   MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);
6120:   PetscFree(jdxm);
6121:   return(0);
6122: }

6124: /*@C
6125:    MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6126:    of a set of rows of a matrix; using local numbering of rows.

6128:    Collective on Mat

6130:    Input Parameters:
6131: +  mat - the matrix
6132: .  numRows - the number of rows to remove
6133: .  rows - the global row indices
6134: .  diag - value put in all diagonals of eliminated rows
6135: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6136: -  b - optional vector of right hand side, that will be adjusted by provided solution

6138:    Notes:
6139:    Before calling MatZeroRowsLocal(), the user must first set the
6140:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6142:    For the AIJ matrix formats this removes the old nonzero structure,
6143:    but does not release memory.  For the dense and block diagonal
6144:    formats this does not alter the nonzero structure.

6146:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6147:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6148:    merely zeroed.

6150:    The user can set a value in the diagonal entry (or for the AIJ and
6151:    row formats can optionally remove the main diagonal entry from the
6152:    nonzero structure as well, by passing 0.0 as the final argument).

6154:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6155:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6157:    Level: intermediate

6159:    Concepts: matrices^zeroing

6161: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRows(), MatSetOption(),
6162:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6163: @*/
6164: PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6165: {

6172:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6173:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6174:   MatCheckPreallocated(mat,1);

6176:   if (mat->ops->zerorowslocal) {
6177:     (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);
6178:   } else {
6179:     IS             is, newis;
6180:     const PetscInt *newRows;

6182:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6183:     ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6184:     ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);
6185:     ISGetIndices(newis,&newRows);
6186:     (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);
6187:     ISRestoreIndices(newis,&newRows);
6188:     ISDestroy(&newis);
6189:     ISDestroy(&is);
6190:   }
6191:   PetscObjectStateIncrease((PetscObject)mat);
6192: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
6193:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
6194:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
6195:   }
6196: #endif
6197:   return(0);
6198: }

6200: /*@
6201:    MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6202:    of a set of rows of a matrix; using local numbering of rows.

6204:    Collective on Mat

6206:    Input Parameters:
6207: +  mat - the matrix
6208: .  is - index set of rows to remove
6209: .  diag - value put in all diagonals of eliminated rows
6210: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6211: -  b - optional vector of right hand side, that will be adjusted by provided solution

6213:    Notes:
6214:    Before calling MatZeroRowsLocalIS(), the user must first set the
6215:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6217:    For the AIJ matrix formats this removes the old nonzero structure,
6218:    but does not release memory.  For the dense and block diagonal
6219:    formats this does not alter the nonzero structure.

6221:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6222:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6223:    merely zeroed.

6225:    The user can set a value in the diagonal entry (or for the AIJ and
6226:    row formats can optionally remove the main diagonal entry from the
6227:    nonzero structure as well, by passing 0.0 as the final argument).

6229:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6230:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6232:    Level: intermediate

6234:    Concepts: matrices^zeroing

6236: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6237:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6238: @*/
6239: PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6240: {
6242:   PetscInt       numRows;
6243:   const PetscInt *rows;

6249:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6250:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6251:   MatCheckPreallocated(mat,1);

6253:   ISGetLocalSize(is,&numRows);
6254:   ISGetIndices(is,&rows);
6255:   MatZeroRowsLocal(mat,numRows,rows,diag,x,b);
6256:   ISRestoreIndices(is,&rows);
6257:   return(0);
6258: }

6260: /*@
6261:    MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6262:    of a set of rows and columns of a matrix; using local numbering of rows.

6264:    Collective on Mat

6266:    Input Parameters:
6267: +  mat - the matrix
6268: .  numRows - the number of rows to remove
6269: .  rows - the global row indices
6270: .  diag - value put in all diagonals of eliminated rows
6271: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6272: -  b - optional vector of right hand side, that will be adjusted by provided solution

6274:    Notes:
6275:    Before calling MatZeroRowsColumnsLocal(), the user must first set the
6276:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6278:    The user can set a value in the diagonal entry (or for the AIJ and
6279:    row formats can optionally remove the main diagonal entry from the
6280:    nonzero structure as well, by passing 0.0 as the final argument).

6282:    Level: intermediate

6284:    Concepts: matrices^zeroing

6286: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6287:           MatZeroRows(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6288: @*/
6289: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6290: {
6292:   IS             is, newis;
6293:   const PetscInt *newRows;

6299:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6300:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6301:   MatCheckPreallocated(mat,1);

6303:   if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6304:   ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6305:   ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);
6306:   ISGetIndices(newis,&newRows);
6307:   (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);
6308:   ISRestoreIndices(newis,&newRows);
6309:   ISDestroy(&newis);
6310:   ISDestroy(&is);
6311:   PetscObjectStateIncrease((PetscObject)mat);
6312: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_VECCUDA)
6313:   if (mat->valid_GPU_matrix != PETSC_OFFLOAD_UNALLOCATED) {
6314:     mat->valid_GPU_matrix = PETSC_OFFLOAD_CPU;
6315:   }
6316: #endif
6317:   return(0);
6318: }

6320: /*@
6321:    MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6322:    of a set of rows and columns of a matrix; using local numbering of rows.

6324:    Collective on Mat

6326:    Input Parameters:
6327: +  mat - the matrix
6328: .  is - index set of rows to remove
6329: .  diag - value put in all diagonals of eliminated rows
6330: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6331: -  b - optional vector of right hand side, that will be adjusted by provided solution

6333:    Notes:
6334:    Before calling MatZeroRowsColumnsLocalIS(), the user must first set the
6335:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6337:    The user can set a value in the diagonal entry (or for the AIJ and
6338:    row formats can optionally remove the main diagonal entry from the
6339:    nonzero structure as well, by passing 0.0 as the final argument).

6341:    Level: intermediate

6343:    Concepts: matrices^zeroing

6345: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6346:           MatZeroRowsColumnsLocal(), MatZeroRows(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6347: @*/
6348: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6349: {
6351:   PetscInt       numRows;
6352:   const PetscInt *rows;

6358:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6359:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6360:   MatCheckPreallocated(mat,1);

6362:   ISGetLocalSize(is,&numRows);
6363:   ISGetIndices(is,&rows);
6364:   MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);
6365:   ISRestoreIndices(is,&rows);
6366:   return(0);
6367: }

6369: /*@C
6370:    MatGetSize - Returns the numbers of rows and columns in a matrix.

6372:    Not Collective

6374:    Input Parameter:
6375: .  mat - the matrix

6377:    Output Parameters:
6378: +  m - the number of global rows
6379: -  n - the number of global columns

6381:    Note: both output parameters can be NULL on input.

6383:    Level: beginner

6385:    Concepts: matrices^size

6387: .seealso: MatGetLocalSize()
6388: @*/
6389: PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n)
6390: {
6393:   if (m) *m = mat->rmap->N;
6394:   if (n) *n = mat->cmap->N;
6395:   return(0);
6396: }

6398: /*@C
6399:    MatGetLocalSize - Returns the number of rows and columns in a matrix
6400:    stored locally.  This information may be implementation dependent, so
6401:    use with care.

6403:    Not Collective

6405:    Input Parameters:
6406: .  mat - the matrix

6408:    Output Parameters:
6409: +  m - the number of local rows
6410: -  n - the number of local columns

6412:    Note: both output parameters can be NULL on input.

6414:    Level: beginner

6416:    Concepts: matrices^local size

6418: .seealso: MatGetSize()
6419: @*/
6420: PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n)
6421: {
6426:   if (m) *m = mat->rmap->n;
6427:   if (n) *n = mat->cmap->n;
6428:   return(0);
6429: }

6431: /*@C
6432:    MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6433:    this processor. (The columns of the "diagonal block")

6435:    Not Collective, unless matrix has not been allocated, then collective on Mat

6437:    Input Parameters:
6438: .  mat - the matrix

6440:    Output Parameters:
6441: +  m - the global index of the first local column
6442: -  n - one more than the global index of the last local column

6444:    Notes:
6445:     both output parameters can be NULL on input.

6447:    Level: developer

6449:    Concepts: matrices^column ownership

6451: .seealso:  MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn()

6453: @*/
6454: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n)
6455: {
6461:   MatCheckPreallocated(mat,1);
6462:   if (m) *m = mat->cmap->rstart;
6463:   if (n) *n = mat->cmap->rend;
6464:   return(0);
6465: }

6467: /*@C
6468:    MatGetOwnershipRange - Returns the range of matrix rows owned by
6469:    this processor, assuming that the matrix is laid out with the first
6470:    n1 rows on the first processor, the next n2 rows on the second, etc.
6471:    For certain parallel layouts this range may not be well defined.

6473:    Not Collective

6475:    Input Parameters:
6476: .  mat - the matrix

6478:    Output Parameters:
6479: +  m - the global index of the first local row
6480: -  n - one more than the global index of the last local row

6482:    Note: Both output parameters can be NULL on input.
6483: $  This function requires that the matrix be preallocated. If you have not preallocated, consider using
6484: $    PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N)
6485: $  and then MPI_Scan() to calculate prefix sums of the local sizes.

