Actual source code: matrix.c

petsc-3.13.2 2020-06-02
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  1: /*
  2:    This is where the abstract matrix operations are defined
  3: */

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

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

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

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

 43: /*@
 44:    MatSetRandom - Sets all components of a matrix to random numbers. For sparse matrices that have been preallocated but not been assembled it randomly selects appropriate locations,
 45:                   for sparse matrices that already have locations it fills the locations with random numbers

 47:    Logically Collective on Mat

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

 54:    Output Parameter:
 55: .  x  - the matrix

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

 64:    Level: intermediate


 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:   MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY);
 94:   MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY);
 95:   PetscRandomDestroy(&randObj);
 96:   return(0);
 97: }

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

102:    Logically Collective on Mat

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

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

112:    Level: advanced

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

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

120: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
121: @*/
122: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat,PetscReal *pivot,PetscInt *row)
123: {
126:   *pivot = mat->factorerror_zeropivot_value;
127:   *row   = mat->factorerror_zeropivot_row;
128:   return(0);
129: }

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

134:    Logically Collective on Mat

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

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

142:    Level: advanced

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

147: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorClearError(), MatFactorGetErrorZeroPivot()
148: @*/
149: PetscErrorCode MatFactorGetError(Mat mat,MatFactorError *err)
150: {
153:   *err = mat->factorerrortype;
154:   return(0);
155: }

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

160:    Logically Collective on Mat

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

165:    Level: developer

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

170: .seealso: MatZeroEntries(), MatFactor(), MatGetFactor(), MatFactorSymbolic(), MatFactorGetError(), MatFactorGetErrorZeroPivot()
171: @*/
172: PetscErrorCode MatFactorClearError(Mat mat)
173: {
176:   mat->factorerrortype             = MAT_FACTOR_NOERROR;
177:   mat->factorerror_zeropivot_value = 0.0;
178:   mat->factorerror_zeropivot_row   = 0;
179:   return(0);
180: }

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

190:   MatCreateVecs(mat,&r,&l);
191:   if (!cols) { /* nonzero rows */
192:     MatGetSize(mat,&N,NULL);
193:     MatGetLocalSize(mat,&n,NULL);
194:     VecSet(l,0.0);
195:     VecSetRandom(r,NULL);
196:     MatMult(mat,r,l);
197:     VecGetArrayRead(l,&al);
198:   } else { /* nonzero columns */
199:     MatGetSize(mat,NULL,&N);
200:     MatGetLocalSize(mat,NULL,&n);
201:     VecSet(r,0.0);
202:     VecSetRandom(l,NULL);
203:     MatMultTranspose(mat,l,r);
204:     VecGetArrayRead(r,&al);
205:   }
206:   if (tol <= 0.0) { for (i=0,nz=0;i<n;i++) if (al[i] != 0.0) nz++; }
207:   else { for (i=0,nz=0;i<n;i++) if (PetscAbsScalar(al[i]) > tol) nz++; }
208:   MPIU_Allreduce(&nz,&gnz,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)mat));
209:   if (gnz != N) {
210:     PetscInt *nzr;
211:     PetscMalloc1(nz,&nzr);
212:     if (nz) {
213:       if (tol < 0) { for (i=0,nz=0;i<n;i++) if (al[i] != 0.0) nzr[nz++] = i; }
214:       else { for (i=0,nz=0;i<n;i++) if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i; }
215:     }
216:     ISCreateGeneral(PetscObjectComm((PetscObject)mat),nz,nzr,PETSC_OWN_POINTER,nonzero);
217:   } else *nonzero = NULL;
218:   if (!cols) { /* nonzero rows */
219:     VecRestoreArrayRead(l,&al);
220:   } else {
221:     VecRestoreArrayRead(r,&al);
222:   }
223:   VecDestroy(&l);
224:   VecDestroy(&r);
225:   return(0);
226: }

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

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

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

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

240:   Level: intermediate

242:  @*/
243: PetscErrorCode MatFindNonzeroRows(Mat mat,IS *keptrows)
244: {

251:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
252:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
253:   if (!mat->ops->findnonzerorows) {
254:     MatFindNonzeroRowsOrCols_Basic(mat,PETSC_FALSE,0.0,keptrows);
255:   } else {
256:     (*mat->ops->findnonzerorows)(mat,keptrows);
257:   }
258:   return(0);
259: }

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

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

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

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

273:   Level: intermediate

275:  @*/
276: PetscErrorCode MatFindZeroRows(Mat mat,IS *zerorows)
277: {
279:   IS keptrows;
280:   PetscInt m, n;


285:   MatFindNonzeroRows(mat, &keptrows);
286:   /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
287:      In keeping with this convention, we set zerorows to NULL if there are no zero
288:      rows. */
289:   if (keptrows == NULL) {
290:     *zerorows = NULL;
291:   } else {
292:     MatGetOwnershipRange(mat,&m,&n);
293:     ISComplement(keptrows,m,n,zerorows);
294:     ISDestroy(&keptrows);
295:   }
296:   return(0);
297: }

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

302:    Not Collective

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

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

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

315:    Level: advanced

317: @*/
318: PetscErrorCode MatGetDiagonalBlock(Mat A,Mat *a)
319: {

326:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
327:   if (!A->ops->getdiagonalblock) {
328:     PetscMPIInt size;
329:     MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
330:     if (size == 1) {
331:       *a = A;
332:       return(0);
333:     } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not coded for matrix type %s",((PetscObject)A)->type_name);
334:   }
335:   (*A->ops->getdiagonalblock)(A,a);
336:   return(0);
337: }

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

342:    Collective on Mat

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

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

350:    Level: advanced

352: @*/
353: PetscErrorCode MatGetTrace(Mat mat,PetscScalar *trace)
354: {
356:   Vec            diag;

359:   MatCreateVecs(mat,&diag,NULL);
360:   MatGetDiagonal(mat,diag);
361:   VecSum(diag,trace);
362:   VecDestroy(&diag);
363:   return(0);
364: }

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

369:    Logically Collective on Mat

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

374:    Level: advanced


377: .seealso: MatImaginaryPart()
378: @*/
379: PetscErrorCode MatRealPart(Mat mat)
380: {

386:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
387:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
388:   if (!mat->ops->realpart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
389:   MatCheckPreallocated(mat,1);
390:   (*mat->ops->realpart)(mat);
391:   return(0);
392: }

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

397:    Collective on Mat

399:    Input Parameter:
400: .  mat - the matrix

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

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

409:    Level: advanced

411: @*/
412: PetscErrorCode MatGetGhosts(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
413: {

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


431: /*@
432:    MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

434:    Logically Collective on Mat

436:    Input Parameters:
437: .  mat - the matrix

439:    Level: advanced


442: .seealso: MatRealPart()
443: @*/
444: PetscErrorCode MatImaginaryPart(Mat mat)
445: {

451:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
452:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
453:   if (!mat->ops->imaginarypart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
454:   MatCheckPreallocated(mat,1);
455:   (*mat->ops->imaginarypart)(mat);
456:   return(0);
457: }

459: /*@
460:    MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for BAIJ matrices)

462:    Not Collective

464:    Input Parameter:
465: .  mat - the matrix

467:    Output Parameters:
468: +  missing - is any diagonal missing
469: -  dd - first diagonal entry that is missing (optional) on this process

471:    Level: advanced


474: .seealso: MatRealPart()
475: @*/
476: PetscErrorCode MatMissingDiagonal(Mat mat,PetscBool *missing,PetscInt *dd)
477: {

484:   if (!mat->assembled) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix %s",((PetscObject)mat)->type_name);
485:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
486:   if (!mat->ops->missingdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
487:   (*mat->ops->missingdiagonal)(mat,missing,dd);
488:   return(0);
489: }

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

496:    Not Collective

498:    Input Parameters:
499: +  mat - the matrix
500: -  row - the row to get

502:    Output Parameters:
503: +  ncols -  if not NULL, the number of nonzeros in the row
504: .  cols - if not NULL, the column numbers
505: -  vals - if not NULL, the values

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

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

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

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

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

529:    Fortran Notes:
530:    The calling sequence from Fortran is
531: .vb
532:    MatGetRow(matrix,row,ncols,cols,values,ierr)
533:          Mat     matrix (input)
534:          integer row    (input)
535:          integer ncols  (output)
536:          integer cols(maxcols) (output)
537:          double precision (or double complex) values(maxcols) output
538: .ve
539:    where maxcols >= maximum nonzeros in any row of the matrix.


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

546:    Level: advanced

548: .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatCreateSubMatrices(), MatGetDiagonal()
549: @*/
550: PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
551: {
553:   PetscInt       incols;

558:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
559:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
560:   if (!mat->ops->getrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
561:   MatCheckPreallocated(mat,1);
562:   PetscLogEventBegin(MAT_GetRow,mat,0,0,0);
563:   (*mat->ops->getrow)(mat,row,&incols,(PetscInt**)cols,(PetscScalar**)vals);
564:   if (ncols) *ncols = incols;
565:   PetscLogEventEnd(MAT_GetRow,mat,0,0,0);
566:   return(0);
567: }

569: /*@
570:    MatConjugate - replaces the matrix values with their complex conjugates

572:    Logically Collective on Mat

574:    Input Parameters:
575: .  mat - the matrix

577:    Level: advanced

579: .seealso:  VecConjugate()
580: @*/
581: PetscErrorCode MatConjugate(Mat mat)
582: {
583: #if defined(PETSC_USE_COMPLEX)

588:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
589:   if (!mat->ops->conjugate) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not provided for matrix type %s, send email to petsc-maint@mcs.anl.gov",((PetscObject)mat)->type_name);
590:   (*mat->ops->conjugate)(mat);
591: #else
593: #endif
594:   return(0);
595: }

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

600:    Not Collective

602:    Input Parameters:
603: +  mat - the matrix
604: .  row - the row to get
605: .  ncols, cols - the number of nonzeros and their columns
606: -  vals - if nonzero the column values

608:    Notes:
609:    This routine should be called after you have finished examining the entries.

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

615:    Fortran Notes:
616:    The calling sequence from Fortran is
617: .vb
618:    MatRestoreRow(matrix,row,ncols,cols,values,ierr)
619:       Mat     matrix (input)
620:       integer row    (input)
621:       integer ncols  (output)
622:       integer cols(maxcols) (output)
623:       double precision (or double complex) values(maxcols) output
624: .ve
625:    Where maxcols >= maximum nonzeros in any row of the matrix.

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

630:    Level: advanced

632: .seealso:  MatGetRow()
633: @*/
634: PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[])
635: {

641:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
642:   if (!mat->ops->restorerow) return(0);
643:   (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);
644:   if (ncols) *ncols = 0;
645:   if (cols)  *cols = NULL;
646:   if (vals)  *vals = NULL;
647:   return(0);
648: }

650: /*@
651:    MatGetRowUpperTriangular - Sets a flag to enable calls to MatGetRow() for matrix in MATSBAIJ format.
652:    You should call MatRestoreRowUpperTriangular() after calling MatGetRow/MatRestoreRow() to disable the flag.

654:    Not Collective

656:    Input Parameters:
657: .  mat - the matrix

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

662:    Level: advanced

664: .seealso: MatRestoreRowUpperTriangular()
665: @*/
666: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
667: {

673:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
674:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
675:   MatCheckPreallocated(mat,1);
676:   if (!mat->ops->getrowuppertriangular) return(0);
677:   (*mat->ops->getrowuppertriangular)(mat);
678:   return(0);
679: }

681: /*@
682:    MatRestoreRowUpperTriangular - Disable calls to MatGetRow() for matrix in MATSBAIJ format.

684:    Not Collective

686:    Input Parameters:
687: .  mat - the matrix

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


693:    Level: advanced

695: .seealso:  MatGetRowUpperTriangular()
696: @*/
697: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
698: {

704:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
705:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
706:   MatCheckPreallocated(mat,1);
707:   if (!mat->ops->restorerowuppertriangular) return(0);
708:   (*mat->ops->restorerowuppertriangular)(mat);
709:   return(0);
710: }

712: /*@C
713:    MatSetOptionsPrefix - Sets the prefix used for searching for all
714:    Mat options in the database.

716:    Logically Collective on Mat

718:    Input Parameter:
719: +  A - the Mat context
720: -  prefix - the prefix to prepend to all option names

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

726:    Level: advanced

728: .seealso: MatSetFromOptions()
729: @*/
730: PetscErrorCode MatSetOptionsPrefix(Mat A,const char prefix[])
731: {

736:   PetscObjectSetOptionsPrefix((PetscObject)A,prefix);
737:   return(0);
738: }

740: /*@C
741:    MatAppendOptionsPrefix - Appends to the prefix used for searching for all
742:    Mat options in the database.

744:    Logically Collective on Mat

746:    Input Parameters:
747: +  A - the Mat context
748: -  prefix - the prefix to prepend to all option names

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

754:    Level: advanced

756: .seealso: MatGetOptionsPrefix()
757: @*/
758: PetscErrorCode MatAppendOptionsPrefix(Mat A,const char prefix[])
759: {

764:   PetscObjectAppendOptionsPrefix((PetscObject)A,prefix);
765:   return(0);
766: }

768: /*@C
769:    MatGetOptionsPrefix - Gets the prefix used for searching for all
770:    Mat options in the database.

772:    Not Collective

774:    Input Parameter:
775: .  A - the Mat context

777:    Output Parameter:
778: .  prefix - pointer to the prefix string used

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

784:    Level: advanced

786: .seealso: MatAppendOptionsPrefix()
787: @*/
788: PetscErrorCode MatGetOptionsPrefix(Mat A,const char *prefix[])
789: {

794:   PetscObjectGetOptionsPrefix((PetscObject)A,prefix);
795:   return(0);
796: }

798: /*@
799:    MatResetPreallocation - Reset mat to use the original nonzero pattern provided by users.

801:    Collective on Mat

803:    Input Parameters:
804: .  A - the Mat context

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

810:    Level: beginner

812: .seealso: MatSeqAIJSetPreallocation(), MatMPIAIJSetPreallocation(), MatXAIJSetPreallocation()
813: @*/
814: PetscErrorCode MatResetPreallocation(Mat A)
815: {

821:   PetscUseMethod(A,"MatResetPreallocation_C",(Mat),(A));
822:   return(0);
823: }


826: /*@
827:    MatSetUp - Sets up the internal matrix data structures for later use.

829:    Collective on Mat

831:    Input Parameters:
832: .  A - the Mat context

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

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

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

841:    Level: beginner

843: .seealso: MatCreate(), MatDestroy()
844: @*/
845: PetscErrorCode MatSetUp(Mat A)
846: {
847:   PetscMPIInt    size;

852:   if (!((PetscObject)A)->type_name) {
853:     MPI_Comm_size(PetscObjectComm((PetscObject)A), &size);
854:     if (size == 1) {
855:       MatSetType(A, MATSEQAIJ);
856:     } else {
857:       MatSetType(A, MATMPIAIJ);
858:     }
859:   }
860:   if (!A->preallocated && A->ops->setup) {
861:     PetscInfo(A,"Warning not preallocating matrix storage\n");
862:     (*A->ops->setup)(A);
863:   }
864:   PetscLayoutSetUp(A->rmap);
865:   PetscLayoutSetUp(A->cmap);
866:   A->preallocated = PETSC_TRUE;
867:   return(0);
868: }

870: #if defined(PETSC_HAVE_SAWS)
871:  #include <petscviewersaws.h>
872: #endif

874: /*@C
875:    MatViewFromOptions - View from Options

877:    Collective on Mat

879:    Input Parameters:
880: +  A - the Mat context
881: .  obj - Optional object
882: -  name - command line option

884:    Level: intermediate
885: .seealso:  Mat, MatView, PetscObjectViewFromOptions(), MatCreate()
886: @*/
887: PetscErrorCode  MatViewFromOptions(Mat A,PetscObject obj,const char name[])
888: {

893:   PetscObjectViewFromOptions((PetscObject)A,obj,name);
894:   return(0);
895: }

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:     The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
955:     the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

957:     See the manual page for MatLoad() for the exact format of the binary file when the binary
958:       viewer is used.

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

963:       One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
964:       and then use the following mouse functions.
965: + left mouse: zoom in
966: . middle mouse: zoom out
967: - right mouse: continue with the simulation

969: .seealso: PetscViewerPushFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(),
970:           PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad()
971: @*/
972: PetscErrorCode MatView(Mat mat,PetscViewer viewer)
973: {
974:   PetscErrorCode    ierr;
975:   PetscInt          rows,cols,rbs,cbs;
976:   PetscBool         isascii,isstring,issaws;
977:   PetscViewerFormat format;
978:   PetscMPIInt       size;

983:   if (!viewer) {PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat),&viewer);}
986:   MatCheckPreallocated(mat,1);

988:   PetscViewerGetFormat(viewer,&format);
989:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
990:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) return(0);

992:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSTRING,&isstring);
993:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
994:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSAWS,&issaws);
995:   if ((!isascii || (format != PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) && mat->factortype) {
996:     SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"No viewers for factored matrix except ASCII info or info_detail");
997:   }

999:   PetscLogEventBegin(MAT_View,mat,viewer,0,0);
1000:   if (isascii) {
1001:     if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix");
1002:     PetscObjectPrintClassNamePrefixType((PetscObject)mat,viewer);
1003:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1004:       MatNullSpace nullsp,transnullsp;

1006:       PetscViewerASCIIPushTab(viewer);
1007:       MatGetSize(mat,&rows,&cols);
1008:       MatGetBlockSizes(mat,&rbs,&cbs);
1009:       if (rbs != 1 || cbs != 1) {
1010:         if (rbs != cbs) {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, rbs=%D, cbs=%D\n",rows,cols,rbs,cbs);}
1011:         else            {PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, bs=%D\n",rows,cols,rbs);}
1012:       } else {
1013:         PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D\n",rows,cols);
1014:       }
1015:       if (mat->factortype) {
1016:         MatSolverType solver;
1017:         MatFactorGetSolverType(mat,&solver);
1018:         PetscViewerASCIIPrintf(viewer,"package used to perform factorization: %s\n",solver);
1019:       }
1020:       if (mat->ops->getinfo) {
1021:         MatInfo info;
1022:         MatGetInfo(mat,MAT_GLOBAL_SUM,&info);
1023:         PetscViewerASCIIPrintf(viewer,"total: nonzeros=%.f, allocated nonzeros=%.f\n",info.nz_used,info.nz_allocated);
1024:         PetscViewerASCIIPrintf(viewer,"total number of mallocs used during MatSetValues calls=%D\n",(PetscInt)info.mallocs);
1025:       }
1026:       MatGetNullSpace(mat,&nullsp);
1027:       MatGetTransposeNullSpace(mat,&transnullsp);
1028:       if (nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached null space\n");}
1029:       if (transnullsp && transnullsp != nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached transposed null space\n");}
1030:       MatGetNearNullSpace(mat,&nullsp);
1031:       if (nullsp) {PetscViewerASCIIPrintf(viewer,"  has attached near null space\n");}
1032:     }
1033:   } else if (issaws) {
1034: #if defined(PETSC_HAVE_SAWS)
1035:     PetscMPIInt rank;

1037:     PetscObjectName((PetscObject)mat);
1038:     MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
1039:     if (!((PetscObject)mat)->amsmem && !rank) {
1040:       PetscObjectViewSAWs((PetscObject)mat,viewer);
1041:     }
1042: #endif
1043:   } else if (isstring) {
1044:     const char *type;
1045:     MatGetType(mat,&type);
1046:     PetscViewerStringSPrintf(viewer," MatType: %-7.7s",type);
1047:     if (mat->ops->view) {(*mat->ops->view)(mat,viewer);}
1048:   }
1049:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1050:     PetscViewerASCIIPushTab(viewer);
1051:     (*mat->ops->viewnative)(mat,viewer);
1052:     PetscViewerASCIIPopTab(viewer);
1053:   } else if (mat->ops->view) {
1054:     PetscViewerASCIIPushTab(viewer);
1055:     (*mat->ops->view)(mat,viewer);
1056:     PetscViewerASCIIPopTab(viewer);
1057:   }
1058:   if (isascii) {
1059:     PetscViewerGetFormat(viewer,&format);
1060:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1061:       PetscViewerASCIIPopTab(viewer);
1062:     }
1063:   }
1064:   PetscLogEventEnd(MAT_View,mat,viewer,0,0);
1065:   return(0);
1066: }

1068: #if defined(PETSC_USE_DEBUG)
1069:  #include <../src/sys/totalview/tv_data_display.h>
1070: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1071: {
1072:   TV_add_row("Local rows", "int", &mat->rmap->n);
1073:   TV_add_row("Local columns", "int", &mat->cmap->n);
1074:   TV_add_row("Global rows", "int", &mat->rmap->N);
1075:   TV_add_row("Global columns", "int", &mat->cmap->N);
1076:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1077:   return TV_format_OK;
1078: }
1079: #endif

1081: /*@C
1082:    MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1083:    with MatView().  The matrix format is determined from the options database.
1084:    Generates a parallel MPI matrix if the communicator has more than one
1085:    processor.  The default matrix type is AIJ.

1087:    Collective on PetscViewer

1089:    Input Parameters:
1090: +  mat - the newly loaded matrix, this needs to have been created with MatCreate()
1091:             or some related function before a call to MatLoad()
1092: -  viewer - binary/HDF5 file viewer

1094:    Options Database Keys:
1095:    Used with block matrix formats (MATSEQBAIJ,  ...) to specify
1096:    block size
1097: .    -matload_block_size <bs>

1099:    Level: beginner

1101:    Notes:
1102:    If the Mat type has not yet been given then MATAIJ is used, call MatSetFromOptions() on the
1103:    Mat before calling this routine if you wish to set it from the options database.

1105:    MatLoad() automatically loads into the options database any options
1106:    given in the file filename.info where filename is the name of the file
1107:    that was passed to the PetscViewerBinaryOpen(). The options in the info
1108:    file will be ignored if you use the -viewer_binary_skip_info option.

1110:    If the type or size of mat is not set before a call to MatLoad, PETSc
1111:    sets the default matrix type AIJ and sets the local and global sizes.
1112:    If type and/or size is already set, then the same are used.

1114:    In parallel, each processor can load a subset of rows (or the
1115:    entire matrix).  This routine is especially useful when a large
1116:    matrix is stored on disk and only part of it is desired on each
1117:    processor.  For example, a parallel solver may access only some of
1118:    the rows from each processor.  The algorithm used here reads
1119:    relatively small blocks of data rather than reading the entire
1120:    matrix and then subsetting it.

1122:    Viewer's PetscViewerType must be either PETSCVIEWERBINARY or PETSCVIEWERHDF5.
1123:    Such viewer can be created using PetscViewerBinaryOpen()/PetscViewerHDF5Open(),
1124:    or the sequence like
1125: $    PetscViewer v;
1126: $    PetscViewerCreate(PETSC_COMM_WORLD,&v);
1127: $    PetscViewerSetType(v,PETSCVIEWERBINARY);
1128: $    PetscViewerSetFromOptions(v);
1129: $    PetscViewerFileSetMode(v,FILE_MODE_READ);
1130: $    PetscViewerFileSetName(v,"datafile");
1131:    The optional PetscViewerSetFromOptions() call allows to override PetscViewerSetType() using option
1132: $ -viewer_type {binary,hdf5}

1134:    See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1135:    and src/mat/tutorials/ex10.c with the second approach.

1137:    Notes about the PETSc binary format:
1138:    In case of PETSCVIEWERBINARY, a native PETSc binary format is used. Each of the blocks
1139:    is read onto rank 0 and then shipped to its destination rank, one after another.
1140:    Multiple objects, both matrices and vectors, can be stored within the same file.
1141:    Their PetscObject name is ignored; they are loaded in the order of their storage.

1143:    Most users should not need to know the details of the binary storage
1144:    format, since MatLoad() and MatView() completely hide these details.
1145:    But for anyone who's interested, the standard binary matrix storage
1146:    format is

1148: $    PetscInt    MAT_FILE_CLASSID
1149: $    PetscInt    number of rows
1150: $    PetscInt    number of columns
1151: $    PetscInt    total number of nonzeros
1152: $    PetscInt    *number nonzeros in each row
1153: $    PetscInt    *column indices of all nonzeros (starting index is zero)
1154: $    PetscScalar *values of all nonzeros

1156:    PETSc automatically does the byte swapping for
1157: machines that store the bytes reversed, e.g.  DEC alpha, freebsd,
1158: linux, Windows and the paragon; thus if you write your own binary
1159: read/write routines you have to swap the bytes; see PetscBinaryRead()
1160: and PetscBinaryWrite() to see how this may be done.

1162:    Notes about the HDF5 (MATLAB MAT-File Version 7.3) format:
1163:    In case of PETSCVIEWERHDF5, a parallel HDF5 reader is used.
1164:    Each processor's chunk is loaded independently by its owning rank.
1165:    Multiple objects, both matrices and vectors, can be stored within the same file.
1166:    They are looked up by their PetscObject name.

1168:    As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1169:    by default the same structure and naming of the AIJ arrays and column count
1170:    within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1171: $    save example.mat A b -v7.3
1172:    can be directly read by this routine (see Reference 1 for details).
1173:    Note that depending on your MATLAB version, this format might be a default,
1174:    otherwise you can set it as default in Preferences.

1176:    Unless -nocompression flag is used to save the file in MATLAB,
1177:    PETSc must be configured with ZLIB package.

1179:    See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1181:    Current HDF5 (MAT-File) limitations:
1182:    This reader currently supports only real MATSEQAIJ, MATMPIAIJ, MATSEQDENSE and MATMPIDENSE matrices.

1184:    Corresponding MatView() is not yet implemented.

1186:    The loaded matrix is actually a transpose of the original one in MATLAB,
1187:    unless you push PETSC_VIEWER_HDF5_MAT format (see examples above).
1188:    With this format, matrix is automatically transposed by PETSc,
1189:    unless the matrix is marked as SPD or symmetric
1190:    (see MatSetOption(), MAT_SPD, MAT_SYMMETRIC).

1192:    References:
1193: 1. MATLAB(R) Documentation, manual page of save(), https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version

1195: .seealso: PetscViewerBinaryOpen(), PetscViewerSetType(), MatView(), VecLoad()

1197:  @*/
1198: PetscErrorCode MatLoad(Mat mat,PetscViewer viewer)
1199: {
1201:   PetscBool      flg;


1207:   if (!((PetscObject)mat)->type_name) {
1208:     MatSetType(mat,MATAIJ);
1209:   }

1211:   flg  = PETSC_FALSE;
1212:   PetscOptionsGetBool(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matload_symmetric",&flg,NULL);
1213:   if (flg) {
1214:     MatSetOption(mat,MAT_SYMMETRIC,PETSC_TRUE);
1215:     MatSetOption(mat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE);
1216:   }
1217:   flg  = PETSC_FALSE;
1218:   PetscOptionsGetBool(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matload_spd",&flg,NULL);
1219:   if (flg) {
1220:     MatSetOption(mat,MAT_SPD,PETSC_TRUE);
1221:   }

1223:   if (!mat->ops->load) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatLoad is not supported for type %s",((PetscObject)mat)->type_name);
1224:   PetscLogEventBegin(MAT_Load,mat,viewer,0,0);
1225:   (*mat->ops->load)(mat,viewer);
1226:   PetscLogEventEnd(MAT_Load,mat,viewer,0,0);
1227:   return(0);
1228: }

1230: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1231: {
1233:   Mat_Redundant  *redund = *redundant;
1234:   PetscInt       i;

1237:   if (redund){
1238:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1239:       ISDestroy(&redund->isrow);
1240:       ISDestroy(&redund->iscol);
1241:       MatDestroySubMatrices(1,&redund->matseq);
1242:     } else {
1243:       PetscFree2(redund->send_rank,redund->recv_rank);
1244:       PetscFree(redund->sbuf_j);
1245:       PetscFree(redund->sbuf_a);
1246:       for (i=0; i<redund->nrecvs; i++) {
1247:         PetscFree(redund->rbuf_j[i]);
1248:         PetscFree(redund->rbuf_a[i]);
1249:       }
1250:       PetscFree4(redund->sbuf_nz,redund->rbuf_nz,redund->rbuf_j,redund->rbuf_a);
1251:     }

1253:     if (redund->subcomm) {
1254:       PetscCommDestroy(&redund->subcomm);
1255:     }
1256:     PetscFree(redund);
1257:   }
1258:   return(0);
1259: }

1261: /*@
1262:    MatDestroy - Frees space taken by a matrix.

1264:    Collective on Mat

1266:    Input Parameter:
1267: .  A - the matrix

1269:    Level: beginner

1271: @*/
1272: PetscErrorCode MatDestroy(Mat *A)
1273: {

1277:   if (!*A) return(0);
1279:   if (--((PetscObject)(*A))->refct > 0) {*A = NULL; return(0);}

1281:   /* if memory was published with SAWs then destroy it */
1282:   PetscObjectSAWsViewOff((PetscObject)*A);
1283:   if ((*A)->ops->destroy) {
1284:     (*(*A)->ops->destroy)(*A);
1285:   }

1287:   PetscFree((*A)->defaultvectype);
1288:   PetscFree((*A)->bsizes);
1289:   PetscFree((*A)->solvertype);
1290:   MatDestroy_Redundant(&(*A)->redundant);
1291:   MatProductClear(*A);

1293:   MatNullSpaceDestroy(&(*A)->nullsp);
1294:   MatNullSpaceDestroy(&(*A)->transnullsp);
1295:   MatNullSpaceDestroy(&(*A)->nearnullsp);
1296:   MatDestroy(&(*A)->schur);
1297:   PetscLayoutDestroy(&(*A)->rmap);
1298:   PetscLayoutDestroy(&(*A)->cmap);
1299:   PetscHeaderDestroy(A);
1300:   return(0);
1301: }

1303: /*@C
1304:    MatSetValues - Inserts or adds a block of values into a matrix.
1305:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
1306:    MUST be called after all calls to MatSetValues() have been completed.

1308:    Not Collective

1310:    Input Parameters:
1311: +  mat - the matrix
1312: .  v - a logically two-dimensional array of values
1313: .  m, idxm - the number of rows and their global indices
1314: .  n, idxn - the number of columns and their global indices
1315: -  addv - either ADD_VALUES or INSERT_VALUES, where
1316:    ADD_VALUES adds values to any existing entries, and
1317:    INSERT_VALUES replaces existing entries with new values

1319:    Notes:
1320:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
1321:       MatSetUp() before using this routine

1323:    By default the values, v, are row-oriented. See MatSetOption() for other options.

1325:    Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES
1326:    options cannot be mixed without intervening calls to the assembly
1327:    routines.

1329:    MatSetValues() uses 0-based row and column numbers in Fortran
1330:    as well as in C.

1332:    Negative indices may be passed in idxm and idxn, these rows and columns are
1333:    simply ignored. This allows easily inserting element stiffness matrices
1334:    with homogeneous Dirchlet boundary conditions that you don't want represented
1335:    in the matrix.

1337:    Efficiency Alert:
1338:    The routine MatSetValuesBlocked() may offer much better efficiency
1339:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1341:    Level: beginner

1343:    Developer Notes:
1344:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
1345:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1347: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1348:           InsertMode, INSERT_VALUES, ADD_VALUES
1349: @*/
1350: PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1351: {
1353: #if defined(PETSC_USE_DEBUG)
1354:   PetscInt       i,j;
1355: #endif

1360:   if (!m || !n) return(0); /* no values to insert */
1363:   MatCheckPreallocated(mat,1);

1365:   if (mat->insertmode == NOT_SET_VALUES) {
1366:     mat->insertmode = addv;
1367:   }
1368: #if defined(PETSC_USE_DEBUG)
1369:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1370:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1371:   if (!mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);

1373:   for (i=0; i<m; i++) {
1374:     for (j=0; j<n; j++) {
1375:       if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i*n+j]))
1376: #if defined(PETSC_USE_COMPLEX)
1377:         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]);
1378: #else
1379:         SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %g at matrix entry (%D,%D)",(double)v[i*n+j],idxm[i],idxn[j]);
1380: #endif
1381:     }
1382:   }
1383: #endif

1385:   if (mat->assembled) {
1386:     mat->was_assembled = PETSC_TRUE;
1387:     mat->assembled     = PETSC_FALSE;
1388:   }
1389:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1390:   (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);
1391:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1392:   return(0);
1393: }


1396: /*@
1397:    MatSetValuesRowLocal - Inserts a row (block row for BAIJ matrices) of nonzero
1398:         values into a matrix

1400:    Not Collective

1402:    Input Parameters:
1403: +  mat - the matrix
1404: .  row - the (block) row to set
1405: -  v - a logically two-dimensional array of values

1407:    Notes:
1408:    By the values, v, are column-oriented (for the block version) and sorted

1410:    All the nonzeros in the row must be provided

1412:    The matrix must have previously had its column indices set

1414:    The row must belong to this process

1416:    Level: intermediate

1418: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1419:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues(), MatSetValuesRow(), MatSetLocalToGlobalMapping()
1420: @*/
1421: PetscErrorCode MatSetValuesRowLocal(Mat mat,PetscInt row,const PetscScalar v[])
1422: {
1424:   PetscInt       globalrow;

1430:   ISLocalToGlobalMappingApply(mat->rmap->mapping,1,&row,&globalrow);
1431:   MatSetValuesRow(mat,globalrow,v);
1432:   return(0);
1433: }

1435: /*@
1436:    MatSetValuesRow - Inserts a row (block row for BAIJ matrices) of nonzero
1437:         values into a matrix

1439:    Not Collective

1441:    Input Parameters:
1442: +  mat - the matrix
1443: .  row - the (block) row to set
1444: -  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

1446:    Notes:
1447:    The values, v, are column-oriented for the block version.

1449:    All the nonzeros in the row must be provided

1451:    THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually MatSetValues() is used.

1453:    The row must belong to this process

1455:    Level: advanced

1457: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
1458:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
1459: @*/
1460: PetscErrorCode MatSetValuesRow(Mat mat,PetscInt row,const PetscScalar v[])
1461: {

1467:   MatCheckPreallocated(mat,1);
1469: #if defined(PETSC_USE_DEBUG)
1470:   if (mat->insertmode == ADD_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add and insert values");
1471:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1472: #endif
1473:   mat->insertmode = INSERT_VALUES;

1475:   if (mat->assembled) {
1476:     mat->was_assembled = PETSC_TRUE;
1477:     mat->assembled     = PETSC_FALSE;
1478:   }
1479:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1480:   if (!mat->ops->setvaluesrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1481:   (*mat->ops->setvaluesrow)(mat,row,v);
1482:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1483:   return(0);
1484: }

1486: /*@
1487:    MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1488:      Using structured grid indexing

1490:    Not Collective

1492:    Input Parameters:
1493: +  mat - the matrix
1494: .  m - number of rows being entered
1495: .  idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1496: .  n - number of columns being entered
1497: .  idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1498: .  v - a logically two-dimensional array of values
1499: -  addv - either ADD_VALUES or INSERT_VALUES, where
1500:    ADD_VALUES adds values to any existing entries, and
1501:    INSERT_VALUES replaces existing entries with new values

1503:    Notes:
1504:    By default the values, v, are row-oriented.  See MatSetOption() for other options.

1506:    Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES
1507:    options cannot be mixed without intervening calls to the assembly
1508:    routines.

1510:    The grid coordinates are across the entire grid, not just the local portion

1512:    MatSetValuesStencil() uses 0-based row and column numbers in Fortran
1513:    as well as in C.

1515:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1517:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1518:    or call MatSetLocalToGlobalMapping() and MatSetStencil() first.

1520:    The columns and rows in the stencil passed in MUST be contained within the
1521:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1522:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1523:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1524:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1526:    In Fortran idxm and idxn should be declared as
1527: $     MatStencil idxm(4,m),idxn(4,n)
1528:    and the values inserted using
1529: $    idxm(MatStencil_i,1) = i
1530: $    idxm(MatStencil_j,1) = j
1531: $    idxm(MatStencil_k,1) = k
1532: $    idxm(MatStencil_c,1) = c
1533:    etc

1535:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1536:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1537:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1538:    DM_BOUNDARY_PERIODIC boundary type.

1540:    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
1541:    a single value per point) you can skip filling those indices.

1543:    Inspired by the structured grid interface to the HYPRE package
1544:    (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1546:    Efficiency Alert:
1547:    The routine MatSetValuesBlockedStencil() may offer much better efficiency
1548:    for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ).

1550:    Level: beginner

1552: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1553:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil
1554: @*/
1555: PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1556: {
1558:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1559:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1560:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1563:   if (!m || !n) return(0); /* no values to insert */

1569:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1570:     jdxm = buf; jdxn = buf+m;
1571:   } else {
1572:     PetscMalloc2(m,&bufm,n,&bufn);
1573:     jdxm = bufm; jdxn = bufn;
1574:   }
1575:   for (i=0; i<m; i++) {
1576:     for (j=0; j<3-sdim; j++) dxm++;
1577:     tmp = *dxm++ - starts[0];
1578:     for (j=0; j<dim-1; j++) {
1579:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1580:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1581:     }
1582:     if (mat->stencil.noc) dxm++;
1583:     jdxm[i] = tmp;
1584:   }
1585:   for (i=0; i<n; i++) {
1586:     for (j=0; j<3-sdim; j++) dxn++;
1587:     tmp = *dxn++ - starts[0];
1588:     for (j=0; j<dim-1; j++) {
1589:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1590:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1591:     }
1592:     if (mat->stencil.noc) dxn++;
1593:     jdxn[i] = tmp;
1594:   }
1595:   MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);
1596:   PetscFree2(bufm,bufn);
1597:   return(0);
1598: }

1600: /*@
1601:    MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1602:      Using structured grid indexing

1604:    Not Collective

1606:    Input Parameters:
1607: +  mat - the matrix
1608: .  m - number of rows being entered
1609: .  idxm - grid coordinates for matrix rows being entered
1610: .  n - number of columns being entered
1611: .  idxn - grid coordinates for matrix columns being entered
1612: .  v - a logically two-dimensional array of values
1613: -  addv - either ADD_VALUES or INSERT_VALUES, where
1614:    ADD_VALUES adds values to any existing entries, and
1615:    INSERT_VALUES replaces existing entries with new values

1617:    Notes:
1618:    By default the values, v, are row-oriented and unsorted.
1619:    See MatSetOption() for other options.