6487:    Level: beginner

6489:    Concepts: matrices^row ownership

6491: .seealso:   MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock()

6493: @*/
6494: PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n)
6495: {
6501:   MatCheckPreallocated(mat,1);
6502:   if (m) *m = mat->rmap->rstart;
6503:   if (n) *n = mat->rmap->rend;
6504:   return(0);
6505: }

6507: /*@C
6508:    MatGetOwnershipRanges - Returns the range of matrix rows owned by
6509:    each process

6511:    Not Collective, unless matrix has not been allocated, then collective on Mat

6513:    Input Parameters:
6514: .  mat - the matrix

6516:    Output Parameters:
6517: .  ranges - start of each processors portion plus one more than the total length at the end

6519:    Level: beginner

6521:    Concepts: matrices^row ownership

6523: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn()

6525: @*/
6526: PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges)
6527: {

6533:   MatCheckPreallocated(mat,1);
6534:   PetscLayoutGetRanges(mat->rmap,ranges);
6535:   return(0);
6536: }

6538: /*@C
6539:    MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6540:    this processor. (The columns of the "diagonal blocks" for each process)

6542:    Not Collective, unless matrix has not been allocated, then collective on Mat

6544:    Input Parameters:
6545: .  mat - the matrix

6547:    Output Parameters:
6548: .  ranges - start of each processors portion plus one more then the total length at the end

6550:    Level: beginner

6552:    Concepts: matrices^column ownership

6554: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges()

6556: @*/
6557: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges)
6558: {

6564:   MatCheckPreallocated(mat,1);
6565:   PetscLayoutGetRanges(mat->cmap,ranges);
6566:   return(0);
6567: }

6569: /*@C
6570:    MatGetOwnershipIS - Get row and column ownership as index sets

6572:    Not Collective

6574:    Input Arguments:
6575: .  A - matrix of type Elemental

6577:    Output Arguments:
6578: +  rows - rows in which this process owns elements
6579: .  cols - columns in which this process owns elements

6581:    Level: intermediate

6583: .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL
6584: @*/
6585: PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols)
6586: {
6587:   PetscErrorCode ierr,(*f)(Mat,IS*,IS*);

6590:   MatCheckPreallocated(A,1);
6591:   PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",&f);
6592:   if (f) {
6593:     (*f)(A,rows,cols);
6594:   } else {   /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6595:     if (rows) {ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);}
6596:     if (cols) {ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);}
6597:   }
6598:   return(0);
6599: }

6601: /*@C
6602:    MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix.
6603:    Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric()
6604:    to complete the factorization.

6606:    Collective on Mat

6608:    Input Parameters:
6609: +  mat - the matrix
6610: .  row - row permutation
6611: .  column - column permutation
6612: -  info - structure containing
6613: $      levels - number of levels of fill.
6614: $      expected fill - as ratio of original fill.
6615: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6616:                 missing diagonal entries)

6618:    Output Parameters:
6619: .  fact - new matrix that has been symbolically factored

6621:    Notes:
6622:     See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

6624:    Most users should employ the simplified KSP interface for linear solvers
6625:    instead of working directly with matrix algebra routines such as this.
6626:    See, e.g., KSPCreate().

6628:    Level: developer

6630:   Concepts: matrices^symbolic LU factorization
6631:   Concepts: matrices^factorization
6632:   Concepts: LU^symbolic factorization

6634: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()
6635:           MatGetOrdering(), MatFactorInfo

6637:     Developer Note: fortran interface is not autogenerated as the f90
6638:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6640: @*/
6641: PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
6642: {

6652:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels);
6653:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6654:   if (!(fact)->ops->ilufactorsymbolic) {
6655:     MatSolverType spackage;
6656:     MatFactorGetSolverType(fact,&spackage);
6657:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage);
6658:   }
6659:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6660:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6661:   MatCheckPreallocated(mat,2);

6663:   PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);
6664:   (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);
6665:   PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);
6666:   return(0);
6667: }

6669: /*@C
6670:    MatICCFactorSymbolic - Performs symbolic incomplete
6671:    Cholesky factorization for a symmetric matrix.  Use
6672:    MatCholeskyFactorNumeric() to complete the factorization.

6674:    Collective on Mat

6676:    Input Parameters:
6677: +  mat - the matrix
6678: .  perm - row and column permutation
6679: -  info - structure containing
6680: $      levels - number of levels of fill.
6681: $      expected fill - as ratio of original fill.

6683:    Output Parameter:
6684: .  fact - the factored matrix

6686:    Notes:
6687:    Most users should employ the KSP interface for linear solvers
6688:    instead of working directly with matrix algebra routines such as this.
6689:    See, e.g., KSPCreate().

6691:    Level: developer

6693:   Concepts: matrices^symbolic incomplete Cholesky factorization
6694:   Concepts: matrices^factorization
6695:   Concepts: Cholsky^symbolic factorization

6697: .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

6699:     Developer Note: fortran interface is not autogenerated as the f90
6700:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6702: @*/
6703: PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
6704: {

6713:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6714:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels);
6715:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6716:   if (!(fact)->ops->iccfactorsymbolic) {
6717:     MatSolverType spackage;
6718:     MatFactorGetSolverType(fact,&spackage);
6719:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage);
6720:   }
6721:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6722:   MatCheckPreallocated(mat,2);

6724:   PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);
6725:   (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);
6726:   PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);
6727:   return(0);
6728: }

6730: /*@C
6731:    MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
6732:    points to an array of valid matrices, they may be reused to store the new
6733:    submatrices.

6735:    Collective on Mat

6737:    Input Parameters:
6738: +  mat - the matrix
6739: .  n   - the number of submatrixes to be extracted (on this processor, may be zero)
6740: .  irow, icol - index sets of rows and columns to extract
6741: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6743:    Output Parameter:
6744: .  submat - the array of submatrices

6746:    Notes:
6747:    MatCreateSubMatrices() can extract ONLY sequential submatrices
6748:    (from both sequential and parallel matrices). Use MatCreateSubMatrix()
6749:    to extract a parallel submatrix.

6751:    Some matrix types place restrictions on the row and column
6752:    indices, such as that they be sorted or that they be equal to each other.

6754:    The index sets may not have duplicate entries.

6756:    When extracting submatrices from a parallel matrix, each processor can
6757:    form a different submatrix by setting the rows and columns of its
6758:    individual index sets according to the local submatrix desired.

6760:    When finished using the submatrices, the user should destroy
6761:    them with MatDestroySubMatrices().

6763:    MAT_REUSE_MATRIX can only be used when the nonzero structure of the
6764:    original matrix has not changed from that last call to MatCreateSubMatrices().

6766:    This routine creates the matrices in submat; you should NOT create them before
6767:    calling it. It also allocates the array of matrix pointers submat.

6769:    For BAIJ matrices the index sets must respect the block structure, that is if they
6770:    request one row/column in a block, they must request all rows/columns that are in
6771:    that block. For example, if the block size is 2 you cannot request just row 0 and
6772:    column 0.

6774:    Fortran Note:
6775:    The Fortran interface is slightly different from that given below; it
6776:    requires one to pass in  as submat a Mat (integer) array of size at least n+1.

6778:    Level: advanced

6780:    Concepts: matrices^accessing submatrices
6781:    Concepts: submatrices

6783: .seealso: MatDestroySubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6784: @*/
6785: PetscErrorCode MatCreateSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6786: {
6788:   PetscInt       i;
6789:   PetscBool      eq;

6794:   if (n) {
6799:   }
6801:   if (n && scall == MAT_REUSE_MATRIX) {
6804:   }
6805:   if (!mat->ops->createsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6806:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6807:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6808:   MatCheckPreallocated(mat,1);

6810:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6811:   (*mat->ops->createsubmatrices)(mat,n,irow,icol,scall,submat);
6812:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6813:   for (i=0; i<n; i++) {
6814:     (*submat)[i]->factortype = MAT_FACTOR_NONE;  /* in case in place factorization was previously done on submatrix */
6815:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6816:       ISEqual(irow[i],icol[i],&eq);
6817:       if (eq) {
6818:         if (mat->symmetric) {
6819:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6820:         } else if (mat->hermitian) {
6821:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6822:         } else if (mat->structurally_symmetric) {
6823:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6824:         }
6825:       }
6826:     }
6827:   }
6828:   return(0);
6829: }

6831: /*@C
6832:    MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of IS that may live on subcomms).

6834:    Collective on Mat

6836:    Input Parameters:
6837: +  mat - the matrix
6838: .  n   - the number of submatrixes to be extracted
6839: .  irow, icol - index sets of rows and columns to extract
6840: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6842:    Output Parameter:
6843: .  submat - the array of submatrices

6845:    Level: advanced

6847:    Concepts: matrices^accessing submatrices
6848:    Concepts: submatrices

6850: .seealso: MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6851: @*/
6852: PetscErrorCode MatCreateSubMatricesMPI(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6853: {
6855:   PetscInt       i;
6856:   PetscBool      eq;

6861:   if (n) {
6866:   }
6868:   if (n && scall == MAT_REUSE_MATRIX) {
6871:   }
6872:   if (!mat->ops->createsubmatricesmpi) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6873:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6874:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6875:   MatCheckPreallocated(mat,1);

6877:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6878:   (*mat->ops->createsubmatricesmpi)(mat,n,irow,icol,scall,submat);
6879:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6880:   for (i=0; i<n; i++) {
6881:     if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) {
6882:       ISEqual(irow[i],icol[i],&eq);
6883:       if (eq) {
6884:         if (mat->symmetric) {
6885:           MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);
6886:         } else if (mat->hermitian) {
6887:           MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);
6888:         } else if (mat->structurally_symmetric) {
6889:           MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
6890:         }
6891:       }
6892:     }
6893:   }
6894:   return(0);
6895: }

6897: /*@C
6898:    MatDestroyMatrices - Destroys an array of matrices.

6900:    Collective on Mat

6902:    Input Parameters:
6903: +  n - the number of local matrices
6904: -  mat - the matrices (note that this is a pointer to the array of matrices)

6906:    Level: advanced

6908:     Notes:
6909:     Frees not only the matrices, but also the array that contains the matrices
6910:            In Fortran will not free the array.

6912: .seealso: MatCreateSubMatrices() MatDestroySubMatrices()
6913: @*/
6914: PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[])
6915: {
6917:   PetscInt       i;

6920:   if (!*mat) return(0);
6921:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6924:   for (i=0; i<n; i++) {
6925:     MatDestroy(&(*mat)[i]);
6926:   }

6928:   /* memory is allocated even if n = 0 */
6929:   PetscFree(*mat);
6930:   return(0);
6931: }

6933: /*@C
6934:    MatDestroySubMatrices - Destroys a set of matrices obtained with MatCreateSubMatrices().

6936:    Collective on Mat

6938:    Input Parameters:
6939: +  n - the number of local matrices
6940: -  mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling
6941:                        sequence of MatCreateSubMatrices())

6943:    Level: advanced

6945:     Notes:
6946:     Frees not only the matrices, but also the array that contains the matrices
6947:            In Fortran will not free the array.