1621:    Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES
1622:    options cannot be mixed without intervening calls to the assembly
1623:    routines.

1625:    The grid coordinates are across the entire grid, not just the local portion

1627:    MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran
1628:    as well as in C.

1630:    For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine

1632:    In order to use this routine you must either obtain the matrix with DMCreateMatrix()
1633:    or call MatSetBlockSize(), MatSetLocalToGlobalMapping() and MatSetStencil() first.

1635:    The columns and rows in the stencil passed in MUST be contained within the
1636:    ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example,
1637:    if you create a DMDA with an overlap of one grid level and on a particular process its first
1638:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1639:    first i index you can use in your column and row indices in MatSetStencil() is 5.

1641:    In Fortran idxm and idxn should be declared as
1642: $     MatStencil idxm(4,m),idxn(4,n)
1643:    and the values inserted using
1644: $    idxm(MatStencil_i,1) = i
1645: $    idxm(MatStencil_j,1) = j
1646: $    idxm(MatStencil_k,1) = k
1647:    etc

1649:    Negative indices may be passed in idxm and idxn, these rows and columns are
1650:    simply ignored. This allows easily inserting element stiffness matrices
1651:    with homogeneous Dirchlet boundary conditions that you don't want represented
1652:    in the matrix.

1654:    Inspired by the structured grid interface to the HYPRE package
1655:    (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1657:    Level: beginner

1659: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1660:           MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil,
1661:           MatSetBlockSize(), MatSetLocalToGlobalMapping()
1662: @*/
1663: PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv)
1664: {
1666:   PetscInt       buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn;
1667:   PetscInt       j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp;
1668:   PetscInt       *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1671:   if (!m || !n) return(0); /* no values to insert */

1678:   if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1679:     jdxm = buf; jdxn = buf+m;
1680:   } else {
1681:     PetscMalloc2(m,&bufm,n,&bufn);
1682:     jdxm = bufm; jdxn = bufn;
1683:   }
1684:   for (i=0; i<m; i++) {
1685:     for (j=0; j<3-sdim; j++) dxm++;
1686:     tmp = *dxm++ - starts[0];
1687:     for (j=0; j<sdim-1; j++) {
1688:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1689:       else                                       tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
1690:     }
1691:     dxm++;
1692:     jdxm[i] = tmp;
1693:   }
1694:   for (i=0; i<n; i++) {
1695:     for (j=0; j<3-sdim; j++) dxn++;
1696:     tmp = *dxn++ - starts[0];
1697:     for (j=0; j<sdim-1; j++) {
1698:       if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1;
1699:       else                                       tmp = tmp*dims[j] + *(dxn-1) - starts[j+1];
1700:     }
1701:     dxn++;
1702:     jdxn[i] = tmp;
1703:   }
1704:   MatSetValuesBlockedLocal(mat,m,jdxm,n,jdxn,v,addv);
1705:   PetscFree2(bufm,bufn);
1706:   return(0);
1707: }

1709: /*@
1710:    MatSetStencil - Sets the grid information for setting values into a matrix via
1711:         MatSetValuesStencil()

1713:    Not Collective

1715:    Input Parameters:
1716: +  mat - the matrix
1717: .  dim - dimension of the grid 1, 2, or 3
1718: .  dims - number of grid points in x, y, and z direction, including ghost points on your processor
1719: .  starts - starting point of ghost nodes on your processor in x, y, and z direction
1720: -  dof - number of degrees of freedom per node


1723:    Inspired by the structured grid interface to the HYPRE package
1724:    (www.llnl.gov/CASC/hyper)

1726:    For matrices generated with DMCreateMatrix() this routine is automatically called and so not needed by the
1727:    user.

1729:    Level: beginner

1731: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal()
1732:           MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil()
1733: @*/
1734: PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof)
1735: {
1736:   PetscInt i;


1743:   mat->stencil.dim = dim + (dof > 1);
1744:   for (i=0; i<dim; i++) {
1745:     mat->stencil.dims[i]   = dims[dim-i-1];      /* copy the values in backwards */
1746:     mat->stencil.starts[i] = starts[dim-i-1];
1747:   }
1748:   mat->stencil.dims[dim]   = dof;
1749:   mat->stencil.starts[dim] = 0;
1750:   mat->stencil.noc         = (PetscBool)(dof == 1);
1751:   return(0);
1752: }

1754: /*@C
1755:    MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1757:    Not Collective

1759:    Input Parameters:
1760: +  mat - the matrix
1761: .  v - a logically two-dimensional array of values
1762: .  m, idxm - the number of block rows and their global block indices
1763: .  n, idxn - the number of block columns and their global block indices
1764: -  addv - either ADD_VALUES or INSERT_VALUES, where
1765:    ADD_VALUES adds values to any existing entries, and
1766:    INSERT_VALUES replaces existing entries with new values

1768:    Notes:
1769:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call
1770:    MatXXXXSetPreallocation() or MatSetUp() before using this routine.

1772:    The m and n count the NUMBER of blocks in the row direction and column direction,
1773:    NOT the total number of rows/columns; for example, if the block size is 2 and
1774:    you are passing in values for rows 2,3,4,5  then m would be 2 (not 4).
1775:    The values in idxm would be 1 2; that is the first index for each block divided by
1776:    the block size.

1778:    Note that you must call MatSetBlockSize() when constructing this matrix (before
1779:    preallocating it).

1781:    By default the values, v, are row-oriented, so the layout of
1782:    v is the same as for MatSetValues(). See MatSetOption() for other options.

1784:    Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES
1785:    options cannot be mixed without intervening calls to the assembly
1786:    routines.

1788:    MatSetValuesBlocked() uses 0-based row and column numbers in Fortran
1789:    as well as in C.

1791:    Negative indices may be passed in idxm and idxn, these rows and columns are
1792:    simply ignored. This allows easily inserting element stiffness matrices
1793:    with homogeneous Dirchlet boundary conditions that you don't want represented
1794:    in the matrix.

1796:    Each time an entry is set within a sparse matrix via MatSetValues(),
1797:    internal searching must be done to determine where to place the
1798:    data in the matrix storage space.  By instead inserting blocks of
1799:    entries via MatSetValuesBlocked(), the overhead of matrix assembly is
1800:    reduced.

1802:    Example:
1803: $   Suppose m=n=2 and block size(bs) = 2 The array is
1804: $
1805: $   1  2  | 3  4
1806: $   5  6  | 7  8
1807: $   - - - | - - -
1808: $   9  10 | 11 12
1809: $   13 14 | 15 16
1810: $
1811: $   v[] should be passed in like
1812: $   v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
1813: $
1814: $  If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1815: $   v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]

1817:    Level: intermediate

1819: .seealso: MatSetBlockSize(), MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal()
1820: @*/
1821: PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv)
1822: {

1828:   if (!m || !n) return(0); /* no values to insert */
1832:   MatCheckPreallocated(mat,1);
1833:   if (mat->insertmode == NOT_SET_VALUES) {
1834:     mat->insertmode = addv;
1835:   }
1836: #if defined(PETSC_USE_DEBUG)
1837:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
1838:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1839:   if (!mat->ops->setvaluesblocked && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1840: #endif

1842:   if (mat->assembled) {
1843:     mat->was_assembled = PETSC_TRUE;
1844:     mat->assembled     = PETSC_FALSE;
1845:   }
1846:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
1847:   if (mat->ops->setvaluesblocked) {
1848:     (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);
1849:   } else {
1850:     PetscInt buf[8192],*bufr=0,*bufc=0,*iidxm,*iidxn;
1851:     PetscInt i,j,bs,cbs;
1852:     MatGetBlockSizes(mat,&bs,&cbs);
1853:     if (m*bs+n*cbs <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1854:       iidxm = buf; iidxn = buf + m*bs;
1855:     } else {
1856:       PetscMalloc2(m*bs,&bufr,n*cbs,&bufc);
1857:       iidxm = bufr; iidxn = bufc;
1858:     }
1859:     for (i=0; i<m; i++) {
1860:       for (j=0; j<bs; j++) {
1861:         iidxm[i*bs+j] = bs*idxm[i] + j;
1862:       }
1863:     }
1864:     for (i=0; i<n; i++) {
1865:       for (j=0; j<cbs; j++) {
1866:         iidxn[i*cbs+j] = cbs*idxn[i] + j;
1867:       }
1868:     }
1869:     MatSetValues(mat,m*bs,iidxm,n*cbs,iidxn,v,addv);
1870:     PetscFree2(bufr,bufc);
1871:   }
1872:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
1873:   return(0);
1874: }

1876: /*@C
1877:    MatGetValues - Gets a block of values from a matrix.

1879:    Not Collective; currently only returns a local block

1881:    Input Parameters:
1882: +  mat - the matrix
1883: .  v - a logically two-dimensional array for storing the values
1884: .  m, idxm - the number of rows and their global indices
1885: -  n, idxn - the number of columns and their global indices

1887:    Notes:
1888:    The user must allocate space (m*n PetscScalars) for the values, v.
1889:    The values, v, are then returned in a row-oriented format,
1890:    analogous to that used by default in MatSetValues().

1892:    MatGetValues() uses 0-based row and column numbers in
1893:    Fortran as well as in C.

1895:    MatGetValues() requires that the matrix has been assembled
1896:    with MatAssemblyBegin()/MatAssemblyEnd().  Thus, calls to
1897:    MatSetValues() and MatGetValues() CANNOT be made in succession
1898:    without intermediate matrix assembly.

1900:    Negative row or column indices will be ignored and those locations in v[] will be
1901:    left unchanged.

1903:    Level: advanced

1905: .seealso: MatGetRow(), MatCreateSubMatrices(), MatSetValues()
1906: @*/
1907: PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
1908: {

1914:   if (!m || !n) return(0);
1918:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
1919:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1920:   if (!mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1921:   MatCheckPreallocated(mat,1);

1923:   PetscLogEventBegin(MAT_GetValues,mat,0,0,0);
1924:   (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);
1925:   PetscLogEventEnd(MAT_GetValues,mat,0,0,0);
1926:   return(0);
1927: }

1929: /*@C
1930:    MatGetValuesLocal - retrieves values into certain locations of a matrix,
1931:    using a local numbering of the nodes.

1933:    Not Collective

1935:    Input Parameters:
1936: +  mat - the matrix
1937: .  nrow, irow - number of rows and their local indices
1938: -  ncol, icol - number of columns and their local indices

1940:    Output Parameter:
1941: .  y -  a logically two-dimensional array of values

1943:    Notes:
1944:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine

1946:    Level: advanced

1948:    Developer Notes:
1949:     This is labelled with C so does not automatically generate Fortran stubs and interfaces
1950:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1952: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(),
1953:            MatSetValuesLocal()
1954: @*/
1955: PetscErrorCode MatGetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],PetscScalar y[])
1956: {

1962:   MatCheckPreallocated(mat,1);
1963:   if (!nrow || !ncol) return(0); /* no values to retrieve */
1966: #if defined(PETSC_USE_DEBUG)
1967:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1968:   if (!mat->ops->getvalueslocal && !mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
1969: #endif
1970:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
1971:   PetscLogEventBegin(MAT_GetValues,mat,0,0,0);
1972:   if (mat->ops->getvalueslocal) {
1973:     (*mat->ops->getvalueslocal)(mat,nrow,irow,ncol,icol,y);
1974:   } else {
1975:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
1976:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
1977:       irowm = buf; icolm = buf+nrow;
1978:     } else {
1979:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
1980:       irowm = bufr; icolm = bufc;
1981:     }
1982:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
1983:     if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
1984:     ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);
1985:     ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);
1986:     MatGetValues(mat,nrow,irowm,ncol,icolm,y);
1987:     PetscFree2(bufr,bufc);
1988:   }
1989:   PetscLogEventEnd(MAT_GetValues,mat,0,0,0);
1990:   return(0);
1991: }

1993: /*@
1994:   MatSetValuesBatch - Adds (ADD_VALUES) many blocks of values into a matrix at once. The blocks must all be square and
1995:   the same size. Currently, this can only be called once and creates the given matrix.

1997:   Not Collective

1999:   Input Parameters:
2000: + mat - the matrix
2001: . nb - the number of blocks
2002: . bs - the number of rows (and columns) in each block
2003: . rows - a concatenation of the rows for each block
2004: - v - a concatenation of logically two-dimensional arrays of values

2006:   Notes:
2007:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2009:   Level: advanced

2011: .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(),
2012:           InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues()
2013: @*/
2014: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2015: {

2023: #if defined(PETSC_USE_DEBUG)
2024:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2025: #endif

2027:   PetscLogEventBegin(MAT_SetValuesBatch,mat,0,0,0);
2028:   if (mat->ops->setvaluesbatch) {
2029:     (*mat->ops->setvaluesbatch)(mat,nb,bs,rows,v);
2030:   } else {
2031:     PetscInt b;
2032:     for (b = 0; b < nb; ++b) {
2033:       MatSetValues(mat, bs, &rows[b*bs], bs, &rows[b*bs], &v[b*bs*bs], ADD_VALUES);
2034:     }
2035:   }
2036:   PetscLogEventEnd(MAT_SetValuesBatch,mat,0,0,0);
2037:   return(0);
2038: }

2040: /*@
2041:    MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2042:    the routine MatSetValuesLocal() to allow users to insert matrix entries
2043:    using a local (per-processor) numbering.

2045:    Not Collective

2047:    Input Parameters:
2048: +  x - the matrix
2049: .  rmapping - row mapping created with ISLocalToGlobalMappingCreate()   or ISLocalToGlobalMappingCreateIS()
2050: - cmapping - column mapping

2052:    Level: intermediate


2055: .seealso:  MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal()
2056: @*/
2057: PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping)
2058: {


2067:   if (x->ops->setlocaltoglobalmapping) {
2068:     (*x->ops->setlocaltoglobalmapping)(x,rmapping,cmapping);
2069:   } else {
2070:     PetscLayoutSetISLocalToGlobalMapping(x->rmap,rmapping);
2071:     PetscLayoutSetISLocalToGlobalMapping(x->cmap,cmapping);
2072:   }
2073:   return(0);
2074: }


2077: /*@
2078:    MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by MatSetLocalToGlobalMapping()

2080:    Not Collective

2082:    Input Parameters:
2083: .  A - the matrix

2085:    Output Parameters:
2086: + rmapping - row mapping
2087: - cmapping - column mapping

2089:    Level: advanced


2092: .seealso:  MatSetValuesLocal()
2093: @*/
2094: PetscErrorCode MatGetLocalToGlobalMapping(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping)
2095: {
2101:   if (rmapping) *rmapping = A->rmap->mapping;
2102:   if (cmapping) *cmapping = A->cmap->mapping;
2103:   return(0);
2104: }

2106: /*@
2107:    MatGetLayouts - Gets the PetscLayout objects for rows and columns

2109:    Not Collective

2111:    Input Parameters:
2112: .  A - the matrix

2114:    Output Parameters:
2115: + rmap - row layout
2116: - cmap - column layout

2118:    Level: advanced

2120: .seealso:  MatCreateVecs(), MatGetLocalToGlobalMapping()
2121: @*/
2122: PetscErrorCode MatGetLayouts(Mat A,PetscLayout *rmap,PetscLayout *cmap)
2123: {
2129:   if (rmap) *rmap = A->rmap;
2130:   if (cmap) *cmap = A->cmap;
2131:   return(0);
2132: }

2134: /*@C
2135:    MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2136:    using a local numbering of the nodes.

2138:    Not Collective

2140:    Input Parameters:
2141: +  mat - the matrix
2142: .  nrow, irow - number of rows and their local indices
2143: .  ncol, icol - number of columns and their local indices
2144: .  y -  a logically two-dimensional array of values
2145: -  addv - either INSERT_VALUES or ADD_VALUES, where
2146:    ADD_VALUES adds values to any existing entries, and
2147:    INSERT_VALUES replaces existing entries with new values

2149:    Notes:
2150:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2151:       MatSetUp() before using this routine

2153:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine

2155:    Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES
2156:    options cannot be mixed without intervening calls to the assembly
2157:    routines.

2159:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2160:    MUST be called after all calls to MatSetValuesLocal() have been completed.

2162:    Level: intermediate

2164:    Developer Notes:
2165:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2166:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

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

2178:   MatCheckPreallocated(mat,1);
2179:   if (!nrow || !ncol) return(0); /* no values to insert */
2182:   if (mat->insertmode == NOT_SET_VALUES) {
2183:     mat->insertmode = addv;
2184:   }
2185: #if defined(PETSC_USE_DEBUG)
2186:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2187:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2188:   if (!mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2189: #endif

2191:   if (mat->assembled) {
2192:     mat->was_assembled = PETSC_TRUE;
2193:     mat->assembled     = PETSC_FALSE;
2194:   }
2195:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2196:   if (mat->ops->setvalueslocal) {
2197:     (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);
2198:   } else {
2199:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2200:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2201:       irowm = buf; icolm = buf+nrow;
2202:     } else {
2203:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2204:       irowm = bufr; icolm = bufc;
2205:     }
2206:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MatSetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2207:     if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MatSetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2208:     ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);
2209:     ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);
2210:     MatSetValues(mat,nrow,irowm,ncol,icolm,y,addv);
2211:     PetscFree2(bufr,bufc);
2212:   }
2213:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2214:   return(0);
2215: }

2217: /*@C
2218:    MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2219:    using a local ordering of the nodes a block at a time.

2221:    Not Collective

2223:    Input Parameters:
2224: +  x - the matrix
2225: .  nrow, irow - number of rows and their local indices
2226: .  ncol, icol - number of columns and their local indices
2227: .  y -  a logically two-dimensional array of values
2228: -  addv - either INSERT_VALUES or ADD_VALUES, where
2229:    ADD_VALUES adds values to any existing entries, and
2230:    INSERT_VALUES replaces existing entries with new values

2232:    Notes:
2233:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or
2234:       MatSetUp() before using this routine

2236:    If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetBlockSize() and MatSetLocalToGlobalMapping()
2237:       before using this routineBefore calling MatSetValuesLocal(), the user must first set the

2239:    Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES
2240:    options cannot be mixed without intervening calls to the assembly
2241:    routines.

2243:    These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
2244:    MUST be called after all calls to MatSetValuesBlockedLocal() have been completed.

2246:    Level: intermediate

2248:    Developer Notes:
2249:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2250:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2252: .seealso:  MatSetBlockSize(), MatSetLocalToGlobalMapping(), MatAssemblyBegin(), MatAssemblyEnd(),
2253:            MatSetValuesLocal(),  MatSetValuesBlocked()
2254: @*/
2255: PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv)
2256: {

2262:   MatCheckPreallocated(mat,1);
2263:   if (!nrow || !ncol) return(0); /* no values to insert */
2267:   if (mat->insertmode == NOT_SET_VALUES) {
2268:     mat->insertmode = addv;
2269:   }
2270: #if defined(PETSC_USE_DEBUG)
2271:   else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
2272:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2273:   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);
2274: #endif

2276:   if (mat->assembled) {
2277:     mat->was_assembled = PETSC_TRUE;
2278:     mat->assembled     = PETSC_FALSE;
2279:   }
2280: #if defined(PETSC_USE_DEBUG)
2281:   /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2282:   if (mat->rmap->mapping) {
2283:     PetscInt irbs, rbs;
2284:     MatGetBlockSizes(mat, &rbs, NULL);
2285:     ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping,&irbs);
2286:     if (rbs != irbs) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Different row block sizes! mat %D, row l2g map %D",rbs,irbs);
2287:   }
2288:   if (mat->cmap->mapping) {
2289:     PetscInt icbs, cbs;
2290:     MatGetBlockSizes(mat,NULL,&cbs);
2291:     ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping,&icbs);
2292:     if (cbs != icbs) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Different col block sizes! mat %D, col l2g map %D",cbs,icbs);
2293:   }
2294: #endif
2295:   PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
2296:   if (mat->ops->setvaluesblockedlocal) {
2297:     (*mat->ops->setvaluesblockedlocal)(mat,nrow,irow,ncol,icol,y,addv);
2298:   } else {
2299:     PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm;
2300:     if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) {
2301:       irowm = buf; icolm = buf + nrow;
2302:     } else {
2303:       PetscMalloc2(nrow,&bufr,ncol,&bufc);
2304:       irowm = bufr; icolm = bufc;
2305:     }
2306:     ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping,nrow,irow,irowm);
2307:     ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping,ncol,icol,icolm);
2308:     MatSetValuesBlocked(mat,nrow,irowm,ncol,icolm,y,addv);
2309:     PetscFree2(bufr,bufc);
2310:   }
2311:   PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
2312:   return(0);
2313: }

2315: /*@
2316:    MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal

2318:    Collective on Mat

2320:    Input Parameters:
2321: +  mat - the matrix
2322: -  x   - the vector to be multiplied

2324:    Output Parameters:
2325: .  y - the result

2327:    Notes:
2328:    The vectors x and y cannot be the same.  I.e., one cannot
2329:    call MatMult(A,y,y).

2331:    Level: developer

2333: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2334: @*/
2335: PetscErrorCode MatMultDiagonalBlock(Mat mat,Vec x,Vec y)
2336: {


2345:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2346:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2347:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2348:   MatCheckPreallocated(mat,1);

2350:   if (!mat->ops->multdiagonalblock) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s does not have a multiply defined",((PetscObject)mat)->type_name);
2351:   (*mat->ops->multdiagonalblock)(mat,x,y);
2352:   PetscObjectStateIncrease((PetscObject)y);
2353:   return(0);
2354: }

2356: /* --------------------------------------------------------*/
2357: /*@
2358:    MatMult - Computes the matrix-vector product, y = Ax.

2360:    Neighbor-wise Collective on Mat

2362:    Input Parameters:
2363: +  mat - the matrix
2364: -  x   - the vector to be multiplied

2366:    Output Parameters:
2367: .  y - the result

2369:    Notes:
2370:    The vectors x and y cannot be the same.  I.e., one cannot
2371:    call MatMult(A,y,y).

2373:    Level: beginner

2375: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2376: @*/
2377: PetscErrorCode MatMult(Mat mat,Vec x,Vec y)
2378: {

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->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);
2391:   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);
2392:   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);
2393: #endif
2394:   VecSetErrorIfLocked(y,3);
2395:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2396:   MatCheckPreallocated(mat,1);

2398:   VecLockReadPush(x);
2399:   if (!mat->ops->mult) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s does not have a multiply defined",((PetscObject)mat)->type_name);
2400:   PetscLogEventBegin(MAT_Mult,mat,x,y,0);
2401:   (*mat->ops->mult)(mat,x,y);
2402:   PetscLogEventEnd(MAT_Mult,mat,x,y,0);
2403:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2404:   VecLockReadPop(x);
2405:   return(0);
2406: }

2408: /*@
2409:    MatMultTranspose - Computes matrix transpose times a vector y = A^T * x.

2411:    Neighbor-wise Collective on Mat

2413:    Input Parameters:
2414: +  mat - the matrix
2415: -  x   - the vector to be multiplied

2417:    Output Parameters:
2418: .  y - the result

2420:    Notes:
2421:    The vectors x and y cannot be the same.  I.e., one cannot
2422:    call MatMultTranspose(A,y,y).

2424:    For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2425:    use MatMultHermitianTranspose()

2427:    Level: beginner

2429: .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd(), MatMultHermitianTranspose(), MatTranspose()
2430: @*/
2431: PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y)
2432: {


2441:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2442:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2443:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2444: #if !defined(PETSC_HAVE_CONSTRAINTS)
2445:   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);
2446:   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);
2447: #endif
2448:   if (mat->erroriffailure) {VecValidValues(x,2,PETSC_TRUE);}
2449:   MatCheckPreallocated(mat,1);

2451:   if (!mat->ops->multtranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s does not have a multiply transpose defined",((PetscObject)mat)->type_name);
2452:   PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);
2453:   VecLockReadPush(x);
2454:   (*mat->ops->multtranspose)(mat,x,y);
2455:   VecLockReadPop(x);
2456:   PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);
2457:   PetscObjectStateIncrease((PetscObject)y);
2458:   if (mat->erroriffailure) {VecValidValues(y,3,PETSC_FALSE);}
2459:   return(0);
2460: }

2462: /*@
2463:    MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector.

2465:    Neighbor-wise Collective on Mat

2467:    Input Parameters:
2468: +  mat - the matrix
2469: -  x   - the vector to be multilplied

2471:    Output Parameters:
2472: .  y - the result

2474:    Notes:
2475:    The vectors x and y cannot be the same.  I.e., one cannot
2476:    call MatMultHermitianTranspose(A,y,y).

2478:    Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2480:    For real numbers MatMultTranspose() and MatMultHermitianTranspose() are identical.

2482:    Level: beginner

2484: .seealso: MatMult(), MatMultAdd(), MatMultHermitianTransposeAdd(), MatMultTranspose()
2485: @*/
2486: PetscErrorCode MatMultHermitianTranspose(Mat mat,Vec x,Vec y)
2487: {
2489:   Vec            w;


2497:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2498:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2499:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2500: #if !defined(PETSC_HAVE_CONSTRAINTS)
2501:   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);
2502:   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);
2503: #endif
2504:   MatCheckPreallocated(mat,1);

2506:   PetscLogEventBegin(MAT_MultHermitianTranspose,mat,x,y,0);
2507:   if (mat->ops->multhermitiantranspose) {
2508:     VecLockReadPush(x);
2509:     (*mat->ops->multhermitiantranspose)(mat,x,y);
2510:     VecLockReadPop(x);
2511:   } else {
2512:     VecDuplicate(x,&w);
2513:     VecCopy(x,w);
2514:     VecConjugate(w);
2515:     MatMultTranspose(mat,w,y);
2516:     VecDestroy(&w);
2517:     VecConjugate(y);
2518:   }
2519:   PetscLogEventEnd(MAT_MultHermitianTranspose,mat,x,y,0);
2520:   PetscObjectStateIncrease((PetscObject)y);
2521:   return(0);
2522: }

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

2527:     Neighbor-wise Collective on Mat

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 MatMultAdd(A,v1,v2,v1).

2540:     Level: beginner

2542: .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd()
2543: @*/
2544: PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2545: {


2555:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2556:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2557:   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);
2558:   /* 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);
2559:      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); */
2560:   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);
2561:   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);
2562:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2563:   MatCheckPreallocated(mat,1);

2565:   if (!mat->ops->multadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No MatMultAdd() for matrix type %s",((PetscObject)mat)->type_name);
2566:   PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);
2567:   VecLockReadPush(v1);
2568:   (*mat->ops->multadd)(mat,v1,v2,v3);
2569:   VecLockReadPop(v1);
2570:   PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);
2571:   PetscObjectStateIncrease((PetscObject)v3);
2572:   return(0);
2573: }

2575: /*@
2576:    MatMultTransposeAdd - Computes v3 = v2 + A' * v1.

2578:    Neighbor-wise Collective on Mat

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 MatMultTransposeAdd(A,v1,v2,v1).

2591:    Level: beginner

2593: .seealso: MatMultTranspose(), MatMultAdd(), MatMult()
2594: @*/
2595: PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2596: {


2606:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2607:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2608:   if (!mat->ops->multtransposeadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2609:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2610:   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);
2611:   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);
2612:   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);
2613:   MatCheckPreallocated(mat,1);

2615:   PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);
2616:   VecLockReadPush(v1);
2617:   (*mat->ops->multtransposeadd)(mat,v1,v2,v3);
2618:   VecLockReadPop(v1);
2619:   PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);
2620:   PetscObjectStateIncrease((PetscObject)v3);
2621:   return(0);
2622: }

2624: /*@
2625:    MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1.

2627:    Neighbor-wise Collective on Mat

2629:    Input Parameters:
2630: +  mat - the matrix
2631: -  v1, v2 - the vectors

2633:    Output Parameters:
2634: .  v3 - the result

2636:    Notes:
2637:    The vectors v1 and v3 cannot be the same.  I.e., one cannot
2638:    call MatMultHermitianTransposeAdd(A,v1,v2,v1).

2640:    Level: beginner

2642: .seealso: MatMultHermitianTranspose(), MatMultTranspose(), MatMultAdd(), MatMult()
2643: @*/
2644: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3)
2645: {


2655:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2656:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2657:   if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors");
2658:   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);
2659:   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);
2660:   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);
2661:   MatCheckPreallocated(mat,1);

2663:   PetscLogEventBegin(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2664:   VecLockReadPush(v1);
2665:   if (mat->ops->multhermitiantransposeadd) {
2666:     (*mat->ops->multhermitiantransposeadd)(mat,v1,v2,v3);
2667:   } else {
2668:     Vec w,z;
2669:     VecDuplicate(v1,&w);
2670:     VecCopy(v1,w);
2671:     VecConjugate(w);
2672:     VecDuplicate(v3,&z);
2673:     MatMultTranspose(mat,w,z);
2674:     VecDestroy(&w);
2675:     VecConjugate(z);
2676:     if (v2 != v3) {
2677:       VecWAXPY(v3,1.0,v2,z);
2678:     } else {
2679:       VecAXPY(v3,1.0,z);
2680:     }
2681:     VecDestroy(&z);
2682:   }
2683:   VecLockReadPop(v1);
2684:   PetscLogEventEnd(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);
2685:   PetscObjectStateIncrease((PetscObject)v3);
2686:   return(0);
2687: }

2689: /*@
2690:    MatMultConstrained - The inner multiplication routine for a
2691:    constrained matrix P^T A P.

2693:    Neighbor-wise Collective on Mat

2695:    Input Parameters:
2696: +  mat - the matrix
2697: -  x   - the vector to be multilplied

2699:    Output Parameters:
2700: .  y - the result

2702:    Notes:
2703:    The vectors x and y cannot be the same.  I.e., one cannot
2704:    call MatMult(A,y,y).

2706:    Level: beginner

2708: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2709: @*/
2710: PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y)
2711: {

2718:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2719:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2720:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2721:   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);
2722:   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);
2723:   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);

2725:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2726:   VecLockReadPush(x);
2727:   (*mat->ops->multconstrained)(mat,x,y);
2728:   VecLockReadPop(x);
2729:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2730:   PetscObjectStateIncrease((PetscObject)y);
2731:   return(0);
2732: }

2734: /*@
2735:    MatMultTransposeConstrained - The inner multiplication routine for a
2736:    constrained matrix P^T A^T P.

2738:    Neighbor-wise Collective on Mat

2740:    Input Parameters:
2741: +  mat - the matrix
2742: -  x   - the vector to be multilplied

2744:    Output Parameters:
2745: .  y - the result

2747:    Notes:
2748:    The vectors x and y cannot be the same.  I.e., one cannot
2749:    call MatMult(A,y,y).

2751:    Level: beginner

2753: .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
2754: @*/
2755: PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y)
2756: {

2763:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2764:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2765:   if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors");
2766:   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);
2767:   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);

2769:   PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);
2770:   (*mat->ops->multtransposeconstrained)(mat,x,y);
2771:   PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);
2772:   PetscObjectStateIncrease((PetscObject)y);
2773:   return(0);
2774: }

2776: /*@C
2777:    MatGetFactorType - gets the type of factorization it is

2779:    Not Collective

2781:    Input Parameters:
2782: .  mat - the matrix

2784:    Output Parameters:
2785: .  t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT

2787:    Level: intermediate

2789: .seealso: MatFactorType, MatGetFactor(), MatSetFactorType()
2790: @*/
2791: PetscErrorCode MatGetFactorType(Mat mat,MatFactorType *t)
2792: {
2797:   *t = mat->factortype;
2798:   return(0);
2799: }

2801: /*@C
2802:    MatSetFactorType - sets the type of factorization it is

2804:    Logically Collective on Mat

2806:    Input Parameters:
2807: +  mat - the matrix
2808: -  t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT

2810:    Level: intermediate

2812: .seealso: MatFactorType, MatGetFactor(), MatGetFactorType()
2813: @*/
2814: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
2815: {
2819:   mat->factortype = t;
2820:   return(0);
2821: }

2823: /* ------------------------------------------------------------*/
2824: /*@C
2825:    MatGetInfo - Returns information about matrix storage (number of
2826:    nonzeros, memory, etc.).

2828:    Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used as the flag

2830:    Input Parameters:
2831: .  mat - the matrix

2833:    Output Parameters:
2834: +  flag - flag indicating the type of parameters to be returned
2835:    (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors,
2836:    MAT_GLOBAL_SUM - sum over all processors)
2837: -  info - matrix information context

2839:    Notes:
2840:    The MatInfo context contains a variety of matrix data, including
2841:    number of nonzeros allocated and used, number of mallocs during
2842:    matrix assembly, etc.  Additional information for factored matrices
2843:    is provided (such as the fill ratio, number of mallocs during
2844:    factorization, etc.).  Much of this info is printed to PETSC_STDOUT
2845:    when using the runtime options
2846: $       -info -mat_view ::ascii_info

2848:    Example for C/C++ Users:
2849:    See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2850:    data within the MatInfo context.  For example,
2851: .vb
2852:       MatInfo info;
2853:       Mat     A;
2854:       double  mal, nz_a, nz_u;

2856:       MatGetInfo(A,MAT_LOCAL,&info);
2857:       mal  = info.mallocs;
2858:       nz_a = info.nz_allocated;
2859: .ve

2861:    Example for Fortran Users:
2862:    Fortran users should declare info as a double precision
2863:    array of dimension MAT_INFO_SIZE, and then extract the parameters
2864:    of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2865:    a complete list of parameter names.
2866: .vb
2867:       double  precision info(MAT_INFO_SIZE)
2868:       double  precision mal, nz_a
2869:       Mat     A
2870:       integer ierr

2872:       call MatGetInfo(A,MAT_LOCAL,info,ierr)
2873:       mal = info(MAT_INFO_MALLOCS)
2874:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
2875: .ve

2877:     Level: intermediate

2879:     Developer Note: fortran interface is not autogenerated as the f90
2880:     interface defintion cannot be generated correctly [due to MatInfo]

2882: .seealso: MatStashGetInfo()

2884: @*/
2885: PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info)
2886: {

2893:   if (!mat->ops->getinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2894:   MatCheckPreallocated(mat,1);
2895:   (*mat->ops->getinfo)(mat,flag,info);
2896:   return(0);
2897: }

2899: /*
2900:    This is used by external packages where it is not easy to get the info from the actual
2901:    matrix factorization.
2902: */
2903: PetscErrorCode MatGetInfo_External(Mat A,MatInfoType flag,MatInfo *info)
2904: {

2908:   PetscMemzero(info,sizeof(MatInfo));
2909:   return(0);
2910: }

2912: /* ----------------------------------------------------------*/

2914: /*@C
2915:    MatLUFactor - Performs in-place LU factorization of matrix.

2917:    Collective on Mat

2919:    Input Parameters:
2920: +  mat - the matrix
2921: .  row - row permutation
2922: .  col - column permutation
2923: -  info - options for factorization, includes
2924: $          fill - expected fill as ratio of original fill.
2925: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
2926: $                   Run with the option -info to determine an optimal value to use

2928:    Notes:
2929:    Most users should employ the simplified KSP interface for linear solvers
2930:    instead of working directly with matrix algebra routines such as this.
2931:    See, e.g., KSPCreate().

2933:    This changes the state of the matrix to a factored matrix; it cannot be used
2934:    for example with MatSetValues() unless one first calls MatSetUnfactored().

2936:    Level: developer

2938: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(),
2939:           MatGetOrdering(), MatSetUnfactored(), MatFactorInfo, MatGetFactor()

2941:     Developer Note: fortran interface is not autogenerated as the f90
2942:     interface defintion cannot be generated correctly [due to MatFactorInfo]

2944: @*/
2945: PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
2946: {
2948:   MatFactorInfo  tinfo;

2956:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2957:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2958:   if (!mat->ops->lufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
2959:   MatCheckPreallocated(mat,1);
2960:   if (!info) {
2961:     MatFactorInfoInitialize(&tinfo);
2962:     info = &tinfo;
2963:   }

2965:   PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);
2966:   (*mat->ops->lufactor)(mat,row,col,info);
2967:   PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);
2968:   PetscObjectStateIncrease((PetscObject)mat);
2969:   return(0);
2970: }

2972: /*@C
2973:    MatILUFactor - Performs in-place ILU factorization of matrix.

2975:    Collective on Mat

2977:    Input Parameters:
2978: +  mat - the matrix
2979: .  row - row permutation
2980: .  col - column permutation
2981: -  info - structure containing
2982: $      levels - number of levels of fill.
2983: $      expected fill - as ratio of original fill.
2984: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
2985:                 missing diagonal entries)

2987:    Notes:
2988:    Probably really in-place only when level of fill is zero, otherwise allocates
2989:    new space to store factored matrix and deletes previous memory.

2991:    Most users should employ the simplified KSP interface for linear solvers
2992:    instead of working directly with matrix algebra routines such as this.
2993:    See, e.g., KSPCreate().

2995:    Level: developer

2997: .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

2999:     Developer Note: fortran interface is not autogenerated as the f90
3000:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3002: @*/
3003: PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info)
3004: {

3013:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
3014:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3015:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3016:   if (!mat->ops->ilufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3017:   MatCheckPreallocated(mat,1);

3019:   PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);
3020:   (*mat->ops->ilufactor)(mat,row,col,info);
3021:   PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);
3022:   PetscObjectStateIncrease((PetscObject)mat);
3023:   return(0);
3024: }

3026: /*@C
3027:    MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3028:    Call this routine before calling MatLUFactorNumeric().