6949: .seealso: MatCreateSubMatrices()
6950: @*/
6951: PetscErrorCode MatDestroySubMatrices(PetscInt n,Mat *mat[])
6952: {
6954:   Mat            mat0;

6957:   if (!*mat) return(0);
6958:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
6959:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6962:   mat0 = (*mat)[0];
6963:   if (mat0 && mat0->ops->destroysubmatrices) {
6964:     (mat0->ops->destroysubmatrices)(n,mat);
6965:   } else {
6966:     MatDestroyMatrices(n,mat);
6967:   }
6968:   return(0);
6969: }

6971: /*@C
6972:    MatGetSeqNonzeroStructure - Extracts the sequential nonzero structure from a matrix.

6974:    Collective on Mat

6976:    Input Parameters:
6977: .  mat - the matrix

6979:    Output Parameter:
6980: .  matstruct - the sequential matrix with the nonzero structure of mat

6982:   Level: intermediate

6984: .seealso: MatDestroySeqNonzeroStructure(), MatCreateSubMatrices(), MatDestroyMatrices()
6985: @*/
6986: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat,Mat *matstruct)
6987: {


6995:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6996:   MatCheckPreallocated(mat,1);

6998:   if (!mat->ops->getseqnonzerostructure) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name);
6999:   PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);
7000:   (*mat->ops->getseqnonzerostructure)(mat,matstruct);
7001:   PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);
7002:   return(0);
7003: }

7005: /*@C
7006:    MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure().

7008:    Collective on Mat

7010:    Input Parameters:
7011: .  mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling
7012:                        sequence of MatGetSequentialNonzeroStructure())

7014:    Level: advanced

7016:     Notes:
7017:     Frees not only the matrices, but also the array that contains the matrices

7019: .seealso: MatGetSeqNonzeroStructure()
7020: @*/
7021: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7022: {

7027:   MatDestroy(mat);
7028:   return(0);
7029: }

7031: /*@
7032:    MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7033:    replaces the index sets by larger ones that represent submatrices with
7034:    additional overlap.

7036:    Collective on Mat

7038:    Input Parameters:
7039: +  mat - the matrix
7040: .  n   - the number of index sets
7041: .  is  - the array of index sets (these index sets will changed during the call)
7042: -  ov  - the additional overlap requested

7044:    Options Database:
7045: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7047:    Level: developer

7049:    Concepts: overlap
7050:    Concepts: ASM^computing overlap

7052: .seealso: MatCreateSubMatrices()
7053: @*/
7054: PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov)
7055: {

7061:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
7062:   if (n) {
7065:   }
7066:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
7067:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7068:   MatCheckPreallocated(mat,1);

7070:   if (!ov) return(0);
7071:   if (!mat->ops->increaseoverlap) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
7072:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
7073:   (*mat->ops->increaseoverlap)(mat,n,is,ov);
7074:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
7075:   return(0);
7076: }


7079: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat,IS*,PetscInt);

7081: /*@
7082:    MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7083:    a sub communicator, replaces the index sets by larger ones that represent submatrices with
7084:    additional overlap.

7086:    Collective on Mat

7088:    Input Parameters:
7089: +  mat - the matrix
7090: .  n   - the number of index sets
7091: .  is  - the array of index sets (these index sets will changed during the call)
7092: -  ov  - the additional overlap requested

7094:    Options Database:
7095: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7097:    Level: developer

7099:    Concepts: overlap
7100:    Concepts: ASM^computing overlap

7102: .seealso: MatCreateSubMatrices()
7103: @*/
7104: PetscErrorCode MatIncreaseOverlapSplit(Mat mat,PetscInt n,IS is[],PetscInt ov)
7105: {
7106:   PetscInt       i;

7112:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
7113:   if (n) {
7116:   }
7117:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
7118:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7119:   MatCheckPreallocated(mat,1);
7120:   if (!ov) return(0);
7121:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
7122:   for(i=0; i<n; i++){
7123:          MatIncreaseOverlapSplit_Single(mat,&is[i],ov);
7124:   }
7125:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
7126:   return(0);
7127: }




7132: /*@
7133:    MatGetBlockSize - Returns the matrix block size.

7135:    Not Collective

7137:    Input Parameter:
7138: .  mat - the matrix

7140:    Output Parameter:
7141: .  bs - block size

7143:    Notes:
7144:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.

7146:    If the block size has not been set yet this routine returns 1.

7148:    Level: intermediate

7150:    Concepts: matrices^block size

7152: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes()
7153: @*/
7154: PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs)
7155: {
7159:   *bs = PetscAbs(mat->rmap->bs);
7160:   return(0);
7161: }

7163: /*@
7164:    MatGetBlockSizes - Returns the matrix block row and column sizes.

7166:    Not Collective

7168:    Input Parameter:
7169: .  mat - the matrix

7171:    Output Parameter:
7172: .  rbs - row block size
7173: .  cbs - column block size

7175:    Notes:
7176:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7177:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7179:    If a block size has not been set yet this routine returns 1.

7181:    Level: intermediate

7183:    Concepts: matrices^block size

7185: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatSetBlockSizes()
7186: @*/
7187: PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs)
7188: {
7193:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7194:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7195:   return(0);
7196: }

7198: /*@
7199:    MatSetBlockSize - Sets the matrix block size.

7201:    Logically Collective on Mat

7203:    Input Parameters:
7204: +  mat - the matrix
7205: -  bs - block size

7207:    Notes:
7208:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7209:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7211:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block size
7212:     is compatible with the matrix local sizes.

7214:    Level: intermediate

7216:    Concepts: matrices^block size

7218: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes()
7219: @*/
7220: PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs)
7221: {

7227:   MatSetBlockSizes(mat,bs,bs);
7228:   return(0);
7229: }

7231: /*@
7232:    MatSetVariableBlockSizes - Sets a diagonal blocks of the matrix that need not be of the same size

7234:    Logically Collective on Mat

7236:    Input Parameters:
7237: +  mat - the matrix
7238: .  nblocks - the number of blocks on this process
7239: -  bsizes - the block sizes

7241:    Notes:
7242:     Currently used by PCVPBJACOBI for SeqAIJ matrices

7244:    Level: intermediate

7246:    Concepts: matrices^block size

7248: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes(), MatGetVariableBlockSizes()
7249: @*/
7250: PetscErrorCode MatSetVariableBlockSizes(Mat mat,PetscInt nblocks,PetscInt *bsizes)
7251: {
7253:   PetscInt       i,ncnt = 0, nlocal;

7257:   if (nblocks < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number of local blocks must be great than or equal to zero");
7258:   MatGetLocalSize(mat,&nlocal,NULL);
7259:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
7260:   if (ncnt != nlocal) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Sum of local block sizes %D does not equal local size of matrix %D",ncnt,nlocal);
7261:   PetscFree(mat->bsizes);
7262:   mat->nblocks = nblocks;
7263:   PetscMalloc1(nblocks,&mat->bsizes);
7264:   PetscMemcpy(mat->bsizes,bsizes,nblocks*sizeof(PetscInt));
7265:   return(0);
7266: }

7268: /*@C
7269:    MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7271:    Logically Collective on Mat

7273:    Input Parameters:
7274: .  mat - the matrix

7276:    Output Parameters:
7277: +  nblocks - the number of blocks on this process
7278: -  bsizes - the block sizes

7280:    Notes: Currently not supported from Fortran

7282:    Level: intermediate

7284:    Concepts: matrices^block size

7286: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes(), MatSetVariableBlockSizes()
7287: @*/
7288: PetscErrorCode MatGetVariableBlockSizes(Mat mat,PetscInt *nblocks,const PetscInt **bsizes)
7289: {
7292:   *nblocks = mat->nblocks;
7293:   *bsizes  = mat->bsizes;
7294:   return(0);
7295: }

7297: /*@
7298:    MatSetBlockSizes - Sets the matrix block row and column sizes.

7300:    Logically Collective on Mat

7302:    Input Parameters:
7303: +  mat - the matrix
7304: -  rbs - row block size
7305: -  cbs - column block size

7307:    Notes:
7308:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7309:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7310:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later

7312:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block sizes
7313:     are compatible with the matrix local sizes.

7315:     The row and column block size determine the blocksize of the "row" and "column" vectors returned by MatCreateVecs().

7317:    Level: intermediate

7319:    Concepts: matrices^block size

7321: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatGetBlockSizes()
7322: @*/
7323: PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs)
7324: {

7331:   if (mat->ops->setblocksizes) {
7332:     (*mat->ops->setblocksizes)(mat,rbs,cbs);
7333:   }
7334:   if (mat->rmap->refcnt) {
7335:     ISLocalToGlobalMapping l2g = NULL;
7336:     PetscLayout            nmap = NULL;

7338:     PetscLayoutDuplicate(mat->rmap,&nmap);
7339:     if (mat->rmap->mapping) {
7340:       ISLocalToGlobalMappingDuplicate(mat->rmap->mapping,&l2g);
7341:     }
7342:     PetscLayoutDestroy(&mat->rmap);
7343:     mat->rmap = nmap;
7344:     mat->rmap->mapping = l2g;
7345:   }
7346:   if (mat->cmap->refcnt) {
7347:     ISLocalToGlobalMapping l2g = NULL;
7348:     PetscLayout            nmap = NULL;

7350:     PetscLayoutDuplicate(mat->cmap,&nmap);
7351:     if (mat->cmap->mapping) {
7352:       ISLocalToGlobalMappingDuplicate(mat->cmap->mapping,&l2g);
7353:     }
7354:     PetscLayoutDestroy(&mat->cmap);
7355:     mat->cmap = nmap;
7356:     mat->cmap->mapping = l2g;
7357:   }
7358:   PetscLayoutSetBlockSize(mat->rmap,rbs);
7359:   PetscLayoutSetBlockSize(mat->cmap,cbs);
7360:   return(0);
7361: }

7363: /*@
7364:    MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7366:    Logically Collective on Mat

7368:    Input Parameters:
7369: +  mat - the matrix
7370: .  fromRow - matrix from which to copy row block size
7371: -  fromCol - matrix from which to copy column block size (can be same as fromRow)

7373:    Level: developer

7375:    Concepts: matrices^block size

7377: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes()
7378: @*/
7379: PetscErrorCode MatSetBlockSizesFromMats(Mat mat,Mat fromRow,Mat fromCol)
7380: {

7387:   if (fromRow->rmap->bs > 0) {PetscLayoutSetBlockSize(mat->rmap,fromRow->rmap->bs);}
7388:   if (fromCol->cmap->bs > 0) {PetscLayoutSetBlockSize(mat->cmap,fromCol->cmap->bs);}
7389:   return(0);
7390: }

7392: /*@
7393:    MatResidual - Default routine to calculate the residual.