3030:    Collective on Mat

3032:    Input Parameters:
3033: +  fact - the factor matrix obtained with MatGetFactor()
3034: .  mat - the matrix
3035: .  row, col - row and column permutations
3036: -  info - options for factorization, includes
3037: $          fill - expected fill as ratio of original fill.
3038: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3039: $                   Run with the option -info to determine an optimal value to use


3042:    Notes:
3043:     See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

3045:    Most users should employ the simplified KSP interface for linear solvers
3046:    instead of working directly with matrix algebra routines such as this.
3047:    See, e.g., KSPCreate().

3049:    Level: developer

3051: .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo, MatFactorInfoInitialize()

3053:     Developer Note: fortran interface is not autogenerated as the f90
3054:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3056: @*/
3057: PetscErrorCode MatLUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
3058: {
3060:   MatFactorInfo  tinfo;

3069:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3070:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3071:   if (!(fact)->ops->lufactorsymbolic) {
3072:     MatSolverType spackage;
3073:     MatFactorGetSolverType(fact,&spackage);
3074:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic LU using solver package %s",((PetscObject)mat)->type_name,spackage);
3075:   }
3076:   MatCheckPreallocated(mat,2);
3077:   if (!info) {
3078:     MatFactorInfoInitialize(&tinfo);
3079:     info = &tinfo;
3080:   }

3082:   PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);
3083:   (fact->ops->lufactorsymbolic)(fact,mat,row,col,info);
3084:   PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);
3085:   PetscObjectStateIncrease((PetscObject)fact);
3086:   return(0);
3087: }

3089: /*@C
3090:    MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3091:    Call this routine after first calling MatLUFactorSymbolic().

3093:    Collective on Mat

3095:    Input Parameters:
3096: +  fact - the factor matrix obtained with MatGetFactor()
3097: .  mat - the matrix
3098: -  info - options for factorization

3100:    Notes:
3101:    See MatLUFactor() for in-place factorization.  See
3102:    MatCholeskyFactorNumeric() for the symmetric, positive definite case.

3104:    Most users should employ the simplified KSP interface for linear solvers
3105:    instead of working directly with matrix algebra routines such as this.
3106:    See, e.g., KSPCreate().

3108:    Level: developer

3110: .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor()

3112:     Developer Note: fortran interface is not autogenerated as the f90
3113:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3115: @*/
3116: PetscErrorCode MatLUFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3117: {
3118:   MatFactorInfo  tinfo;

3126:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3127:   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);

3129:   if (!(fact)->ops->lufactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric LU",((PetscObject)mat)->type_name);
3130:   MatCheckPreallocated(mat,2);
3131:   if (!info) {
3132:     MatFactorInfoInitialize(&tinfo);
3133:     info = &tinfo;
3134:   }

3136:   PetscLogEventBegin(MAT_LUFactorNumeric,mat,fact,0,0);
3137:   (fact->ops->lufactornumeric)(fact,mat,info);
3138:   PetscLogEventEnd(MAT_LUFactorNumeric,mat,fact,0,0);
3139:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3140:   PetscObjectStateIncrease((PetscObject)fact);
3141:   return(0);
3142: }

3144: /*@C
3145:    MatCholeskyFactor - Performs in-place Cholesky factorization of a
3146:    symmetric matrix.

3148:    Collective on Mat

3150:    Input Parameters:
3151: +  mat - the matrix
3152: .  perm - row and column permutations
3153: -  f - expected fill as ratio of original fill

3155:    Notes:
3156:    See MatLUFactor() for the nonsymmetric case.  See also
3157:    MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric().

3159:    Most users should employ the simplified KSP interface for linear solvers
3160:    instead of working directly with matrix algebra routines such as this.
3161:    See, e.g., KSPCreate().

3163:    Level: developer

3165: .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric()
3166:           MatGetOrdering()

3168:     Developer Note: fortran interface is not autogenerated as the f90
3169:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3171: @*/
3172: PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,const MatFactorInfo *info)
3173: {
3175:   MatFactorInfo  tinfo;

3182:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3183:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3184:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3185:   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);
3186:   MatCheckPreallocated(mat,1);
3187:   if (!info) {
3188:     MatFactorInfoInitialize(&tinfo);
3189:     info = &tinfo;
3190:   }

3192:   PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);
3193:   (*mat->ops->choleskyfactor)(mat,perm,info);
3194:   PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);
3195:   PetscObjectStateIncrease((PetscObject)mat);
3196:   return(0);
3197: }

3199: /*@C
3200:    MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3201:    of a symmetric matrix.

3203:    Collective on Mat

3205:    Input Parameters:
3206: +  fact - the factor matrix obtained with MatGetFactor()
3207: .  mat - the matrix
3208: .  perm - row and column permutations
3209: -  info - options for factorization, includes
3210: $          fill - expected fill as ratio of original fill.
3211: $          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3212: $                   Run with the option -info to determine an optimal value to use

3214:    Notes:
3215:    See MatLUFactorSymbolic() for the nonsymmetric case.  See also
3216:    MatCholeskyFactor() and MatCholeskyFactorNumeric().

3218:    Most users should employ the simplified KSP interface for linear solvers
3219:    instead of working directly with matrix algebra routines such as this.
3220:    See, e.g., KSPCreate().

3222:    Level: developer

3224: .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric()
3225:           MatGetOrdering()

3227:     Developer Note: fortran interface is not autogenerated as the f90
3228:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3230: @*/
3231: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
3232: {
3234:   MatFactorInfo  tinfo;

3242:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square");
3243:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3244:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3245:   if (!(fact)->ops->choleskyfactorsymbolic) {
3246:     MatSolverType spackage;
3247:     MatFactorGetSolverType(fact,&spackage);
3248:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s symbolic factor Cholesky using solver package %s",((PetscObject)mat)->type_name,spackage);
3249:   }
3250:   MatCheckPreallocated(mat,2);
3251:   if (!info) {
3252:     MatFactorInfoInitialize(&tinfo);
3253:     info = &tinfo;
3254:   }

3256:   PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3257:   (fact->ops->choleskyfactorsymbolic)(fact,mat,perm,info);
3258:   PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
3259:   PetscObjectStateIncrease((PetscObject)fact);
3260:   return(0);
3261: }

3263: /*@C
3264:    MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3265:    of a symmetric matrix. Call this routine after first calling
3266:    MatCholeskyFactorSymbolic().

3268:    Collective on Mat

3270:    Input Parameters:
3271: +  fact - the factor matrix obtained with MatGetFactor()
3272: .  mat - the initial matrix
3273: .  info - options for factorization
3274: -  fact - the symbolic factor of mat


3277:    Notes:
3278:    Most users should employ the simplified KSP interface for linear solvers
3279:    instead of working directly with matrix algebra routines such as this.
3280:    See, e.g., KSPCreate().

3282:    Level: developer

3284: .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric()

3286:     Developer Note: fortran interface is not autogenerated as the f90
3287:     interface defintion cannot be generated correctly [due to MatFactorInfo]

3289: @*/
3290: PetscErrorCode MatCholeskyFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info)
3291: {
3292:   MatFactorInfo  tinfo;

3300:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3301:   if (!(fact)->ops->choleskyfactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric factor Cholesky",((PetscObject)mat)->type_name);
3302:   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);
3303:   MatCheckPreallocated(mat,2);
3304:   if (!info) {
3305:     MatFactorInfoInitialize(&tinfo);
3306:     info = &tinfo;
3307:   }

3309:   PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3310:   (fact->ops->choleskyfactornumeric)(fact,mat,info);
3311:   PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,fact,0,0);
3312:   MatViewFromOptions(fact,NULL,"-mat_factor_view");
3313:   PetscObjectStateIncrease((PetscObject)fact);
3314:   return(0);
3315: }

3317: /* ----------------------------------------------------------------*/
3318: /*@
3319:    MatSolve - Solves A x = b, given a factored matrix.

3321:    Neighbor-wise Collective on Mat

3323:    Input Parameters:
3324: +  mat - the factored matrix
3325: -  b - the right-hand-side vector

3327:    Output Parameter:
3328: .  x - the result vector

3330:    Notes:
3331:    The vectors b and x cannot be the same.  I.e., one cannot
3332:    call MatSolve(A,x,x).

3334:    Notes:
3335:    Most users should employ the simplified KSP interface for linear solvers
3336:    instead of working directly with matrix algebra routines such as this.
3337:    See, e.g., KSPCreate().

3339:    Level: developer

3341: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd()
3342: @*/
3343: PetscErrorCode MatSolve(Mat mat,Vec b,Vec x)
3344: {

3354:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3355:   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);
3356:   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);
3357:   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);
3358:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3359:   MatCheckPreallocated(mat,1);

3361:   PetscLogEventBegin(MAT_Solve,mat,b,x,0);
3362:   if (mat->factorerrortype) {
3363:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3364:     VecSetInf(x);
3365:   } else {
3366:     if (!mat->ops->solve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3367:     (*mat->ops->solve)(mat,b,x);
3368:   }
3369:   PetscLogEventEnd(MAT_Solve,mat,b,x,0);
3370:   PetscObjectStateIncrease((PetscObject)x);
3371:   return(0);
3372: }

3374: static PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X,PetscBool trans)
3375: {
3377:   Vec            b,x;
3378:   PetscInt       m,N,i;
3379:   PetscScalar    *bb,*xx;

3382:   MatDenseGetArrayRead(B,(const PetscScalar**)&bb);
3383:   MatDenseGetArray(X,&xx);
3384:   MatGetLocalSize(B,&m,NULL);  /* number local rows */
3385:   MatGetSize(B,NULL,&N);       /* total columns in dense matrix */
3386:   MatCreateVecs(A,&x,&b);
3387:   for (i=0; i<N; i++) {
3388:     VecPlaceArray(b,bb + i*m);
3389:     VecPlaceArray(x,xx + i*m);
3390:     if (trans) {
3391:       MatSolveTranspose(A,b,x);
3392:     } else {
3393:       MatSolve(A,b,x);
3394:     }
3395:     VecResetArray(x);
3396:     VecResetArray(b);
3397:   }
3398:   VecDestroy(&b);
3399:   VecDestroy(&x);
3400:   MatDenseRestoreArrayRead(B,(const PetscScalar**)&bb);
3401:   MatDenseRestoreArray(X,&xx);
3402:   return(0);
3403: }

3405: /*@
3406:    MatMatSolve - Solves A X = B, given a factored matrix.

3408:    Neighbor-wise Collective on Mat

3410:    Input Parameters:
3411: +  A - the factored matrix
3412: -  B - the right-hand-side matrix MATDENSE (or sparse -- when using MUMPS)

3414:    Output Parameter:
3415: .  X - the result matrix (dense matrix)

3417:    Notes:
3418:    If B is a MATDENSE matrix then one can call MatMatSolve(A,B,B);
3419:    otherwise, B and X cannot be the same.

3421:    Notes:
3422:    Most users should usually employ the simplified KSP interface for linear solvers
3423:    instead of working directly with matrix algebra routines such as this.
3424:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3425:    at a time.

3427:    Level: developer

3429: .seealso: MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3430: @*/
3431: PetscErrorCode MatMatSolve(Mat A,Mat B,Mat X)
3432: {

3442:   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);
3443:   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);
3444:   if (X->cmap->N != B->cmap->N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Solution matrix must have same number of columns as rhs matrix");
3445:   if (!A->rmap->N && !A->cmap->N) return(0);
3446:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3447:   MatCheckPreallocated(A,1);

3449:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3450:   if (!A->ops->matsolve) {
3451:     PetscInfo1(A,"Mat type %s using basic MatMatSolve\n",((PetscObject)A)->type_name);
3452:     MatMatSolve_Basic(A,B,X,PETSC_FALSE);
3453:   } else {
3454:     (*A->ops->matsolve)(A,B,X);
3455:   }
3456:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3457:   PetscObjectStateIncrease((PetscObject)X);
3458:   return(0);
3459: }

3461: /*@
3462:    MatMatSolveTranspose - Solves A^T X = B, given a factored matrix.

3464:    Neighbor-wise Collective on Mat

3466:    Input Parameters:
3467: +  A - the factored matrix
3468: -  B - the right-hand-side matrix  (dense matrix)

3470:    Output Parameter:
3471: .  X - the result matrix (dense matrix)

3473:    Notes:
3474:    The matrices B and X cannot be the same.  I.e., one cannot
3475:    call MatMatSolveTranspose(A,X,X).

3477:    Notes:
3478:    Most users should usually employ the simplified KSP interface for linear solvers
3479:    instead of working directly with matrix algebra routines such as this.
3480:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3481:    at a time.

3483:    When using SuperLU_Dist or MUMPS as a parallel solver, PETSc will use their functionality to solve multiple right hand sides simultaneously.

3485:    Level: developer

3487: .seealso: MatMatSolve(), MatLUFactor(), MatCholeskyFactor()
3488: @*/
3489: PetscErrorCode MatMatSolveTranspose(Mat A,Mat B,Mat X)
3490: {

3500:   if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3501:   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);
3502:   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);
3503:   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);
3504:   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");
3505:   if (!A->rmap->N && !A->cmap->N) return(0);
3506:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3507:   MatCheckPreallocated(A,1);

3509:   PetscLogEventBegin(MAT_MatSolve,A,B,X,0);
3510:   if (!A->ops->matsolvetranspose) {
3511:     PetscInfo1(A,"Mat type %s using basic MatMatSolveTranspose\n",((PetscObject)A)->type_name);
3512:     MatMatSolve_Basic(A,B,X,PETSC_TRUE);
3513:   } else {
3514:     (*A->ops->matsolvetranspose)(A,B,X);
3515:   }
3516:   PetscLogEventEnd(MAT_MatSolve,A,B,X,0);
3517:   PetscObjectStateIncrease((PetscObject)X);
3518:   return(0);
3519: }

3521: /*@
3522:    MatMatTransposeSolve - Solves A X = B^T, given a factored matrix.

3524:    Neighbor-wise Collective on Mat

3526:    Input Parameters:
3527: +  A - the factored matrix
3528: -  Bt - the transpose of right-hand-side matrix

3530:    Output Parameter:
3531: .  X - the result matrix (dense matrix)

3533:    Notes:
3534:    Most users should usually employ the simplified KSP interface for linear solvers
3535:    instead of working directly with matrix algebra routines such as this.
3536:    See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X)
3537:    at a time.

3539:    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().

3541:    Level: developer

3543: .seealso: MatMatSolve(), MatMatSolveTranspose(), MatLUFactor(), MatCholeskyFactor()
3544: @*/
3545: PetscErrorCode MatMatTransposeSolve(Mat A,Mat Bt,Mat X)
3546: {


3557:   if (X == Bt) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices");
3558:   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);
3559:   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);
3560:   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");
3561:   if (!A->rmap->N && !A->cmap->N) return(0);
3562:   if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
3563:   MatCheckPreallocated(A,1);

3565:   if (!A->ops->mattransposesolve) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
3566:   PetscLogEventBegin(MAT_MatTrSolve,A,Bt,X,0);
3567:   (*A->ops->mattransposesolve)(A,Bt,X);
3568:   PetscLogEventEnd(MAT_MatTrSolve,A,Bt,X,0);
3569:   PetscObjectStateIncrease((PetscObject)X);
3570:   return(0);
3571: }

3573: /*@
3574:    MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3575:                             U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3577:    Neighbor-wise Collective on Mat

3579:    Input Parameters:
3580: +  mat - the factored matrix
3581: -  b - the right-hand-side vector

3583:    Output Parameter:
3584: .  x - the result vector

3586:    Notes:
3587:    MatSolve() should be used for most applications, as it performs
3588:    a forward solve followed by a backward solve.

3590:    The vectors b and x cannot be the same,  i.e., one cannot
3591:    call MatForwardSolve(A,x,x).

3593:    For matrix in seqsbaij format with block size larger than 1,
3594:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3595:    MatForwardSolve() solves U^T*D y = b, and
3596:    MatBackwardSolve() solves U x = y.
3597:    Thus they do not provide a symmetric preconditioner.

3599:    Most users should employ the simplified KSP interface for linear solvers
3600:    instead of working directly with matrix algebra routines such as this.
3601:    See, e.g., KSPCreate().

3603:    Level: developer

3605: .seealso: MatSolve(), MatBackwardSolve()
3606: @*/
3607: PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x)
3608: {

3618:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3619:   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);
3620:   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);
3621:   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);
3622:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3623:   MatCheckPreallocated(mat,1);

3625:   if (!mat->ops->forwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3626:   PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);
3627:   (*mat->ops->forwardsolve)(mat,b,x);
3628:   PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);
3629:   PetscObjectStateIncrease((PetscObject)x);
3630:   return(0);
3631: }

3633: /*@
3634:    MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3635:                              D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3637:    Neighbor-wise Collective on Mat

3639:    Input Parameters:
3640: +  mat - the factored matrix
3641: -  b - the right-hand-side vector

3643:    Output Parameter:
3644: .  x - the result vector

3646:    Notes:
3647:    MatSolve() should be used for most applications, as it performs
3648:    a forward solve followed by a backward solve.

3650:    The vectors b and x cannot be the same.  I.e., one cannot
3651:    call MatBackwardSolve(A,x,x).

3653:    For matrix in seqsbaij format with block size larger than 1,
3654:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3655:    MatForwardSolve() solves U^T*D y = b, and
3656:    MatBackwardSolve() solves U x = y.
3657:    Thus they do not provide a symmetric preconditioner.

3659:    Most users should employ the simplified KSP interface for linear solvers
3660:    instead of working directly with matrix algebra routines such as this.
3661:    See, e.g., KSPCreate().

3663:    Level: developer

3665: .seealso: MatSolve(), MatForwardSolve()
3666: @*/
3667: PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x)
3668: {

3678:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3679:   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);
3680:   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);
3681:   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);
3682:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3683:   MatCheckPreallocated(mat,1);

3685:   if (!mat->ops->backwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3686:   PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);
3687:   (*mat->ops->backwardsolve)(mat,b,x);
3688:   PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);
3689:   PetscObjectStateIncrease((PetscObject)x);
3690:   return(0);
3691: }

3693: /*@
3694:    MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3696:    Neighbor-wise Collective on Mat

3698:    Input Parameters:
3699: +  mat - the factored matrix
3700: .  b - the right-hand-side vector
3701: -  y - the vector to be added to

3703:    Output Parameter:
3704: .  x - the result vector

3706:    Notes:
3707:    The vectors b and x cannot be the same.  I.e., one cannot
3708:    call MatSolveAdd(A,x,y,x).

3710:    Most users should employ the simplified KSP interface for linear solvers
3711:    instead of working directly with matrix algebra routines such as this.
3712:    See, e.g., KSPCreate().

3714:    Level: developer

3716: .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd()
3717: @*/
3718: PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x)
3719: {
3720:   PetscScalar    one = 1.0;
3721:   Vec            tmp;

3733:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3734:   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);
3735:   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);
3736:   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);
3737:   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);
3738:   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);
3739:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3740:    MatCheckPreallocated(mat,1);

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

3767: /*@
3768:    MatSolveTranspose - Solves A' x = b, given a factored matrix.