7395:    Collective on Mat and Vec

7397:    Input Parameters:
7398: +  mat - the matrix
7399: .  b   - the right-hand-side
7400: -  x   - the approximate solution

7402:    Output Parameter:
7403: .  r - location to store the residual

7405:    Level: developer

7407: .keywords: MG, default, multigrid, residual

7409: .seealso: PCMGSetResidual()
7410: @*/
7411: PetscErrorCode MatResidual(Mat mat,Vec b,Vec x,Vec r)
7412: {

7421:   MatCheckPreallocated(mat,1);
7422:   PetscLogEventBegin(MAT_Residual,mat,0,0,0);
7423:   if (!mat->ops->residual) {
7424:     MatMult(mat,x,r);
7425:     VecAYPX(r,-1.0,b);
7426:   } else {
7427:     (*mat->ops->residual)(mat,b,x,r);
7428:   }
7429:   PetscLogEventEnd(MAT_Residual,mat,0,0,0);
7430:   return(0);
7431: }

7433: /*@C
7434:     MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices.

7436:    Collective on Mat

7438:     Input Parameters:
7439: +   mat - the matrix
7440: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7441: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be   symmetrized
7442: -   inodecompressed - PETSC_TRUE or PETSC_FALSE  indicating if the nonzero structure of the
7443:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7444:                  always used.

7446:     Output Parameters:
7447: +   n - number of rows in the (possibly compressed) matrix
7448: .   ia - the row pointers [of length n+1]
7449: .   ja - the column indices
7450: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7451:            are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set

7453:     Level: developer

7455:     Notes:
7456:     You CANNOT change any of the ia[] or ja[] values.

7458:     Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values.

7460:     Fortran Notes:
7461:     In Fortran use
7462: $
7463: $      PetscInt ia(1), ja(1)
7464: $      PetscOffset iia, jja
7465: $      call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr)
7466: $      ! Access the ith and jth entries via ia(iia + i) and ja(jja + j)

7468:      or
7469: $
7470: $    PetscInt, pointer :: ia(:),ja(:)
7471: $    call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7472: $    ! Access the ith and jth entries via ia(i) and ja(j)

7474: .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray()
7475: @*/
7476: PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7477: {

7487:   MatCheckPreallocated(mat,1);
7488:   if (!mat->ops->getrowij) *done = PETSC_FALSE;
7489:   else {
7490:     *done = PETSC_TRUE;
7491:     PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);
7492:     (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7493:     PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);
7494:   }
7495:   return(0);
7496: }

7498: /*@C
7499:     MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7501:     Collective on Mat

7503:     Input Parameters:
7504: +   mat - the matrix
7505: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7506: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7507:                 symmetrized
7508: .   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7509:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7510:                  always used.
7511: .   n - number of columns in the (possibly compressed) matrix
7512: .   ia - the column pointers
7513: -   ja - the row indices

7515:     Output Parameters:
7516: .   done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned

7518:     Note:
7519:     This routine zeros out n, ia, and ja. This is to prevent accidental
7520:     us of the array after it has been restored. If you pass NULL, it will
7521:     not zero the pointers.  Use of ia or ja after MatRestoreColumnIJ() is invalid.

7523:     Level: developer

7525: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7526: @*/
7527: PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7528: {

7538:   MatCheckPreallocated(mat,1);
7539:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
7540:   else {
7541:     *done = PETSC_TRUE;
7542:     (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7543:   }
7544:   return(0);
7545: }

7547: /*@C
7548:     MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with
7549:     MatGetRowIJ().

7551:     Collective on Mat

7553:     Input Parameters:
7554: +   mat - the matrix
7555: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7556: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7557:                 symmetrized
7558: .   inodecompressed -  PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7559:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7560:                  always used.
7561: .   n - size of (possibly compressed) matrix
7562: .   ia - the row pointers
7563: -   ja - the column indices

7565:     Output Parameters:
7566: .   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7568:     Note:
7569:     This routine zeros out n, ia, and ja. This is to prevent accidental
7570:     us of the array after it has been restored. If you pass NULL, it will
7571:     not zero the pointers.  Use of ia or ja after MatRestoreRowIJ() is invalid.

7573:     Level: developer

7575: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7576: @*/
7577: PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7578: {

7587:   MatCheckPreallocated(mat,1);

7589:   if (!mat->ops->restorerowij) *done = PETSC_FALSE;
7590:   else {
7591:     *done = PETSC_TRUE;
7592:     (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7593:     if (n)  *n = 0;
7594:     if (ia) *ia = NULL;
7595:     if (ja) *ja = NULL;
7596:   }
7597:   return(0);
7598: }

7600: /*@C
7601:     MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with
7602:     MatGetColumnIJ().

7604:     Collective on Mat

7606:     Input Parameters:
7607: +   mat - the matrix
7608: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7609: -   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7610:                 symmetrized
7611: -   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7612:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7613:                  always used.

7615:     Output Parameters:
7616: +   n - size of (possibly compressed) matrix
7617: .   ia - the column pointers
7618: .   ja - the row indices
7619: -   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7621:     Level: developer

7623: .seealso: MatGetColumnIJ(), MatRestoreRowIJ()
7624: @*/
7625: PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7626: {

7635:   MatCheckPreallocated(mat,1);

7637:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
7638:   else {
7639:     *done = PETSC_TRUE;
7640:     (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7641:     if (n)  *n = 0;
7642:     if (ia) *ia = NULL;
7643:     if (ja) *ja = NULL;
7644:   }
7645:   return(0);
7646: }

7648: /*@C
7649:     MatColoringPatch -Used inside matrix coloring routines that
7650:     use MatGetRowIJ() and/or MatGetColumnIJ().

7652:     Collective on Mat

7654:     Input Parameters:
7655: +   mat - the matrix
7656: .   ncolors - max color value
7657: .   n   - number of entries in colorarray
7658: -   colorarray - array indicating color for each column

7660:     Output Parameters:
7661: .   iscoloring - coloring generated using colorarray information

7663:     Level: developer

7665: .seealso: MatGetRowIJ(), MatGetColumnIJ()

7667: @*/
7668: PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring)
7669: {

7677:   MatCheckPreallocated(mat,1);

7679:   if (!mat->ops->coloringpatch) {
7680:     ISColoringCreate(PetscObjectComm((PetscObject)mat),ncolors,n,colorarray,PETSC_OWN_POINTER,iscoloring);
7681:   } else {
7682:     (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);
7683:   }
7684:   return(0);
7685: }


7688: /*@
7689:    MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

7691:    Logically Collective on Mat

7693:    Input Parameter:
7694: .  mat - the factored matrix to be reset

7696:    Notes:
7697:    This routine should be used only with factored matrices formed by in-place
7698:    factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE
7699:    format).  This option can save memory, for example, when solving nonlinear
7700:    systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
7701:    ILU(0) preconditioner.

7703:    Note that one can specify in-place ILU(0) factorization by calling
7704: .vb
7705:      PCType(pc,PCILU);
7706:      PCFactorSeUseInPlace(pc);
7707: .ve
7708:    or by using the options -pc_type ilu -pc_factor_in_place

7710:    In-place factorization ILU(0) can also be used as a local
7711:    solver for the blocks within the block Jacobi or additive Schwarz
7712:    methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
7713:    for details on setting local solver options.

7715:    Most users should employ the simplified KSP interface for linear solvers
7716:    instead of working directly with matrix algebra routines such as this.
7717:    See, e.g., KSPCreate().

7719:    Level: developer

7721: .seealso: PCFactorSetUseInPlace(), PCFactorGetUseInPlace()

7723:    Concepts: matrices^unfactored

7725: @*/
7726: PetscErrorCode MatSetUnfactored(Mat mat)
7727: {

7733:   MatCheckPreallocated(mat,1);
7734:   mat->factortype = MAT_FACTOR_NONE;
7735:   if (!mat->ops->setunfactored) return(0);
7736:   (*mat->ops->setunfactored)(mat);
7737:   return(0);
7738: }

7740: /*MC
7741:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran90.

7743:     Synopsis:
7744:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7746:     Not collective

7748:     Input Parameter:
7749: .   x - matrix

7751:     Output Parameters:
7752: +   xx_v - the Fortran90 pointer to the array
7753: -   ierr - error code

7755:     Example of Usage:
7756: .vb
7757:       PetscScalar, pointer xx_v(:,:)
7758:       ....
7759:       call MatDenseGetArrayF90(x,xx_v,ierr)
7760:       a = xx_v(3)
7761:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
7762: .ve

7764:     Level: advanced

7766: .seealso:  MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJGetArrayF90()

7768:     Concepts: matrices^accessing array

7770: M*/

7772: /*MC
7773:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
7774:     accessed with MatDenseGetArrayF90().

7776:     Synopsis:
7777:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7779:     Not collective

7781:     Input Parameters:
7782: +   x - matrix
7783: -   xx_v - the Fortran90 pointer to the array

7785:     Output Parameter:
7786: .   ierr - error code

7788:     Example of Usage:
7789: .vb
7790:        PetscScalar, pointer xx_v(:,:)
7791:        ....
7792:        call MatDenseGetArrayF90(x,xx_v,ierr)
7793:        a = xx_v(3)
7794:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
7795: .ve

7797:     Level: advanced

7799: .seealso:  MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJRestoreArrayF90()

7801: M*/


7804: /*MC
7805:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran90.

7807:     Synopsis:
7808:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7810:     Not collective

7812:     Input Parameter:
7813: .   x - matrix

7815:     Output Parameters:
7816: +   xx_v - the Fortran90 pointer to the array
7817: -   ierr - error code

7819:     Example of Usage:
7820: .vb
7821:       PetscScalar, pointer xx_v(:)
7822:       ....
7823:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7824:       a = xx_v(3)
7825:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7826: .ve

7828:     Level: advanced

7830: .seealso:  MatSeqAIJRestoreArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseGetArrayF90()

7832:     Concepts: matrices^accessing array

7834: M*/

7836: /*MC
7837:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
7838:     accessed with MatSeqAIJGetArrayF90().

7840:     Synopsis:
7841:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7843:     Not collective

7845:     Input Parameters:
7846: +   x - matrix
7847: -   xx_v - the Fortran90 pointer to the array

7849:     Output Parameter:
7850: .   ierr - error code

7852:     Example of Usage:
7853: .vb
7854:        PetscScalar, pointer xx_v(:)
7855:        ....
7856:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7857:        a = xx_v(3)
7858:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7859: .ve

7861:     Level: advanced

7863: .seealso:  MatSeqAIJGetArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseRestoreArrayF90()

7865: M*/


7868: /*@
7869:     MatCreateSubMatrix - Gets a single submatrix on the same number of processors
7870:                       as the original matrix.

7872:     Collective on Mat

7874:     Input Parameters:
7875: +   mat - the original matrix
7876: .   isrow - parallel IS containing the rows this processor should obtain
7877: .   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.
7878: -   cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

7880:     Output Parameter:
7881: .   newmat - the new submatrix, of the same type as the old

7883:     Level: advanced

7885:     Notes:
7886:     The submatrix will be able to be multiplied with vectors using the same layout as iscol.

7888:     Some matrix types place restrictions on the row and column indices, such
7889:     as that they be sorted or that they be equal to each other.

7891:     The index sets may not have duplicate entries.

7893:       The first time this is called you should use a cll of MAT_INITIAL_MATRIX,
7894:    the MatCreateSubMatrix() routine will create the newmat for you. Any additional calls
7895:    to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX
7896:    will reuse the matrix generated the first time.  You should call MatDestroy() on newmat when
7897:    you are finished using it.

7899:     The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
7900:     the input matrix.

7902:     If iscol is NULL then all columns are obtained (not supported in Fortran).

7904:    Example usage:
7905:    Consider the following 8x8 matrix with 34 non-zero values, that is
7906:    assembled across 3 processors. Let's assume that proc0 owns 3 rows,
7907:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
7908:    as follows:

7910: .vb
7911:             1  2  0  |  0  3  0  |  0  4
7912:     Proc0   0  5  6  |  7  0  0  |  8  0
7913:             9  0 10  | 11  0  0  | 12  0
7914:     -------------------------------------
7915:            13  0 14  | 15 16 17  |  0  0
7916:     Proc1   0 18  0  | 19 20 21  |  0  0
7917:             0  0  0  | 22 23  0  | 24  0
7918:     -------------------------------------
7919:     Proc2  25 26 27  |  0  0 28  | 29  0
7920:            30  0  0  | 31 32 33  |  0 34
7921: .ve

7923:     Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6].  The resulting submatrix is

7925: .vb
7926:             2  0  |  0  3  0  |  0
7927:     Proc0   5  6  |  7  0  0  |  8
7928:     -------------------------------
7929:     Proc1  18  0  | 19 20 21  |  0
7930:     -------------------------------
7931:     Proc2  26 27  |  0  0 28  | 29
7932:             0  0  | 31 32 33  |  0
7933: .ve


7936:     Concepts: matrices^submatrices

7938: .seealso: MatCreateSubMatrices()
7939: @*/
7940: PetscErrorCode MatCreateSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat)
7941: {
7943:   PetscMPIInt    size;
7944:   Mat            *local;
7945:   IS             iscoltmp;

7954:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7955:   if (cll == MAT_IGNORE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot use MAT_IGNORE_MATRIX");

7957:   MatCheckPreallocated(mat,1);
7958:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

7960:   if (!iscol || isrow == iscol) {
7961:     PetscBool   stride;
7962:     PetscMPIInt grabentirematrix = 0,grab;
7963:     PetscObjectTypeCompare((PetscObject)isrow,ISSTRIDE,&stride);
7964:     if (stride) {
7965:       PetscInt first,step,n,rstart,rend;
7966:       ISStrideGetInfo(isrow,&first,&step);
7967:       if (step == 1) {
7968:         MatGetOwnershipRange(mat,&rstart,&rend);
7969:         if (rstart == first) {
7970:           ISGetLocalSize(isrow,&n);
7971:           if (n == rend-rstart) {
7972:             grabentirematrix = 1;
7973:           }
7974:         }
7975:       }
7976:     }
7977:     MPIU_Allreduce(&grabentirematrix,&grab,1,MPI_INT,MPI_MIN,PetscObjectComm((PetscObject)mat));
7978:     if (grab) {
7979:       PetscInfo(mat,"Getting entire matrix as submatrix\n");
7980:       if (cll == MAT_INITIAL_MATRIX) {
7981:         *newmat = mat;
7982:         PetscObjectReference((PetscObject)mat);
7983:       }
7984:       return(0);
7985:     }
7986:   }

7988:   if (!iscol) {
7989:     ISCreateStride(PetscObjectComm((PetscObject)mat),mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);
7990:   } else {
7991:     iscoltmp = iscol;
7992:   }

7994:   /* if original matrix is on just one processor then use submatrix generated */
7995:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
7996:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);
7997:     if (!iscol) {ISDestroy(&iscoltmp);}
7998:     return(0);
7999:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8000:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);
8001:     *newmat = *local;
8002:     PetscFree(local);
8003:     if (!iscol) {ISDestroy(&iscoltmp);}
8004:     return(0);
8005:   } else if (!mat->ops->createsubmatrix) {
8006:     /* Create a new matrix type that implements the operation using the full matrix */
8007:     PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
8008:     switch (cll) {
8009:     case MAT_INITIAL_MATRIX:
8010:       MatCreateSubMatrixVirtual(mat,isrow,iscoltmp,newmat);
8011:       break;
8012:     case MAT_REUSE_MATRIX:
8013:       MatSubMatrixVirtualUpdate(*newmat,mat,isrow,iscoltmp);
8014:       break;
8015:     default: SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8016:     }
8017:     PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
8018:     if (!iscol) {ISDestroy(&iscoltmp);}
8019:     return(0);
8020:   }

8022:   if (!mat->ops->createsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8023:   PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
8024:   (*mat->ops->createsubmatrix)(mat,isrow,iscoltmp,cll,newmat);
8025:   PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);

8027:   /* Propagate symmetry information for diagonal blocks */
8028:   if (isrow == iscoltmp) {
8029:     if (mat->symmetric_set && mat->symmetric) {
8030:       MatSetOption(*newmat,MAT_SYMMETRIC,PETSC_TRUE);
8031:     }
8032:     if (mat->structurally_symmetric_set && mat->structurally_symmetric) {
8033:       MatSetOption(*newmat,MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
8034:     }
8035:     if (mat->hermitian_set && mat->hermitian) {
8036:       MatSetOption(*newmat,MAT_HERMITIAN,PETSC_TRUE);
8037:     }
8038:     if (mat->spd_set && mat->spd) {
8039:       MatSetOption(*newmat,MAT_SPD,PETSC_TRUE);
8040:     }
8041:   }

8043:   if (!iscol) {ISDestroy(&iscoltmp);}
8044:   if (*newmat && cll == MAT_INITIAL_MATRIX) {PetscObjectStateIncrease((PetscObject)*newmat);}
8045:   return(0);
8046: }

8048: /*@
8049:    MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8050:    used during the assembly process to store values that belong to
8051:    other processors.

8053:    Not Collective

8055:    Input Parameters:
8056: +  mat   - the matrix
8057: .  size  - the initial size of the stash.
8058: -  bsize - the initial size of the block-stash(if used).

8060:    Options Database Keys:
8061: +   -matstash_initial_size <size> or <size0,size1,...sizep-1>
8062: -   -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1>

8064:    Level: intermediate

8066:    Notes:
8067:      The block-stash is used for values set with MatSetValuesBlocked() while
8068:      the stash is used for values set with MatSetValues()

8070:      Run with the option -info and look for output of the form
8071:      MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8072:      to determine the appropriate value, MM, to use for size and
8073:      MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8074:      to determine the value, BMM to use for bsize

8076:    Concepts: stash^setting matrix size
8077:    Concepts: matrices^stash

8079: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo()

8081: @*/
8082: PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize)
8083: {

8089:   MatStashSetInitialSize_Private(&mat->stash,size);
8090:   MatStashSetInitialSize_Private(&mat->bstash,bsize);
8091:   return(0);
8092: }

8094: /*@
8095:    MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
8096:      the matrix

8098:    Neighbor-wise Collective on Mat

8100:    Input Parameters:
8101: +  mat   - the matrix
8102: .  x,y - the vectors
8103: -  w - where the result is stored

8105:    Level: intermediate

8107:    Notes:
8108:     w may be the same vector as y.

8110:     This allows one to use either the restriction or interpolation (its transpose)
8111:     matrix to do the interpolation

8113:     Concepts: interpolation

8115: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

8117: @*/
8118: PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w)
8119: {
8121:   PetscInt       M,N,Ny;

8129:   MatCheckPreallocated(A,1);
8130:   MatGetSize(A,&M,&N);
8131:   VecGetSize(y,&Ny);
8132:   if (M == Ny) {
8133:     MatMultAdd(A,x,y,w);
8134:   } else {
8135:     MatMultTransposeAdd(A,x,y,w);
8136:   }
8137:   return(0);
8138: }

8140: /*@
8141:    MatInterpolate - y = A*x or A'*x depending on the shape of
8142:      the matrix

8144:    Neighbor-wise Collective on Mat

8146:    Input Parameters:
8147: +  mat   - the matrix
8148: -  x,y - the vectors

8150:    Level: intermediate

8152:    Notes:
8153:     This allows one to use either the restriction or interpolation (its transpose)
8154:     matrix to do the interpolation

8156:    Concepts: matrices^interpolation

8158: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

8160: @*/
8161: PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y)
8162: {
8164:   PetscInt       M,N,Ny;

8171:   MatCheckPreallocated(A,1);
8172:   MatGetSize(A,&M,&N);
8173:   VecGetSize(y,&Ny);
8174:   if (M == Ny) {
8175:     MatMult(A,x,y);
8176:   } else {
8177:     MatMultTranspose(A,x,y);
8178:   }
8179:   return(0);
8180: }

8182: /*@
8183:    MatRestrict - y = A*x or A'*x

8185:    Neighbor-wise Collective on Mat

8187:    Input Parameters:
8188: +  mat   - the matrix
8189: -  x,y - the vectors

8191:    Level: intermediate

8193:    Notes:
8194:     This allows one to use either the restriction or interpolation (its transpose)
8195:     matrix to do the restriction

8197:    Concepts: matrices^restriction

8199: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate()

8201: @*/
8202: PetscErrorCode MatRestrict(Mat A,Vec x,Vec y)
8203: {
8205:   PetscInt       M,N,Ny;

8212:   MatCheckPreallocated(A,1);

8214:   MatGetSize(A,&M,&N);
8215:   VecGetSize(y,&Ny);
8216:   if (M == Ny) {
8217:     MatMult(A,x,y);
8218:   } else {
8219:     MatMultTranspose(A,x,y);
8220:   }
8221:   return(0);
8222: }

8224: /*@
8225:    MatGetNullSpace - retrieves the null space of a matrix.