3770:    Neighbor-wise Collective on Mat

3772:    Input Parameters:
3773: +  mat - the factored matrix
3774: -  b - the right-hand-side vector

3776:    Output Parameter:
3777: .  x - the result vector

3779:    Notes:
3780:    The vectors b and x cannot be the same.  I.e., one cannot
3781:    call MatSolveTranspose(A,x,x).

3783:    Most users should employ the simplified KSP interface for linear solvers
3784:    instead of working directly with matrix algebra routines such as this.
3785:    See, e.g., KSPCreate().

3787:    Level: developer

3789: .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd()
3790: @*/
3791: PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x)
3792: {

3802:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3803:   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);
3804:   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);
3805:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3806:   MatCheckPreallocated(mat,1);
3807:   PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);
3808:   if (mat->factorerrortype) {
3809:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3810:     VecSetInf(x);
3811:   } else {
3812:     if (!mat->ops->solvetranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name);
3813:     (*mat->ops->solvetranspose)(mat,b,x);
3814:   }
3815:   PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);
3816:   PetscObjectStateIncrease((PetscObject)x);
3817:   return(0);
3818: }

3820: /*@
3821:    MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
3822:                       factored matrix.

3824:    Neighbor-wise Collective on Mat

3826:    Input Parameters:
3827: +  mat - the factored matrix
3828: .  b - the right-hand-side vector
3829: -  y - the vector to be added to

3831:    Output Parameter:
3832: .  x - the result vector

3834:    Notes:
3835:    The vectors b and x cannot be the same.  I.e., one cannot
3836:    call MatSolveTransposeAdd(A,x,y,x).

3838:    Most users should employ the simplified KSP interface for linear solvers
3839:    instead of working directly with matrix algebra routines such as this.
3840:    See, e.g., KSPCreate().

3842:    Level: developer

3844: .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose()
3845: @*/
3846: PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x)
3847: {
3848:   PetscScalar    one = 1.0;
3850:   Vec            tmp;

3861:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
3862:   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);
3863:   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);
3864:   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);
3865:   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);
3866:   if (!mat->rmap->N && !mat->cmap->N) return(0);
3867:    MatCheckPreallocated(mat,1);

3869:   PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);
3870:   if (mat->factorerrortype) {
3871:     PetscInfo1(mat,"MatFactorError %D\n",mat->factorerrortype);
3872:     VecSetInf(x);
3873:   } else if (mat->ops->solvetransposeadd){
3874:     (*mat->ops->solvetransposeadd)(mat,b,y,x);
3875:   } else {
3876:     /* do the solve then the add manually */
3877:     if (x != y) {
3878:       MatSolveTranspose(mat,b,x);
3879:       VecAXPY(x,one,y);
3880:     } else {
3881:       VecDuplicate(x,&tmp);
3882:       PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);
3883:       VecCopy(x,tmp);
3884:       MatSolveTranspose(mat,b,x);
3885:       VecAXPY(x,one,tmp);
3886:       VecDestroy(&tmp);
3887:     }
3888:   }
3889:   PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);
3890:   PetscObjectStateIncrease((PetscObject)x);
3891:   return(0);
3892: }
3893: /* ----------------------------------------------------------------*/

3895: /*@
3896:    MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

3898:    Neighbor-wise Collective on Mat

3900:    Input Parameters:
3901: +  mat - the matrix
3902: .  b - the right hand side
3903: .  omega - the relaxation factor
3904: .  flag - flag indicating the type of SOR (see below)
3905: .  shift -  diagonal shift
3906: .  its - the number of iterations
3907: -  lits - the number of local iterations

3909:    Output Parameters:
3910: .  x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess)

3912:    SOR Flags:
3913: +     SOR_FORWARD_SWEEP - forward SOR
3914: .     SOR_BACKWARD_SWEEP - backward SOR
3915: .     SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR)
3916: .     SOR_LOCAL_FORWARD_SWEEP - local forward SOR
3917: .     SOR_LOCAL_BACKWARD_SWEEP - local forward SOR
3918: .     SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR
3919: .     SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies
3920:          upper/lower triangular part of matrix to
3921:          vector (with omega)
3922: -     SOR_ZERO_INITIAL_GUESS - zero initial guess

3924:    Notes:
3925:    SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and
3926:    SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings
3927:    on each processor.

3929:    Application programmers will not generally use MatSOR() directly,
3930:    but instead will employ the KSP/PC interface.

3932:    Notes:
3933:     for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

3935:    Notes for Advanced Users:
3936:    The flags are implemented as bitwise inclusive or operations.
3937:    For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP)
3938:    to specify a zero initial guess for SSOR.

3940:    Most users should employ the simplified KSP interface for linear solvers
3941:    instead of working directly with matrix algebra routines such as this.
3942:    See, e.g., KSPCreate().

3944:    Vectors x and b CANNOT be the same

3946:    Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes

3948:    Level: developer

3950: @*/
3951: PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x)
3952: {

3962:   if (!mat->ops->sor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
3963:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3964:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3965:   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);
3966:   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);
3967:   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);
3968:   if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its);
3969:   if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits);
3970:   if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same");

3972:   MatCheckPreallocated(mat,1);
3973:   PetscLogEventBegin(MAT_SOR,mat,b,x,0);
3974:   ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);
3975:   PetscLogEventEnd(MAT_SOR,mat,b,x,0);
3976:   PetscObjectStateIncrease((PetscObject)x);
3977:   return(0);
3978: }

3980: /*
3981:       Default matrix copy routine.
3982: */
3983: PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str)
3984: {
3985:   PetscErrorCode    ierr;
3986:   PetscInt          i,rstart = 0,rend = 0,nz;
3987:   const PetscInt    *cwork;
3988:   const PetscScalar *vwork;

3991:   if (B->assembled) {
3992:     MatZeroEntries(B);
3993:   }
3994:   if (str == SAME_NONZERO_PATTERN) {
3995:     MatGetOwnershipRange(A,&rstart,&rend);
3996:     for (i=rstart; i<rend; i++) {
3997:       MatGetRow(A,i,&nz,&cwork,&vwork);
3998:       MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);
3999:       MatRestoreRow(A,i,&nz,&cwork,&vwork);
4000:     }
4001:   } else {
4002:     MatAYPX(B,0.0,A,str);
4003:   }
4004:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4005:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
4006:   return(0);
4007: }

4009: /*@
4010:    MatCopy - Copies a matrix to another matrix.

4012:    Collective on Mat

4014:    Input Parameters:
4015: +  A - the matrix
4016: -  str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN

4018:    Output Parameter:
4019: .  B - where the copy is put

4021:    Notes:
4022:    If you use SAME_NONZERO_PATTERN then the two matrices had better have the
4023:    same nonzero pattern or the routine will crash.

4025:    MatCopy() copies the matrix entries of a matrix to another existing
4026:    matrix (after first zeroing the second matrix).  A related routine is
4027:    MatConvert(), which first creates a new matrix and then copies the data.

4029:    Level: intermediate

4031: .seealso: MatConvert(), MatDuplicate()

4033: @*/
4034: PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str)
4035: {
4037:   PetscInt       i;

4045:   MatCheckPreallocated(B,2);
4046:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4047:   if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4048:   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);
4049:   MatCheckPreallocated(A,1);
4050:   if (A == B) return(0);

4052:   PetscLogEventBegin(MAT_Copy,A,B,0,0);
4053:   if (A->ops->copy) {
4054:     (*A->ops->copy)(A,B,str);
4055:   } else { /* generic conversion */
4056:     MatCopy_Basic(A,B,str);
4057:   }

4059:   B->stencil.dim = A->stencil.dim;
4060:   B->stencil.noc = A->stencil.noc;
4061:   for (i=0; i<=A->stencil.dim; i++) {
4062:     B->stencil.dims[i]   = A->stencil.dims[i];
4063:     B->stencil.starts[i] = A->stencil.starts[i];
4064:   }

4066:   PetscLogEventEnd(MAT_Copy,A,B,0,0);
4067:   PetscObjectStateIncrease((PetscObject)B);
4068:   return(0);
4069: }

4071: /*@C
4072:    MatConvert - Converts a matrix to another matrix, either of the same
4073:    or different type.

4075:    Collective on Mat

4077:    Input Parameters:
4078: +  mat - the matrix
4079: .  newtype - new matrix type.  Use MATSAME to create a new matrix of the
4080:    same type as the original matrix.
4081: -  reuse - denotes if the destination matrix is to be created or reused.
4082:    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
4083:    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).

4085:    Output Parameter:
4086: .  M - pointer to place new matrix

4088:    Notes:
4089:    MatConvert() first creates a new matrix and then copies the data from
4090:    the first matrix.  A related routine is MatCopy(), which copies the matrix
4091:    entries of one matrix to another already existing matrix context.

4093:    Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4094:    the MPI communicator of the generated matrix is always the same as the communicator
4095:    of the input matrix.

4097:    Level: intermediate

4099: .seealso: MatCopy(), MatDuplicate()
4100: @*/
4101: PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M)
4102: {
4104:   PetscBool      sametype,issame,flg,issymmetric,ishermitian;
4105:   char           convname[256],mtype[256];
4106:   Mat            B;

4112:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4113:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4114:   MatCheckPreallocated(mat,1);

4116:   PetscOptionsGetString(((PetscObject)mat)->options,((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);
4117:   if (flg) newtype = mtype;

4119:   PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);
4120:   PetscStrcmp(newtype,"same",&issame);
4121:   if ((reuse == MAT_INPLACE_MATRIX) && (mat != *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires same input and output matrix");
4122:   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");

4124:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4125:     PetscInfo3(mat,"Early return for inplace %s %d %d\n",((PetscObject)mat)->type_name,sametype,issame);
4126:     return(0);
4127:   }

4129:   /* Cache Mat options because some converter use MatHeaderReplace  */
4130:   issymmetric = mat->symmetric;
4131:   ishermitian = mat->hermitian;

4133:   if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4134:     PetscInfo3(mat,"Calling duplicate for initial matrix %s %d %d\n",((PetscObject)mat)->type_name,sametype,issame);
4135:     (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);
4136:   } else {
4137:     PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=NULL;
4138:     const char     *prefix[3] = {"seq","mpi",""};
4139:     PetscInt       i;
4140:     /*
4141:        Order of precedence:
4142:        0) See if newtype is a superclass of the current matrix.
4143:        1) See if a specialized converter is known to the current matrix.
4144:        2) See if a specialized converter is known to the desired matrix class.
4145:        3) See if a good general converter is registered for the desired class
4146:           (as of 6/27/03 only MATMPIADJ falls into this category).
4147:        4) See if a good general converter is known for the current matrix.
4148:        5) Use a really basic converter.
4149:     */

4151:     /* 0) See if newtype is a superclass of the current matrix.
4152:           i.e mat is mpiaij and newtype is aij */
4153:     for (i=0; i<2; i++) {
4154:       PetscStrncpy(convname,prefix[i],sizeof(convname));
4155:       PetscStrlcat(convname,newtype,sizeof(convname));
4156:       PetscStrcmp(convname,((PetscObject)mat)->type_name,&flg);
4157:       PetscInfo3(mat,"Check superclass %s %s -> %d\n",convname,((PetscObject)mat)->type_name,flg);
4158:       if (flg) {
4159:         if (reuse == MAT_INPLACE_MATRIX) {
4160:           return(0);
4161:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4162:           (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);
4163:           return(0);
4164:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4165:           MatCopy(mat,*M,SAME_NONZERO_PATTERN);
4166:           return(0);
4167:         }
4168:       }
4169:     }
4170:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4171:     for (i=0; i<3; i++) {
4172:       PetscStrncpy(convname,"MatConvert_",sizeof(convname));
4173:       PetscStrlcat(convname,((PetscObject)mat)->type_name,sizeof(convname));
4174:       PetscStrlcat(convname,"_",sizeof(convname));
4175:       PetscStrlcat(convname,prefix[i],sizeof(convname));
4176:       PetscStrlcat(convname,issame ? ((PetscObject)mat)->type_name : newtype,sizeof(convname));
4177:       PetscStrlcat(convname,"_C",sizeof(convname));
4178:       PetscObjectQueryFunction((PetscObject)mat,convname,&conv);
4179:       PetscInfo3(mat,"Check specialized (1) %s (%s) -> %d\n",convname,((PetscObject)mat)->type_name,!!conv);
4180:       if (conv) goto foundconv;
4181:     }

4183:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4184:     MatCreate(PetscObjectComm((PetscObject)mat),&B);
4185:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);
4186:     MatSetType(B,newtype);
4187:     for (i=0; i<3; i++) {
4188:       PetscStrncpy(convname,"MatConvert_",sizeof(convname));
4189:       PetscStrlcat(convname,((PetscObject)mat)->type_name,sizeof(convname));
4190:       PetscStrlcat(convname,"_",sizeof(convname));
4191:       PetscStrlcat(convname,prefix[i],sizeof(convname));
4192:       PetscStrlcat(convname,newtype,sizeof(convname));
4193:       PetscStrlcat(convname,"_C",sizeof(convname));
4194:       PetscObjectQueryFunction((PetscObject)B,convname,&conv);
4195:       PetscInfo3(mat,"Check specialized (2) %s (%s) -> %d\n",convname,((PetscObject)B)->type_name,!!conv);
4196:       if (conv) {
4197:         MatDestroy(&B);
4198:         goto foundconv;
4199:       }
4200:     }

4202:     /* 3) See if a good general converter is registered for the desired class */
4203:     conv = B->ops->convertfrom;
4204:     PetscInfo2(mat,"Check convertfrom (%s) -> %d\n",((PetscObject)B)->type_name,!!conv);
4205:     MatDestroy(&B);
4206:     if (conv) goto foundconv;

4208:     /* 4) See if a good general converter is known for the current matrix */
4209:     if (mat->ops->convert) conv = mat->ops->convert;

4211:     PetscInfo2(mat,"Check general convert (%s) -> %d\n",((PetscObject)mat)->type_name,!!conv);
4212:     if (conv) goto foundconv;

4214:     /* 5) Use a really basic converter. */
4215:     PetscInfo(mat,"Using MatConvert_Basic\n");
4216:     conv = MatConvert_Basic;

4218: foundconv:
4219:     PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4220:     (*conv)(mat,newtype,reuse,M);
4221:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4222:       /* the block sizes must be same if the mappings are copied over */
4223:       (*M)->rmap->bs = mat->rmap->bs;
4224:       (*M)->cmap->bs = mat->cmap->bs;
4225:       PetscObjectReference((PetscObject)mat->rmap->mapping);
4226:       PetscObjectReference((PetscObject)mat->cmap->mapping);
4227:       (*M)->rmap->mapping = mat->rmap->mapping;
4228:       (*M)->cmap->mapping = mat->cmap->mapping;
4229:     }
4230:     (*M)->stencil.dim = mat->stencil.dim;
4231:     (*M)->stencil.noc = mat->stencil.noc;
4232:     for (i=0; i<=mat->stencil.dim; i++) {
4233:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4234:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4235:     }
4236:     PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4237:   }
4238:   PetscObjectStateIncrease((PetscObject)*M);

4240:   /* Copy Mat options */
4241:   if (issymmetric) {
4242:     MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);
4243:   }
4244:   if (ishermitian) {
4245:     MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);
4246:   }
4247:   return(0);
4248: }

4250: /*@C
4251:    MatFactorGetSolverType - Returns name of the package providing the factorization routines

4253:    Not Collective

4255:    Input Parameter:
4256: .  mat - the matrix, must be a factored matrix

4258:    Output Parameter:
4259: .   type - the string name of the package (do not free this string)

4261:    Notes:
4262:       In Fortran you pass in a empty string and the package name will be copied into it.
4263:     (Make sure the string is long enough)

4265:    Level: intermediate

4267: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor()
4268: @*/
4269: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4270: {
4271:   PetscErrorCode ierr, (*conv)(Mat,MatSolverType*);

4276:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
4277:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverType_C",&conv);
4278:   if (!conv) {
4279:     *type = MATSOLVERPETSC;
4280:   } else {
4281:     (*conv)(mat,type);
4282:   }
4283:   return(0);
4284: }

4286: typedef struct _MatSolverTypeForSpecifcType* MatSolverTypeForSpecifcType;
4287: struct _MatSolverTypeForSpecifcType {
4288:   MatType                        mtype;
4289:   PetscErrorCode                 (*getfactor[4])(Mat,MatFactorType,Mat*);
4290:   MatSolverTypeForSpecifcType next;
4291: };

4293: typedef struct _MatSolverTypeHolder* MatSolverTypeHolder;
4294: struct _MatSolverTypeHolder {
4295:   char                           *name;
4296:   MatSolverTypeForSpecifcType handlers;
4297:   MatSolverTypeHolder         next;
4298: };

4300: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4302: /*@C
4303:    MatSolvePackageRegister - Registers a MatSolverType that works for a particular matrix type

4305:    Input Parameters:
4306: +    package - name of the package, for example petsc or superlu
4307: .    mtype - the matrix type that works with this package
4308: .    ftype - the type of factorization supported by the package
4309: -    getfactor - routine that will create the factored matrix ready to be used

4311:     Level: intermediate

4313: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4314: @*/
4315: PetscErrorCode MatSolverTypeRegister(MatSolverType package,MatType mtype,MatFactorType ftype,PetscErrorCode (*getfactor)(Mat,MatFactorType,Mat*))
4316: {
4317:   PetscErrorCode              ierr;
4318:   MatSolverTypeHolder         next = MatSolverTypeHolders,prev = NULL;
4319:   PetscBool                   flg;
4320:   MatSolverTypeForSpecifcType inext,iprev = NULL;

4323:   MatInitializePackage();
4324:   if (!next) {
4325:     PetscNew(&MatSolverTypeHolders);
4326:     PetscStrallocpy(package,&MatSolverTypeHolders->name);
4327:     PetscNew(&MatSolverTypeHolders->handlers);
4328:     PetscStrallocpy(mtype,(char **)&MatSolverTypeHolders->handlers->mtype);
4329:     MatSolverTypeHolders->handlers->getfactor[(int)ftype-1] = getfactor;
4330:     return(0);
4331:   }
4332:   while (next) {
4333:     PetscStrcasecmp(package,next->name,&flg);
4334:     if (flg) {
4335:       if (!next->handlers) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MatSolverTypeHolder is missing handlers");
4336:       inext = next->handlers;
4337:       while (inext) {
4338:         PetscStrcasecmp(mtype,inext->mtype,&flg);
4339:         if (flg) {
4340:           inext->getfactor[(int)ftype-1] = getfactor;
4341:           return(0);
4342:         }
4343:         iprev = inext;
4344:         inext = inext->next;
4345:       }
4346:       PetscNew(&iprev->next);
4347:       PetscStrallocpy(mtype,(char **)&iprev->next->mtype);
4348:       iprev->next->getfactor[(int)ftype-1] = getfactor;
4349:       return(0);
4350:     }
4351:     prev = next;
4352:     next = next->next;
4353:   }
4354:   PetscNew(&prev->next);
4355:   PetscStrallocpy(package,&prev->next->name);
4356:   PetscNew(&prev->next->handlers);
4357:   PetscStrallocpy(mtype,(char **)&prev->next->handlers->mtype);
4358:   prev->next->handlers->getfactor[(int)ftype-1] = getfactor;
4359:   return(0);
4360: }