8227:    Logically Collective on Mat and MatNullSpace

8229:    Input Parameters:
8230: +  mat - the matrix
8231: -  nullsp - the null space object

8233:    Level: developer

8235:    Concepts: null space^attaching to matrix

8237: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetNullSpace()
8238: @*/
8239: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8240: {
8244:   *nullsp = (mat->symmetric_set && mat->symmetric && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8245:   return(0);
8246: }

8248: /*@
8249:    MatSetNullSpace - attaches a null space to a matrix.

8251:    Logically Collective on Mat and MatNullSpace

8253:    Input Parameters:
8254: +  mat - the matrix
8255: -  nullsp - the null space object

8257:    Level: advanced

8259:    Notes:
8260:       This null space is used by the linear solvers. Overwrites any previous null space that may have been attached

8262:       For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) you also likely should
8263:       call MatSetTransposeNullSpace(). This allows the linear system to be solved in a least squares sense.

8265:       You can remove the null space by calling this routine with an nullsp of NULL


8268:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8269:    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).
8270:    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
8271:    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
8272:    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).

8274:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8276:     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
8277:     routine also automatically calls MatSetTransposeNullSpace().

8279:    Concepts: null space^attaching to matrix

8281: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetTransposeNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8282: @*/
8283: PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp)
8284: {

8290:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8291:   MatNullSpaceDestroy(&mat->nullsp);
8292:   mat->nullsp = nullsp;
8293:   if (mat->symmetric_set && mat->symmetric) {
8294:     MatSetTransposeNullSpace(mat,nullsp);
8295:   }
8296:   return(0);
8297: }

8299: /*@
8300:    MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8302:    Logically Collective on Mat and MatNullSpace

8304:    Input Parameters:
8305: +  mat - the matrix
8306: -  nullsp - the null space object

8308:    Level: developer

8310:    Concepts: null space^attaching to matrix

8312: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetTransposeNullSpace(), MatSetNullSpace(), MatGetNullSpace()
8313: @*/
8314: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8315: {
8320:   *nullsp = (mat->symmetric_set && mat->symmetric && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8321:   return(0);
8322: }

8324: /*@
8325:    MatSetTransposeNullSpace - attaches a null space to a matrix.

8327:    Logically Collective on Mat and MatNullSpace

8329:    Input Parameters:
8330: +  mat - the matrix
8331: -  nullsp - the null space object

8333:    Level: advanced

8335:    Notes:
8336:       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.
8337:       You must also call MatSetNullSpace()


8340:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8341:    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).
8342:    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
8343:    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
8344:    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).

8346:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8348:    Concepts: null space^attaching to matrix

8350: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8351: @*/
8352: PetscErrorCode MatSetTransposeNullSpace(Mat mat,MatNullSpace nullsp)
8353: {

8359:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8360:   MatNullSpaceDestroy(&mat->transnullsp);
8361:   mat->transnullsp = nullsp;
8362:   return(0);
8363: }

8365: /*@
8366:    MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8367:         This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8369:    Logically Collective on Mat and MatNullSpace

8371:    Input Parameters:
8372: +  mat - the matrix
8373: -  nullsp - the null space object

8375:    Level: advanced

8377:    Notes:
8378:       Overwrites any previous near null space that may have been attached

8380:       You can remove the null space by calling this routine with an nullsp of NULL

8382:    Concepts: null space^attaching to matrix

8384: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace(), MatNullSpaceCreateRigidBody(), MatGetNearNullSpace()
8385: @*/
8386: PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp)
8387: {

8394:   MatCheckPreallocated(mat,1);
8395:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8396:   MatNullSpaceDestroy(&mat->nearnullsp);
8397:   mat->nearnullsp = nullsp;
8398:   return(0);
8399: }

8401: /*@
8402:    MatGetNearNullSpace -Get null space attached with MatSetNearNullSpace()

8404:    Not Collective

8406:    Input Parameters:
8407: .  mat - the matrix

8409:    Output Parameters:
8410: .  nullsp - the null space object, NULL if not set

8412:    Level: developer

8414:    Concepts: null space^attaching to matrix

8416: .seealso: MatSetNearNullSpace(), MatGetNullSpace(), MatNullSpaceCreate()
8417: @*/
8418: PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp)
8419: {
8424:   MatCheckPreallocated(mat,1);
8425:   *nullsp = mat->nearnullsp;
8426:   return(0);
8427: }

8429: /*@C
8430:    MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8432:    Collective on Mat

8434:    Input Parameters:
8435: +  mat - the matrix
8436: .  row - row/column permutation
8437: .  fill - expected fill factor >= 1.0
8438: -  level - level of fill, for ICC(k)

8440:    Notes:
8441:    Probably really in-place only when level of fill is zero, otherwise allocates
8442:    new space to store factored matrix and deletes previous memory.

8444:    Most users should employ the simplified KSP interface for linear solvers
8445:    instead of working directly with matrix algebra routines such as this.
8446:    See, e.g., KSPCreate().

8448:    Level: developer

8450:    Concepts: matrices^incomplete Cholesky factorization
8451:    Concepts: Cholesky factorization

8453: .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()

8455:     Developer Note: fortran interface is not autogenerated as the f90
8456:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8458: @*/
8459: PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info)
8460: {

8468:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
8469:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
8470:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
8471:   if (!mat->ops->iccfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8472:   MatCheckPreallocated(mat,1);
8473:   (*mat->ops->iccfactor)(mat,row,info);
8474:   PetscObjectStateIncrease((PetscObject)mat);
8475:   return(0);
8476: }

8478: /*@
8479:    MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
8480:          ghosted ones.

8482:    Not Collective

8484:    Input Parameters:
8485: +  mat - the matrix
8486: -  diag = the diagonal values, including ghost ones

8488:    Level: developer

8490:    Notes:
8491:     Works only for MPIAIJ and MPIBAIJ matrices

8493: .seealso: MatDiagonalScale()
8494: @*/
8495: PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag)
8496: {
8498:   PetscMPIInt    size;


8505:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled");
8506:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
8507:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
8508:   if (size == 1) {
8509:     PetscInt n,m;
8510:     VecGetSize(diag,&n);
8511:     MatGetSize(mat,0,&m);
8512:     if (m == n) {
8513:       MatDiagonalScale(mat,0,diag);
8514:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions");
8515:   } else {
8516:     PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));
8517:   }
8518:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
8519:   PetscObjectStateIncrease((PetscObject)mat);
8520:   return(0);
8521: }

8523: /*@
8524:    MatGetInertia - Gets the inertia from a factored matrix

8526:    Collective on Mat

8528:    Input Parameter:
8529: .  mat - the matrix

8531:    Output Parameters:
8532: +   nneg - number of negative eigenvalues
8533: .   nzero - number of zero eigenvalues
8534: -   npos - number of positive eigenvalues

8536:    Level: advanced

8538:    Notes:
8539:     Matrix must have been factored by MatCholeskyFactor()


8542: @*/
8543: PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
8544: {

8550:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8551:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled");
8552:   if (!mat->ops->getinertia) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8553:   (*mat->ops->getinertia)(mat,nneg,nzero,npos);
8554:   return(0);
8555: }

8557: /* ----------------------------------------------------------------*/
8558: /*@C
8559:    MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

8561:    Neighbor-wise Collective on Mat and Vecs

8563:    Input Parameters:
8564: +  mat - the factored matrix
8565: -  b - the right-hand-side vectors

8567:    Output Parameter:
8568: .  x - the result vectors

8570:    Notes:
8571:    The vectors b and x cannot be the same.  I.e., one cannot
8572:    call MatSolves(A,x,x).

8574:    Notes:
8575:    Most users should employ the simplified KSP interface for linear solvers
8576:    instead of working directly with matrix algebra routines such as this.
8577:    See, e.g., KSPCreate().

8579:    Level: developer

8581:    Concepts: matrices^triangular solves

8583: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve()
8584: @*/
8585: PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x)
8586: {

8592:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
8593:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8594:   if (!mat->rmap->N && !mat->cmap->N) return(0);

8596:   if (!mat->ops->solves) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8597:   MatCheckPreallocated(mat,1);
8598:   PetscLogEventBegin(MAT_Solves,mat,0,0,0);
8599:   (*mat->ops->solves)(mat,b,x);
8600:   PetscLogEventEnd(MAT_Solves,mat,0,0,0);
8601:   return(0);
8602: }

8604: /*@
8605:    MatIsSymmetric - Test whether a matrix is symmetric

8607:    Collective on Mat

8609:    Input Parameter:
8610: +  A - the matrix to test
8611: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

8613:    Output Parameters:
8614: .  flg - the result

8616:    Notes:
8617:     For real numbers MatIsSymmetric() and MatIsHermitian() return identical results

8619:    Level: intermediate

8621:    Concepts: matrix^symmetry

8623: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown()
8624: @*/
8625: PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool  *flg)
8626: {


8633:   if (!A->symmetric_set) {
8634:     if (!A->ops->issymmetric) {
8635:       MatType mattype;
8636:       MatGetType(A,&mattype);
8637:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8638:     }
8639:     (*A->ops->issymmetric)(A,tol,flg);
8640:     if (!tol) {
8641:       A->symmetric_set = PETSC_TRUE;
8642:       A->symmetric     = *flg;
8643:       if (A->symmetric) {
8644:         A->structurally_symmetric_set = PETSC_TRUE;
8645:         A->structurally_symmetric     = PETSC_TRUE;
8646:       }
8647:     }
8648:   } else if (A->symmetric) {
8649:     *flg = PETSC_TRUE;
8650:   } else if (!tol) {
8651:     *flg = PETSC_FALSE;
8652:   } else {
8653:     if (!A->ops->issymmetric) {
8654:       MatType mattype;
8655:       MatGetType(A,&mattype);
8656:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype);
8657:     }
8658:     (*A->ops->issymmetric)(A,tol,flg);
8659:   }
8660:   return(0);
8661: }

8663: /*@
8664:    MatIsHermitian - Test whether a matrix is Hermitian

8666:    Collective on Mat

8668:    Input Parameter:
8669: +  A - the matrix to test
8670: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

8672:    Output Parameters:
8673: .  flg - the result

8675:    Level: intermediate

8677:    Concepts: matrix^symmetry

8679: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(),
8680:           MatIsSymmetricKnown(), MatIsSymmetric()
8681: @*/
8682: PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool  *flg)
8683: {


8690:   if (!A->hermitian_set) {
8691:     if (!A->ops->ishermitian) {
8692:       MatType mattype;
8693:       MatGetType(A,&mattype);
8694:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8695:     }
8696:     (*A->ops->ishermitian)(A,tol,flg);
8697:     if (!tol) {
8698:       A->hermitian_set = PETSC_TRUE;
8699:       A->hermitian     = *flg;
8700:       if (A->hermitian) {
8701:         A->structurally_symmetric_set = PETSC_TRUE;
8702:         A->structurally_symmetric     = PETSC_TRUE;
8703:       }
8704:     }
8705:   } else if (A->hermitian) {
8706:     *flg = PETSC_TRUE;
8707:   } else if (!tol) {
8708:     *flg = PETSC_FALSE;
8709:   } else {
8710:     if (!A->ops->ishermitian) {
8711:       MatType mattype;
8712:       MatGetType(A,&mattype);
8713:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype);
8714:     }
8715:     (*A->ops->ishermitian)(A,tol,flg);
8716:   }
8717:   return(0);
8718: }

8720: /*@
8721:    MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric.

8723:    Not Collective

8725:    Input Parameter:
8726: .  A - the matrix to check

8728:    Output Parameters:
8729: +  set - if the symmetric flag is set (this tells you if the next flag is valid)
8730: -  flg - the result

8732:    Level: advanced

8734:    Concepts: matrix^symmetry

8736:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric()
8737:          if you want it explicitly checked

8739: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8740: @*/
8741: PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8742: {
8747:   if (A->symmetric_set) {
8748:     *set = PETSC_TRUE;
8749:     *flg = A->symmetric;
8750:   } else {
8751:     *set = PETSC_FALSE;
8752:   }
8753:   return(0);
8754: }

8756: /*@
8757:    MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian.

8759:    Not Collective

8761:    Input Parameter:
8762: .  A - the matrix to check

8764:    Output Parameters:
8765: +  set - if the hermitian flag is set (this tells you if the next flag is valid)
8766: -  flg - the result

8768:    Level: advanced

8770:    Concepts: matrix^symmetry

8772:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian()
8773:          if you want it explicitly checked

8775: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8776: @*/
8777: PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8778: {
8783:   if (A->hermitian_set) {
8784:     *set = PETSC_TRUE;
8785:     *flg = A->hermitian;
8786:   } else {
8787:     *set = PETSC_FALSE;
8788:   }
8789:   return(0);
8790: }

8792: /*@
8793:    MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

8795:    Collective on Mat

8797:    Input Parameter:
8798: .  A - the matrix to test

8800:    Output Parameters:
8801: .  flg - the result

8803:    Level: intermediate

8805:    Concepts: matrix^symmetry

8807: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption()
8808: @*/
8809: PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool  *flg)
8810: {

8816:   if (!A->structurally_symmetric_set) {
8817:     if (!A->ops->isstructurallysymmetric) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric");
8818:     (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);

8820:     A->structurally_symmetric_set = PETSC_TRUE;
8821:   }
8822:   *flg = A->structurally_symmetric;
8823:   return(0);
8824: }

8826: /*@
8827:    MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
8828:        to be communicated to other processors during the MatAssemblyBegin/End() process

8830:     Not collective

8832:    Input Parameter:
8833: .   vec - the vector

8835:    Output Parameters:
8836: +   nstash   - the size of the stash
8837: .   reallocs - the number of additional mallocs incurred.
8838: .   bnstash   - the size of the block stash
8839: -   breallocs - the number of additional mallocs incurred.in the block stash

8841:    Level: advanced

8843: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize()

8845: @*/
8846: PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs)
8847: {

8851:   MatStashGetInfo_Private(&mat->stash,nstash,reallocs);
8852:   MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);
8853:   return(0);
8854: }

8856: /*@C
8857:    MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
8858:      parallel layout

8860:    Collective on Mat

8862:    Input Parameter:
8863: .  mat - the matrix

8865:    Output Parameter:
8866: +   right - (optional) vector that the matrix can be multiplied against
8867: -   left - (optional) vector that the matrix vector product can be stored in

8869:    Notes:
8870:     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().

8872:   Notes:
8873:     These are new vectors which are not owned by the Mat, they should be destroyed in VecDestroy() when no longer needed

8875:   Level: advanced

8877: .seealso: MatCreate(), VecDestroy()
8878: @*/
8879: PetscErrorCode MatCreateVecs(Mat mat,Vec *right,Vec *left)
8880: {

8886:   if (mat->ops->getvecs) {
8887:     (*mat->ops->getvecs)(mat,right,left);
8888:   } else {
8889:     PetscInt rbs,cbs;
8890:     MatGetBlockSizes(mat,&rbs,&cbs);
8891:     if (right) {
8892:       if (mat->cmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for columns not yet setup");
8893:       VecCreate(PetscObjectComm((PetscObject)mat),right);
8894:       VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
8895:       VecSetBlockSize(*right,cbs);
8896:       VecSetType(*right,mat->defaultvectype);
8897:       PetscLayoutReference(mat->cmap,&(*right)->map);
8898:     }
8899:     if (left) {
8900:       if (mat->rmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for rows not yet setup");
8901:       VecCreate(PetscObjectComm((PetscObject)mat),left);
8902:       VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
8903:       VecSetBlockSize(*left,rbs);
8904:       VecSetType(*left,mat->defaultvectype);
8905:       PetscLayoutReference(mat->rmap,&(*left)->map);
8906:     }
8907:   }
8908:   return(0);
8909: }

8911: /*@C
8912:    MatFactorInfoInitialize - Initializes a MatFactorInfo data structure
8913:      with default values.

8915:    Not Collective

8917:    Input Parameters:
8918: .    info - the MatFactorInfo data structure


8921:    Notes:
8922:     The solvers are generally used through the KSP and PC objects, for example
8923:           PCLU, PCILU, PCCHOLESKY, PCICC

8925:    Level: developer

8927: .seealso: MatFactorInfo

8929:     Developer Note: fortran interface is not autogenerated as the f90
8930:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8932: @*/

8934: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
8935: {

8939:   PetscMemzero(info,sizeof(MatFactorInfo));
8940:   return(0);
8941: }

8943: /*@
8944:    MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

8946:    Collective on Mat

8948:    Input Parameters:
8949: +  mat - the factored matrix
8950: -  is - the index set defining the Schur indices (0-based)

8952:    Notes:
8953:     Call MatFactorSolveSchurComplement() or MatFactorSolveSchurComplementTranspose() after this call to solve a Schur complement system.

8955:    You can call MatFactorGetSchurComplement() or MatFactorCreateSchurComplement() after this call.

8957:    Level: developer

8959:    Concepts:

8961: .seealso: MatGetFactor(), MatFactorGetSchurComplement(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSolveSchurComplement(),
8962:           MatFactorSolveSchurComplementTranspose(), MatFactorSolveSchurComplement()

8964: @*/
8965: PetscErrorCode MatFactorSetSchurIS(Mat mat,IS is)
8966: {
8967:   PetscErrorCode ierr,(*f)(Mat,IS);

8975:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
8976:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorSetSchurIS_C",&f);
8977:   if (!f) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
8978:   if (mat->schur) {
8979:     MatDestroy(&mat->schur);
8980:   }
8981:   (*f)(mat,is);
8982:   if (!mat->schur) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_PLIB,"Schur complement has not been created");
8983:   MatFactorSetUpInPlaceSchur_Private(mat);
8984:   return(0);
8985: }

8987: /*@
8988:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

8990:    Logically Collective on Mat

8992:    Input Parameters:
8993: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
8994: .  S - location where to return the Schur complement, can be NULL
8995: -  status - the status of the Schur complement matrix, can be NULL

8997:    Notes:
8998:    You must call MatFactorSetSchurIS() before calling this routine.

9000:    The routine provides a copy of the Schur matrix stored within the solver data structures.
9001:    The caller must destroy the object when it is no longer needed.
9002:    If MatFactorInvertSchurComplement() has been called, the routine gets back the inverse.

9004:    Use MatFactorGetSchurComplement() to get access to the Schur complement matrix inside the factored matrix instead of making a copy of it (which this function does)

9006:    Developer Notes:
9007:     The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9008:    matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9010:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

9012:    Level: advanced

9014:    References:

9016: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorSchurStatus
9017: @*/
9018: PetscErrorCode MatFactorCreateSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
9019: {

9026:   if (S) {
9027:     PetscErrorCode (*f)(Mat,Mat*);

9029:     PetscObjectQueryFunction((PetscObject)F,"MatFactorCreateSchurComplement_C",&f);
9030:     if (f) {
9031:       (*f)(F,S);
9032:     } else {
9033:       MatDuplicate(F->schur,MAT_COPY_VALUES,S);
9034:     }
9035:   }
9036:   if (status) *status = F->schur_status;
9037:   return(0);
9038: }

9040: /*@
9041:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9043:    Logically Collective on Mat

9045:    Input Parameters:
9046: +  F - the factored matrix obtained by calling MatGetFactor()
9047: .  *S - location where to return the Schur complement, can be NULL
9048: -  status - the status of the Schur complement matrix, can be NULL

9050:    Notes:
9051:    You must call MatFactorSetSchurIS() before calling this routine.