4362: /*@C
4363:    MatSolvePackageGet - Get's the function that creates the factor matrix if it exist

4365:    Input Parameters:
4366: +    package - name of the package, for example petsc or superlu
4367: .    ftype - the type of factorization supported by the package
4368: -    mtype - the matrix type that works with this package

4370:    Output Parameters:
4371: +   foundpackage - PETSC_TRUE if the package was registered
4372: .   foundmtype - PETSC_TRUE if the package supports the requested mtype
4373: -   getfactor - routine that will create the factored matrix ready to be used or NULL if not found

4375:     Level: intermediate

4377: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4378: @*/
4379: PetscErrorCode MatSolverTypeGet(MatSolverType package,MatType mtype,MatFactorType ftype,PetscBool *foundpackage,PetscBool *foundmtype,PetscErrorCode (**getfactor)(Mat,MatFactorType,Mat*))
4380: {
4381:   PetscErrorCode              ierr;
4382:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4383:   PetscBool                   flg;
4384:   MatSolverTypeForSpecifcType inext;

4387:   if (foundpackage) *foundpackage = PETSC_FALSE;
4388:   if (foundmtype)   *foundmtype   = PETSC_FALSE;
4389:   if (getfactor)    *getfactor    = NULL;

4391:   if (package) {
4392:     while (next) {
4393:       PetscStrcasecmp(package,next->name,&flg);
4394:       if (flg) {
4395:         if (foundpackage) *foundpackage = PETSC_TRUE;
4396:         inext = next->handlers;
4397:         while (inext) {
4398:           PetscStrbeginswith(mtype,inext->mtype,&flg);
4399:           if (flg) {
4400:             if (foundmtype) *foundmtype = PETSC_TRUE;
4401:             if (getfactor)  *getfactor  = inext->getfactor[(int)ftype-1];
4402:             return(0);
4403:           }
4404:           inext = inext->next;
4405:         }
4406:       }
4407:       next = next->next;
4408:     }
4409:   } else {
4410:     while (next) {
4411:       inext = next->handlers;
4412:       while (inext) {
4413:         PetscStrbeginswith(mtype,inext->mtype,&flg);
4414:         if (flg && inext->getfactor[(int)ftype-1]) {
4415:           if (foundpackage) *foundpackage = PETSC_TRUE;
4416:           if (foundmtype)   *foundmtype   = PETSC_TRUE;
4417:           if (getfactor)    *getfactor    = inext->getfactor[(int)ftype-1];
4418:           return(0);
4419:         }
4420:         inext = inext->next;
4421:       }
4422:       next = next->next;
4423:     }
4424:   }
4425:   return(0);
4426: }

4428: PetscErrorCode MatSolverTypeDestroy(void)
4429: {
4430:   PetscErrorCode              ierr;
4431:   MatSolverTypeHolder         next = MatSolverTypeHolders,prev;
4432:   MatSolverTypeForSpecifcType inext,iprev;

4435:   while (next) {
4436:     PetscFree(next->name);
4437:     inext = next->handlers;
4438:     while (inext) {
4439:       PetscFree(inext->mtype);
4440:       iprev = inext;
4441:       inext = inext->next;
4442:       PetscFree(iprev);
4443:     }
4444:     prev = next;
4445:     next = next->next;
4446:     PetscFree(prev);
4447:   }
4448:   MatSolverTypeHolders = NULL;
4449:   return(0);
4450: }

4452: /*@C
4453:    MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4455:    Collective on Mat

4457:    Input Parameters:
4458: +  mat - the matrix
4459: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4460: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4462:    Output Parameters:
4463: .  f - the factor matrix used with MatXXFactorSymbolic() calls

4465:    Notes:
4466:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4467:      such as pastix, superlu, mumps etc.

4469:       PETSc must have been ./configure to use the external solver, using the option --download-package

4471:    Level: intermediate

4473: .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable()
4474: @*/
4475: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type,MatFactorType ftype,Mat *f)
4476: {
4477:   PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*);
4478:   PetscBool      foundpackage,foundmtype;


4484:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4485:   MatCheckPreallocated(mat,1);

4487:   MatSolverTypeGet(type,((PetscObject)mat)->type_name,ftype,&foundpackage,&foundmtype,&conv);
4488:   if (!foundpackage) {
4489:     if (type) {
4490:       SETERRQ4(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate solver package %s for factorization type %s and matrix type %s. Perhaps you must ./configure with --download-%s",type,MatFactorTypes[ftype],((PetscObject)mat)->type_name,type);
4491:     } else {
4492:       SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"Could not locate a solver package for factorization type %s and matrix type %s.",MatFactorTypes[ftype],((PetscObject)mat)->type_name);
4493:     }
4494:   }
4495:   if (!foundmtype) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_MISSING_FACTOR,"MatSolverType %s does not support matrix type %s",type,((PetscObject)mat)->type_name);
4496:   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);

4498:   (*conv)(mat,ftype,f);
4499:   return(0);
4500: }

4502: /*@C
4503:    MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type

4505:    Not Collective

4507:    Input Parameters:
4508: +  mat - the matrix
4509: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4510: -  ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU,

4512:    Output Parameter:
4513: .    flg - PETSC_TRUE if the factorization is available

4515:    Notes:
4516:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4517:      such as pastix, superlu, mumps etc.

4519:       PETSc must have been ./configure to use the external solver, using the option --download-package

4521:    Level: intermediate

4523: .seealso: MatCopy(), MatDuplicate(), MatGetFactor()
4524: @*/
4525: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type,MatFactorType ftype,PetscBool  *flg)
4526: {
4527:   PetscErrorCode ierr, (*gconv)(Mat,MatFactorType,Mat*);


4533:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4534:   MatCheckPreallocated(mat,1);

4536:   *flg = PETSC_FALSE;
4537:   MatSolverTypeGet(type,((PetscObject)mat)->type_name,ftype,NULL,NULL,&gconv);
4538:   if (gconv) {
4539:     *flg = PETSC_TRUE;
4540:   }
4541:   return(0);
4542: }

4544:  #include <petscdmtypes.h>

4546: /*@
4547:    MatDuplicate - Duplicates a matrix including the non-zero structure.

4549:    Collective on Mat

4551:    Input Parameters:
4552: +  mat - the matrix
4553: -  op - One of MAT_DO_NOT_COPY_VALUES, MAT_COPY_VALUES, or MAT_SHARE_NONZERO_PATTERN.
4554:         See the manual page for MatDuplicateOption for an explanation of these options.

4556:    Output Parameter:
4557: .  M - pointer to place new matrix

4559:    Level: intermediate

4561:    Notes:
4562:     You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN.
4563:     When original mat is a product of matrix operation, e.g., an output of MatMatMult() or MatCreateSubMatrix(), only the simple matrix data structure of mat is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated. User should not use MatDuplicate() to create new matrix M if M is intended to be reused as the product of matrix operation.

4565: .seealso: MatCopy(), MatConvert(), MatDuplicateOption
4566: @*/
4567: PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M)
4568: {
4570:   Mat            B;
4571:   PetscInt       i;
4572:   DM             dm;
4573:   void           (*viewf)(void);

4579:   if (op == MAT_COPY_VALUES && !mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"MAT_COPY_VALUES not allowed for unassembled matrix");
4580:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4581:   MatCheckPreallocated(mat,1);

4583:   *M = 0;
4584:   if (!mat->ops->duplicate) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not written for matrix type %s\n",((PetscObject)mat)->type_name);
4585:   PetscLogEventBegin(MAT_Convert,mat,0,0,0);
4586:   (*mat->ops->duplicate)(mat,op,M);
4587:   B    = *M;

4589:   MatGetOperation(mat,MATOP_VIEW,&viewf);
4590:   if (viewf) {
4591:     MatSetOperation(B,MATOP_VIEW,viewf);
4592:   }

4594:   B->stencil.dim = mat->stencil.dim;
4595:   B->stencil.noc = mat->stencil.noc;
4596:   for (i=0; i<=mat->stencil.dim; i++) {
4597:     B->stencil.dims[i]   = mat->stencil.dims[i];
4598:     B->stencil.starts[i] = mat->stencil.starts[i];
4599:   }

4601:   B->nooffproczerorows = mat->nooffproczerorows;
4602:   B->nooffprocentries  = mat->nooffprocentries;

4604:   PetscObjectQuery((PetscObject) mat, "__PETSc_dm", (PetscObject*) &dm);
4605:   if (dm) {
4606:     PetscObjectCompose((PetscObject) B, "__PETSc_dm", (PetscObject) dm);
4607:   }
4608:   PetscLogEventEnd(MAT_Convert,mat,0,0,0);
4609:   PetscObjectStateIncrease((PetscObject)B);
4610:   return(0);
4611: }

4613: /*@
4614:    MatGetDiagonal - Gets the diagonal of a matrix.

4616:    Logically Collective on Mat

4618:    Input Parameters:
4619: +  mat - the matrix
4620: -  v - the vector for storing the diagonal

4622:    Output Parameter:
4623: .  v - the diagonal of the matrix

4625:    Level: intermediate

4627:    Note:
4628:    Currently only correct in parallel for square matrices.

4630: .seealso: MatGetRow(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs()
4631: @*/
4632: PetscErrorCode MatGetDiagonal(Mat mat,Vec v)
4633: {

4640:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4641:   if (!mat->ops->getdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4642:   MatCheckPreallocated(mat,1);

4644:   (*mat->ops->getdiagonal)(mat,v);
4645:   PetscObjectStateIncrease((PetscObject)v);
4646:   return(0);
4647: }

4649: /*@C
4650:    MatGetRowMin - Gets the minimum value (of the real part) of each
4651:         row of the matrix

4653:    Logically Collective on Mat

4655:    Input Parameters:
4656: .  mat - the matrix

4658:    Output Parameter:
4659: +  v - the vector for storing the maximums
4660: -  idx - the indices of the column found for each row (optional)

4662:    Level: intermediate

4664:    Notes:
4665:     The result of this call are the same as if one converted the matrix to dense format
4666:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4668:     This code is only implemented for a couple of matrix formats.

4670: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs(),
4671:           MatGetRowMax()
4672: @*/
4673: PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[])
4674: {

4681:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4682:   if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4683:   MatCheckPreallocated(mat,1);

4685:   (*mat->ops->getrowmin)(mat,v,idx);
4686:   PetscObjectStateIncrease((PetscObject)v);
4687:   return(0);
4688: }

4690: /*@C
4691:    MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4692:         row of the matrix

4694:    Logically Collective on Mat

4696:    Input Parameters:
4697: .  mat - the matrix

4699:    Output Parameter:
4700: +  v - the vector for storing the minimums
4701: -  idx - the indices of the column found for each row (or NULL if not needed)

4703:    Level: intermediate

4705:    Notes:
4706:     if a row is completely empty or has only 0.0 values then the idx[] value for that
4707:     row is 0 (the first column).

4709:     This code is only implemented for a couple of matrix formats.

4711: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin()
4712: @*/
4713: PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[])
4714: {

4721:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4722:   if (!mat->ops->getrowminabs) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4723:   MatCheckPreallocated(mat,1);
4724:   if (idx) {PetscArrayzero(idx,mat->rmap->n);}

4726:   (*mat->ops->getrowminabs)(mat,v,idx);
4727:   PetscObjectStateIncrease((PetscObject)v);
4728:   return(0);
4729: }

4731: /*@C
4732:    MatGetRowMax - Gets the maximum value (of the real part) of each
4733:         row of the matrix

4735:    Logically Collective on Mat

4737:    Input Parameters:
4738: .  mat - the matrix

4740:    Output Parameter:
4741: +  v - the vector for storing the maximums
4742: -  idx - the indices of the column found for each row (optional)

4744:    Level: intermediate

4746:    Notes:
4747:     The result of this call are the same as if one converted the matrix to dense format
4748:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4750:     This code is only implemented for a couple of matrix formats.

4752: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMaxAbs(), MatGetRowMin()
4753: @*/
4754: PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[])
4755: {

4762:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4763:   if (!mat->ops->getrowmax) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4764:   MatCheckPreallocated(mat,1);

4766:   (*mat->ops->getrowmax)(mat,v,idx);
4767:   PetscObjectStateIncrease((PetscObject)v);
4768:   return(0);
4769: }

4771: /*@C
4772:    MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
4773:         row of the matrix

4775:    Logically Collective on Mat

4777:    Input Parameters:
4778: .  mat - the matrix

4780:    Output Parameter:
4781: +  v - the vector for storing the maximums
4782: -  idx - the indices of the column found for each row (or NULL if not needed)

4784:    Level: intermediate

4786:    Notes:
4787:     if a row is completely empty or has only 0.0 values then the idx[] value for that
4788:     row is 0 (the first column).

4790:     This code is only implemented for a couple of matrix formats.

4792: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMin()
4793: @*/
4794: PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[])
4795: {

4802:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4803:   if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4804:   MatCheckPreallocated(mat,1);
4805:   if (idx) {PetscArrayzero(idx,mat->rmap->n);}

4807:   (*mat->ops->getrowmaxabs)(mat,v,idx);
4808:   PetscObjectStateIncrease((PetscObject)v);
4809:   return(0);
4810: }

4812: /*@
4813:    MatGetRowSum - Gets the sum of each row of the matrix

4815:    Logically or Neighborhood Collective on Mat

4817:    Input Parameters:
4818: .  mat - the matrix

4820:    Output Parameter:
4821: .  v - the vector for storing the sum of rows

4823:    Level: intermediate

4825:    Notes:
4826:     This code is slow since it is not currently specialized for different formats

4828: .seealso: MatGetDiagonal(), MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRowMax(), MatGetRowMin()
4829: @*/
4830: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
4831: {
4832:   Vec            ones;

4839:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4840:   MatCheckPreallocated(mat,1);
4841:   MatCreateVecs(mat,&ones,NULL);
4842:   VecSet(ones,1.);
4843:   MatMult(mat,ones,v);
4844:   VecDestroy(&ones);
4845:   return(0);
4846: }

4848: /*@
4849:    MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

4851:    Collective on Mat

4853:    Input Parameter:
4854: +  mat - the matrix to transpose
4855: -  reuse - either MAT_INITIAL_MATRIX, MAT_REUSE_MATRIX, or MAT_INPLACE_MATRIX

4857:    Output Parameters:
4858: .  B - the transpose

4860:    Notes:
4861:      If you use MAT_INPLACE_MATRIX then you must pass in &mat for B

4863:      MAT_REUSE_MATRIX causes the B matrix from a previous call to this function with MAT_INITIAL_MATRIX to be used

4865:      Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

4867:    Level: intermediate

4869: .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4870: @*/
4871: PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B)
4872: {

4878:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
4879:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
4880:   if (!mat->ops->transpose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
4881:   if (reuse == MAT_INPLACE_MATRIX && mat != *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_INPLACE_MATRIX requires last matrix to match first");
4882:   if (reuse == MAT_REUSE_MATRIX && mat == *B) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Perhaps you mean MAT_INPLACE_MATRIX");
4883:   MatCheckPreallocated(mat,1);

4885:   PetscLogEventBegin(MAT_Transpose,mat,0,0,0);
4886:   (*mat->ops->transpose)(mat,reuse,B);
4887:   PetscLogEventEnd(MAT_Transpose,mat,0,0,0);
4888:   if (B) {PetscObjectStateIncrease((PetscObject)*B);}
4889:   return(0);
4890: }

4892: /*@
4893:    MatIsTranspose - Test whether a matrix is another one's transpose,
4894:         or its own, in which case it tests symmetry.

4896:    Collective on Mat

4898:    Input Parameter:
4899: +  A - the matrix to test
4900: -  B - the matrix to test against, this can equal the first parameter

4902:    Output Parameters:
4903: .  flg - the result

4905:    Notes:
4906:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4907:    has a running time of the order of the number of nonzeros; the parallel
4908:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4910:    Level: intermediate

4912: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian()
4913: @*/
4914: PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4915: {
4916:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4922:   PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",&f);
4923:   PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",&g);
4924:   *flg = PETSC_FALSE;
4925:   if (f && g) {
4926:     if (f == g) {
4927:       (*f)(A,B,tol,flg);
4928:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test");
4929:   } else {
4930:     MatType mattype;
4931:     if (!f) {
4932:       MatGetType(A,&mattype);
4933:     } else {
4934:       MatGetType(B,&mattype);
4935:     }
4936:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type %s does not support checking for transpose",mattype);
4937:   }
4938:   return(0);
4939: }

4941: /*@
4942:    MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate.

4944:    Collective on Mat

4946:    Input Parameter:
4947: +  mat - the matrix to transpose and complex conjugate
4948: -  reuse - MAT_INITIAL_MATRIX to create a new matrix, MAT_INPLACE_MATRIX to reuse the first argument to store the transpose

4950:    Output Parameters:
4951: .  B - the Hermitian

4953:    Level: intermediate

4955: .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse
4956: @*/
4957: PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B)
4958: {

4962:   MatTranspose(mat,reuse,B);
4963: #if defined(PETSC_USE_COMPLEX)
4964:   MatConjugate(*B);
4965: #endif
4966:   return(0);
4967: }

4969: /*@
4970:    MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

4972:    Collective on Mat

4974:    Input Parameter:
4975: +  A - the matrix to test
4976: -  B - the matrix to test against, this can equal the first parameter

4978:    Output Parameters:
4979: .  flg - the result

4981:    Notes:
4982:    Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm
4983:    has a running time of the order of the number of nonzeros; the parallel
4984:    test involves parallel copies of the block-offdiagonal parts of the matrix.

4986:    Level: intermediate

4988: .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose()
4989: @*/
4990: PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool  *flg)
4991: {
4992:   PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*);

4998:   PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",&f);
4999:   PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",&g);
5000:   if (f && g) {
5001:     if (f==g) {
5002:       (*f)(A,B,tol,flg);
5003:     } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test");
5004:   }
5005:   return(0);
5006: }

5008: /*@
5009:    MatPermute - Creates a new matrix with rows and columns permuted from the
5010:    original.

5012:    Collective on Mat

5014:    Input Parameters:
5015: +  mat - the matrix to permute
5016: .  row - row permutation, each processor supplies only the permutation for its rows
5017: -  col - column permutation, each processor supplies only the permutation for its columns

5019:    Output Parameters:
5020: .  B - the permuted matrix

5022:    Level: advanced

5024:    Note:
5025:    The index sets map from row/col of permuted matrix to row/col of original matrix.
5026:    The index sets should be on the same communicator as Mat and have the same local sizes.

5028: .seealso: MatGetOrdering(), ISAllGather()

5030: @*/
5031: PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B)
5032: {

5041:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5042:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5043:   if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name);
5044:   MatCheckPreallocated(mat,1);

5046:   (*mat->ops->permute)(mat,row,col,B);
5047:   PetscObjectStateIncrease((PetscObject)*B);
5048:   return(0);
5049: }

5051: /*@
5052:    MatEqual - Compares two matrices.