9053:    Schur complement mode is currently implemented for sequential matrices.
9054:    The routine returns a the Schur Complement stored within the data strutures of the solver.
9055:    If MatFactorInvertSchurComplement() has previously been called, the returned matrix is actually the inverse of the Schur complement.
9056:    The returned matrix should not be destroyed; the caller should call MatFactorRestoreSchurComplement() when the object is no longer needed.

9058:    Use MatFactorCreateSchurComplement() to create a copy of the Schur complement matrix that is within a factored matrix

9060:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

9062:    Level: advanced

9064:    References:

9066: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
9067: @*/
9068: PetscErrorCode MatFactorGetSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
9069: {
9074:   if (S) *S = F->schur;
9075:   if (status) *status = F->schur_status;
9076:   return(0);
9077: }

9079: /*@
9080:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to MatFactorGetSchurComplement

9082:    Logically Collective on Mat

9084:    Input Parameters:
9085: +  F - the factored matrix obtained by calling MatGetFactor()
9086: .  *S - location where the Schur complement is stored
9087: -  status - the status of the Schur complement matrix (see MatFactorSchurStatus)

9089:    Notes:

9091:    Level: advanced

9093:    References:

9095: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
9096: @*/
9097: PetscErrorCode MatFactorRestoreSchurComplement(Mat F,Mat* S,MatFactorSchurStatus status)
9098: {

9103:   if (S) {
9105:     *S = NULL;
9106:   }
9107:   F->schur_status = status;
9108:   MatFactorUpdateSchurStatus_Private(F);
9109:   return(0);
9110: }

9112: /*@
9113:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9115:    Logically Collective on Mat

9117:    Input Parameters:
9118: +  F - the factored matrix obtained by calling MatGetFactor()
9119: .  rhs - location where the right hand side of the Schur complement system is stored
9120: -  sol - location where the solution of the Schur complement system has to be returned

9122:    Notes:
9123:    The sizes of the vectors should match the size of the Schur complement

9125:    Must be called after MatFactorSetSchurIS()

9127:    Level: advanced

9129:    References:

9131: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplement()
9132: @*/
9133: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9134: {

9146:   MatFactorFactorizeSchurComplement(F);
9147:   switch (F->schur_status) {
9148:   case MAT_FACTOR_SCHUR_FACTORED:
9149:     MatSolveTranspose(F->schur,rhs,sol);
9150:     break;
9151:   case MAT_FACTOR_SCHUR_INVERTED:
9152:     MatMultTranspose(F->schur,rhs,sol);
9153:     break;
9154:   default:
9155:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
9156:     break;
9157:   }
9158:   return(0);
9159: }

9161: /*@
9162:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9164:    Logically Collective on Mat

9166:    Input Parameters:
9167: +  F - the factored matrix obtained by calling MatGetFactor()
9168: .  rhs - location where the right hand side of the Schur complement system is stored
9169: -  sol - location where the solution of the Schur complement system has to be returned

9171:    Notes:
9172:    The sizes of the vectors should match the size of the Schur complement

9174:    Must be called after MatFactorSetSchurIS()

9176:    Level: advanced

9178:    References:

9180: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplementTranspose()
9181: @*/
9182: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9183: {

9195:   MatFactorFactorizeSchurComplement(F);
9196:   switch (F->schur_status) {
9197:   case MAT_FACTOR_SCHUR_FACTORED:
9198:     MatSolve(F->schur,rhs,sol);
9199:     break;
9200:   case MAT_FACTOR_SCHUR_INVERTED:
9201:     MatMult(F->schur,rhs,sol);
9202:     break;
9203:   default:
9204:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
9205:     break;
9206:   }
9207:   return(0);
9208: }

9210: /*@
9211:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9213:    Logically Collective on Mat

9215:    Input Parameters:
9216: +  F - the factored matrix obtained by calling MatGetFactor()

9218:    Notes:
9219:     Must be called after MatFactorSetSchurIS().

9221:    Call MatFactorGetSchurComplement() or  MatFactorCreateSchurComplement() AFTER this call to actually compute the inverse and get access to it.

9223:    Level: advanced

9225:    References:

9227: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorCreateSchurComplement()
9228: @*/
9229: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9230: {

9236:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) return(0);
9237:   MatFactorFactorizeSchurComplement(F);
9238:   MatFactorInvertSchurComplement_Private(F);
9239:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9240:   return(0);
9241: }

9243: /*@
9244:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9246:    Logically Collective on Mat

9248:    Input Parameters:
9249: +  F - the factored matrix obtained by calling MatGetFactor()

9251:    Notes:
9252:     Must be called after MatFactorSetSchurIS().

9254:    Level: advanced

9256:    References:

9258: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorInvertSchurComplement()
9259: @*/
9260: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9261: {

9267:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) return(0);
9268:   MatFactorFactorizeSchurComplement_Private(F);
9269:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9270:   return(0);
9271: }

9273: /*@
9274:    MatPtAP - Creates the matrix product C = P^T * A * P

9276:    Neighbor-wise Collective on Mat

9278:    Input Parameters:
9279: +  A - the matrix
9280: .  P - the projection matrix
9281: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9282: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use PETSC_DEFAULT if you do not have a good estimate
9283:           if the result is a dense matrix this is irrelevent

9285:    Output Parameters:
9286: .  C - the product matrix

9288:    Notes:
9289:    C will be created and must be destroyed by the user with MatDestroy().

9291:    This routine is currently only implemented for pairs of sequential dense matrices, AIJ matrices and classes
9292:    which inherit from AIJ.

9294:    Level: intermediate

9296: .seealso: MatPtAPSymbolic(), MatPtAPNumeric(), MatMatMult(), MatRARt()
9297: @*/
9298: PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C)
9299: {
9301:   PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*);
9302:   PetscErrorCode (*fP)(Mat,Mat,MatReuse,PetscReal,Mat*);
9303:   PetscErrorCode (*ptap)(Mat,Mat,MatReuse,PetscReal,Mat*)=NULL;
9304:   PetscBool      sametype;

9309:   MatCheckPreallocated(A,1);
9310:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
9311:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9312:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9315:   MatCheckPreallocated(P,2);
9316:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9317:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9319:   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);
9320:   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);
9321:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9322:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9324:   if (scall == MAT_REUSE_MATRIX) {

9328:     if (!(*C)->ops->ptapnumeric) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"MatPtAPNumeric implementation is missing. You cannot use MAT_REUSE_MATRIX");
9329:     PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9330:     PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9331:     (*(*C)->ops->ptapnumeric)(A,P,*C);
9332:     PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9333:     PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9334:     return(0);
9335:   }

9337:   if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0;
9338:   if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%g must be >= 1.0",(double)fill);

9340:   fA = A->ops->ptap;
9341:   fP = P->ops->ptap;
9342:   PetscStrcmp(((PetscObject)A)->type_name,((PetscObject)P)->type_name,&sametype);
9343:   if (fP == fA && sametype) {
9344:     if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",((PetscObject)A)->type_name);
9345:     ptap = fA;
9346:   } else {
9347:     /* dispatch based on the type of A and P from their PetscObject's PetscFunctionLists. */
9348:     char ptapname[256];
9349:     PetscStrncpy(ptapname,"MatPtAP_",sizeof(ptapname));
9350:     PetscStrlcat(ptapname,((PetscObject)A)->type_name,sizeof(ptapname));
9351:     PetscStrlcat(ptapname,"_",sizeof(ptapname));
9352:     PetscStrlcat(ptapname,((PetscObject)P)->type_name,sizeof(ptapname));
9353:     PetscStrlcat(ptapname,"_C",sizeof(ptapname)); /* e.g., ptapname = "MatPtAP_seqdense_seqaij_C" */
9354:     PetscObjectQueryFunction((PetscObject)P,ptapname,&ptap);
9355:     if (!ptap) SETERRQ3(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatPtAP requires A, %s, to be compatible with P, %s (Misses composed function %s)",((PetscObject)A)->type_name,((PetscObject)P)->type_name,ptapname);
9356:   }

9358:   PetscLogEventBegin(MAT_PtAP,A,P,0,0);
9359:   (*ptap)(A,P,scall,fill,C);
9360:   PetscLogEventEnd(MAT_PtAP,A,P,0,0);
9361:   if (A->symmetric_set && A->symmetric) {
9362:     MatSetOption(*C,MAT_SYMMETRIC,PETSC_TRUE);
9363:   }
9364:   return(0);
9365: }

9367: /*@
9368:    MatPtAPNumeric - Computes the matrix product C = P^T * A * P

9370:    Neighbor-wise Collective on Mat

9372:    Input Parameters:
9373: +  A - the matrix
9374: -  P - the projection matrix

9376:    Output Parameters:
9377: .  C - the product matrix

9379:    Notes:
9380:    C must have been created by calling MatPtAPSymbolic and must be destroyed by
9381:    the user using MatDeatroy().

9383:    This routine is currently only implemented for pairs of AIJ matrices and classes
9384:    which inherit from AIJ.  C will be of type MATAIJ.

9386:    Level: intermediate

9388: .seealso: MatPtAP(), MatPtAPSymbolic(), MatMatMultNumeric()
9389: @*/
9390: PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C)
9391: {

9397:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9398:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9401:   MatCheckPreallocated(P,2);
9402:   if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9403:   if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9406:   MatCheckPreallocated(C,3);
9407:   if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9408:   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);
9409:   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);
9410:   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);
9411:   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);
9412:   MatCheckPreallocated(A,1);

9414:   if (!C->ops->ptapnumeric) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"MatPtAPNumeric implementation is missing. You should call MatPtAPSymbolic first");
9415:   PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);
9416:   (*C->ops->ptapnumeric)(A,P,C);
9417:   PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);
9418:   return(0);
9419: }

9421: /*@
9422:    MatPtAPSymbolic - Creat