5054:    Collective on Mat

5056:    Input Parameters:
5057: +  A - the first matrix
5058: -  B - the second matrix

5060:    Output Parameter:
5061: .  flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise.

5063:    Level: intermediate

5065: @*/
5066: PetscErrorCode MatEqual(Mat A,Mat B,PetscBool  *flg)
5067: {

5077:   MatCheckPreallocated(B,2);
5078:   if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5079:   if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5080:   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);
5081:   if (!A->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name);
5082:   if (!B->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name);
5083:   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);
5084:   MatCheckPreallocated(A,1);

5086:   (*A->ops->equal)(A,B,flg);
5087:   return(0);
5088: }

5090: /*@
5091:    MatDiagonalScale - Scales a matrix on the left and right by diagonal
5092:    matrices that are stored as vectors.  Either of the two scaling
5093:    matrices can be NULL.

5095:    Collective on Mat

5097:    Input Parameters:
5098: +  mat - the matrix to be scaled
5099: .  l - the left scaling vector (or NULL)
5100: -  r - the right scaling vector (or NULL)

5102:    Notes:
5103:    MatDiagonalScale() computes A = LAR, where
5104:    L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5105:    The L scales the rows of the matrix, the R scales the columns of the matrix.

5107:    Level: intermediate


5110: .seealso: MatScale(), MatShift(), MatDiagonalSet()
5111: @*/
5112: PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r)
5113: {

5119:   if (!mat->ops->diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5122:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5123:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5124:   MatCheckPreallocated(mat,1);

5126:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
5127:   (*mat->ops->diagonalscale)(mat,l,r);
5128:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
5129:   PetscObjectStateIncrease((PetscObject)mat);
5130:   return(0);
5131: }

5133: /*@
5134:     MatScale - Scales all elements of a matrix by a given number.

5136:     Logically Collective on Mat

5138:     Input Parameters:
5139: +   mat - the matrix to be scaled
5140: -   a  - the scaling value

5142:     Output Parameter:
5143: .   mat - the scaled matrix

5145:     Level: intermediate

5147: .seealso: MatDiagonalScale()
5148: @*/
5149: PetscErrorCode MatScale(Mat mat,PetscScalar a)
5150: {

5156:   if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5157:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5158:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5160:   MatCheckPreallocated(mat,1);

5162:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
5163:   if (a != (PetscScalar)1.0) {
5164:     (*mat->ops->scale)(mat,a);
5165:     PetscObjectStateIncrease((PetscObject)mat);
5166:   }
5167:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
5168:   return(0);
5169: }

5171: /*@
5172:    MatNorm - Calculates various norms of a matrix.

5174:    Collective on Mat

5176:    Input Parameters:
5177: +  mat - the matrix
5178: -  type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY

5180:    Output Parameters:
5181: .  nrm - the resulting norm

5183:    Level: intermediate

5185: @*/
5186: PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm)
5187: {


5195:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5196:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5197:   if (!mat->ops->norm) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5198:   MatCheckPreallocated(mat,1);

5200:   (*mat->ops->norm)(mat,type,nrm);
5201:   return(0);
5202: }

5204: /*
5205:      This variable is used to prevent counting of MatAssemblyBegin() that
5206:    are called from within a MatAssemblyEnd().
5207: */
5208: static PetscInt MatAssemblyEnd_InUse = 0;
5209: /*@
5210:    MatAssemblyBegin - Begins assembling the matrix.  This routine should
5211:    be called after completing all calls to MatSetValues().

5213:    Collective on Mat

5215:    Input Parameters:
5216: +  mat - the matrix
5217: -  type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY

5219:    Notes:
5220:    MatSetValues() generally caches the values.  The matrix is ready to
5221:    use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
5222:    Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES
5223:    in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before
5224:    using the matrix.

5226:    ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the
5227:    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
5228:    a global collective operation requring all processes that share the matrix.

5230:    Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed
5231:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5232:    before MAT_FINAL_ASSEMBLY so the space is not compressed out.

5234:    Level: beginner

5236: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled()
5237: @*/
5238: PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type)
5239: {

5245:   MatCheckPreallocated(mat,1);
5246:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5247:   if (mat->assembled) {
5248:     mat->was_assembled = PETSC_TRUE;
5249:     mat->assembled     = PETSC_FALSE;
5250:   }

5252:   if (!MatAssemblyEnd_InUse) {
5253:     PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);
5254:     if (mat->ops->assemblybegin) {(*mat->ops->assemblybegin)(mat,type);}
5255:     PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);
5256:   } else if (mat->ops->assemblybegin) {
5257:     (*mat->ops->assemblybegin)(mat,type);
5258:   }
5259:   return(0);
5260: }

5262: /*@
5263:    MatAssembled - Indicates if a matrix has been assembled and is ready for
5264:      use; for example, in matrix-vector product.

5266:    Not Collective

5268:    Input Parameter:
5269: .  mat - the matrix

5271:    Output Parameter:
5272: .  assembled - PETSC_TRUE or PETSC_FALSE

5274:    Level: advanced

5276: .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin()
5277: @*/
5278: PetscErrorCode MatAssembled(Mat mat,PetscBool  *assembled)
5279: {
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->num_ass++;
5350:     mat->assembled        = PETSC_TRUE;
5351:     mat->ass_nonzerostate = mat->nonzerostate;
5352:   }

5354:   mat->insertmode = NOT_SET_VALUES;
5355:   MatAssemblyEnd_InUse--;
5356:   PetscObjectStateIncrease((PetscObject)mat);
5357:   if (!mat->symmetric_eternal) {
5358:     mat->symmetric_set              = PETSC_FALSE;
5359:     mat->hermitian_set              = PETSC_FALSE;
5360:     mat->structurally_symmetric_set = PETSC_FALSE;
5361:   }
5362:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5363:     MatViewFromOptions(mat,NULL,"-mat_view");

5365:     if (mat->checksymmetryonassembly) {
5366:       MatIsSymmetric(mat,mat->checksymmetrytol,&flg);
5367:       if (flg) {
5368:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5369:       } else {
5370:         PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is not symmetric (tolerance %g)\n",(double)mat->checksymmetrytol);
5371:       }
5372:     }
5373:     if (mat->nullsp && mat->checknullspaceonassembly) {
5374:       MatNullSpaceTest(mat->nullsp,mat,NULL);
5375:     }
5376:   }
5377:   inassm--;
5378:   return(0);
5379: }

5381: /*@
5382:    MatSetOption - Sets a parameter option for a matrix. Some options
5383:    may be specific to certain storage formats.  Some options
5384:    determine how values will be inserted (or added). Sorted,
5385:    row-oriented input will generally assemble the fastest. The default
5386:    is row-oriented.

5388:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5390:    Input Parameters:
5391: +  mat - the matrix
5392: .  option - the option, one of those listed below (and possibly others),
5393: -  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5395:   Options Describing Matrix Structure:
5396: +    MAT_SPD - symmetric positive definite
5397: .    MAT_SYMMETRIC - symmetric in terms of both structure and value
5398: .    MAT_HERMITIAN - transpose is the complex conjugation
5399: .    MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure
5400: -    MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag
5401:                             you set to be kept with all future use of the matrix
5402:                             including after MatAssemblyBegin/End() which could
5403:                             potentially change the symmetry structure, i.e. you
5404:                             KNOW the matrix will ALWAYS have the property you set.
5405:                             Note that setting this flag alone implies nothing about whether the matrix is symmetric/Hermitian;
5406:                             the relevant flags must be set independently.


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:    MAT_SORTED_FULL - each process provides exactly its local rows; all column indices for a given row are passed in a
5488:                      single call to MatSetValues(), preallocation is perfect, row oriented, INSERT_VALUES is used. Common
5489:                      with finite difference schemes with non-periodic boundary conditions.
5490:    Notes:
5491:     Can only be called after MatSetSizes() and MatSetType() have been set.

5493:    Level: intermediate

5495: .seealso:  MatOption, Mat

5497: @*/
5498: PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg)
5499: {

5505:   if (op > 0) {
5508:   }

5510:   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);
5511:   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()");

5513:   switch (op) {
5514:   case MAT_NO_OFF_PROC_ENTRIES:
5515:     mat->nooffprocentries = flg;
5516:     return(0);
5517:     break;
5518:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5519:     mat->assembly_subset = flg;
5520:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
5521: #if !defined(PETSC_HAVE_MPIUNI)
5522:       MatStashScatterDestroy_BTS(&mat->stash);
5523: #endif
5524:       mat->stash.first_assembly_done = PETSC_FALSE;
5525:     }
5526:     return(0);
5527:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5528:     mat->nooffproczerorows = flg;
5529:     return(0);
5530:     break;
5531:   case MAT_SPD:
5532:     mat->spd_set = PETSC_TRUE;
5533:     mat->spd     = flg;
5534:     if (flg) {
5535:       mat->symmetric                  = PETSC_TRUE;
5536:       mat->structurally_symmetric     = PETSC_TRUE;
5537:       mat->symmetric_set              = PETSC_TRUE;
5538:       mat->structurally_symmetric_set = PETSC_TRUE;
5539:     }
5540:     break;
5541:   case MAT_SYMMETRIC:
5542:     mat->symmetric = flg;
5543:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5544:     mat->symmetric_set              = PETSC_TRUE;
5545:     mat->structurally_symmetric_set = flg;
5546: #if !defined(PETSC_USE_COMPLEX)
5547:     mat->hermitian     = flg;
5548:     mat->hermitian_set = PETSC_TRUE;
5549: #endif
5550:     break;
5551:   case MAT_HERMITIAN:
5552:     mat->hermitian = flg;
5553:     if (flg) mat->structurally_symmetric = PETSC_TRUE;
5554:     mat->hermitian_set              = PETSC_TRUE;
5555:     mat->structurally_symmetric_set = flg;
5556: #if !defined(PETSC_USE_COMPLEX)
5557:     mat->symmetric     = flg;
5558:     mat->symmetric_set = PETSC_TRUE;
5559: #endif
5560:     break;
5561:   case MAT_STRUCTURALLY_SYMMETRIC:
5562:     mat->structurally_symmetric     = flg;
5563:     mat->structurally_symmetric_set = PETSC_TRUE;
5564:     break;
5565:   case MAT_SYMMETRY_ETERNAL:
5566:     mat->symmetric_eternal = flg;
5567:     break;
5568:   case MAT_STRUCTURE_ONLY:
5569:     mat->structure_only = flg;
5570:     break;
5571:   case MAT_SORTED_FULL:
5572:     mat->sortedfull = flg;
5573:     break;
5574:   default:
5575:     break;
5576:   }
5577:   if (mat->ops->setoption) {
5578:     (*mat->ops->setoption)(mat,op,flg);
5579:   }
5580:   return(0);
5581: }

5583: /*@
5584:    MatGetOption - Gets a parameter option that has been set for a matrix.

5586:    Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption

5588:    Input Parameters:
5589: +  mat - the matrix
5590: -  option - the option, this only responds to certain options, check the code for which ones

5592:    Output Parameter:
5593: .  flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE)

5595:     Notes:
5596:     Can only be called after MatSetSizes() and MatSetType() have been set.

5598:    Level: intermediate

5600: .seealso:  MatOption, MatSetOption()

5602: @*/
5603: PetscErrorCode MatGetOption(Mat mat,MatOption op,PetscBool *flg)
5604: {

5609:   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);
5610:   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()");

5612:   switch (op) {
5613:   case MAT_NO_OFF_PROC_ENTRIES:
5614:     *flg = mat->nooffprocentries;
5615:     break;
5616:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5617:     *flg = mat->nooffproczerorows;
5618:     break;
5619:   case MAT_SYMMETRIC:
5620:     *flg = mat->symmetric;
5621:     break;
5622:   case MAT_HERMITIAN:
5623:     *flg = mat->hermitian;
5624:     break;
5625:   case MAT_STRUCTURALLY_SYMMETRIC:
5626:     *flg = mat->structurally_symmetric;
5627:     break;
5628:   case MAT_SYMMETRY_ETERNAL:
5629:     *flg = mat->symmetric_eternal;
5630:     break;
5631:   case MAT_SPD:
5632:     *flg = mat->spd;
5633:     break;
5634:   default:
5635:     break;
5636:   }
5637:   return(0);
5638: }

5640: /*@
5641:    MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
5642:    this routine retains the old nonzero structure.

5644:    Logically Collective on Mat

5646:    Input Parameters:
5647: .  mat - the matrix

5649:    Level: intermediate

5651:    Notes:
5652:     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.
5653:    See the Performance chapter of the users manual for information on preallocating matrices.

5655: .seealso: MatZeroRows()
5656: @*/
5657: PetscErrorCode MatZeroEntries(Mat mat)
5658: {

5664:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5665:   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");
5666:   if (!mat->ops->zeroentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5667:   MatCheckPreallocated(mat,1);

5669:   PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);
5670:   (*mat->ops->zeroentries)(mat);
5671:   PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);
5672:   PetscObjectStateIncrease((PetscObject)mat);
5673:   return(0);
5674: }

5676: /*@
5677:    MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
5678:    of a set of rows and columns of a matrix.

5680:    Collective on Mat

5682:    Input Parameters:
5683: +  mat - the matrix
5684: .  numRows - the number of rows to remove
5685: .  rows - the global row indices
5686: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5687: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5688: -  b - optional vector of right hand side, that will be adjusted by provided solution

5690:    Notes:
5691:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5693:    The user can set a value in the diagonal entry (or for the AIJ and
5694:    row formats can optionally remove the main diagonal entry from the
5695:    nonzero structure as well, by passing 0.0 as the final argument).

5697:    For the parallel case, all processes that share the matrix (i.e.,
5698:    those in the communicator used for matrix creation) MUST call this
5699:    routine, regardless of whether any rows being zeroed are owned by
5700:    them.

5702:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5703:    list only rows local to itself).

5705:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5707:    Level: intermediate

5709: .seealso: MatZeroRowsIS(), MatZeroRows(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5710:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5711: @*/
5712: PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5713: {

5720:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5721:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5722:   if (!mat->ops->zerorowscolumns) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5723:   MatCheckPreallocated(mat,1);

5725:   (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);
5726:   MatViewFromOptions(mat,NULL,"-mat_view");
5727:   PetscObjectStateIncrease((PetscObject)mat);
5728:   return(0);
5729: }

5731: /*@
5732:    MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
5733:    of a set of rows and columns of a matrix.

5735:    Collective on Mat

5737:    Input Parameters:
5738: +  mat - the matrix
5739: .  is - the rows to zero
5740: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5741: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5742: -  b - optional vector of right hand side, that will be adjusted by provided solution

5744:    Notes:
5745:    This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

5747:    The user can set a value in the diagonal entry (or for the AIJ and
5748:    row formats can optionally remove the main diagonal entry from the
5749:    nonzero structure as well, by passing 0.0 as the final argument).

5751:    For the parallel case, all processes that share the matrix (i.e.,
5752:    those in the communicator used for matrix creation) MUST call this
5753:    routine, regardless of whether any rows being zeroed are owned by
5754:    them.

5756:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5757:    list only rows local to itself).

5759:    The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine.

5761:    Level: intermediate

5763: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5764:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRows(), MatZeroRowsColumnsStencil()
5765: @*/
5766: PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5767: {
5769:   PetscInt       numRows;
5770:   const PetscInt *rows;

5777:   ISGetLocalSize(is,&numRows);
5778:   ISGetIndices(is,&rows);
5779:   MatZeroRowsColumns(mat,numRows,rows,diag,x,b);
5780:   ISRestoreIndices(is,&rows);
5781:   return(0);
5782: }

5784: /*@
5785:    MatZeroRows - Zeros all entries (except possibly the main diagonal)
5786:    of a set of rows of a matrix.

5788:    Collective on Mat

5790:    Input Parameters:
5791: +  mat - the matrix
5792: .  numRows - the number of rows to remove
5793: .  rows - the global row indices
5794: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5795: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5796: -  b - optional vector of right hand side, that will be adjusted by provided solution

5798:    Notes:
5799:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5800:    but does not release memory.  For the dense and block diagonal
5801:    formats this does not alter the nonzero structure.

5803:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5804:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5805:    merely zeroed.

5807:    The user can set a value in the diagonal entry (or for the AIJ and
5808:    row formats can optionally remove the main diagonal entry from the
5809:    nonzero structure as well, by passing 0.0 as the final argument).

5811:    For the parallel case, all processes that share the matrix (i.e.,
5812:    those in the communicator used for matrix creation) MUST call this
5813:    routine, regardless of whether any rows being zeroed are owned by
5814:    them.

5816:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5817:    list only rows local to itself).

5819:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5820:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5822:    Level: intermediate

5824: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5825:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5826: @*/
5827: PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
5828: {

5835:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
5836:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
5837:   if (!mat->ops->zerorows) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
5838:   MatCheckPreallocated(mat,1);

5840:   (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);
5841:   MatViewFromOptions(mat,NULL,"-mat_view");
5842:   PetscObjectStateIncrease((PetscObject)mat);
5843:   return(0);
5844: }

5846: /*@
5847:    MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
5848:    of a set of rows of a matrix.

5850:    Collective on Mat

5852:    Input Parameters:
5853: +  mat - the matrix
5854: .  is - index set of rows to remove
5855: .  diag - value put in all diagonals of eliminated rows
5856: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5857: -  b - optional vector of right hand side, that will be adjusted by provided solution

5859:    Notes:
5860:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5861:    but does not release memory.  For the dense and block diagonal
5862:    formats this does not alter the nonzero structure.

5864:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5865:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5866:    merely zeroed.

5868:    The user can set a value in the diagonal entry (or for the AIJ and
5869:    row formats can optionally remove the main diagonal entry from the
5870:    nonzero structure as well, by passing 0.0 as the final argument).

5872:    For the parallel case, all processes that share the matrix (i.e.,
5873:    those in the communicator used for matrix creation) MUST call this
5874:    routine, regardless of whether any rows being zeroed are owned by
5875:    them.

5877:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5878:    list only rows local to itself).

5880:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
5881:    owns that are to be zeroed. This saves a global synchronization in the implementation.

5883:    Level: intermediate

5885: .seealso: MatZeroRows(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5886:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5887: @*/
5888: PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
5889: {
5890:   PetscInt       numRows;
5891:   const PetscInt *rows;

5898:   ISGetLocalSize(is,&numRows);
5899:   ISGetIndices(is,&rows);
5900:   MatZeroRows(mat,numRows,rows,diag,x,b);
5901:   ISRestoreIndices(is,&rows);
5902:   return(0);
5903: }

5905: /*@
5906:    MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
5907:    of a set of rows of a matrix. These rows must be local to the process.

5909:    Collective on Mat

5911:    Input Parameters:
5912: +  mat - the matrix
5913: .  numRows - the number of rows to remove
5914: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
5915: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
5916: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
5917: -  b - optional vector of right hand side, that will be adjusted by provided solution

5919:    Notes:
5920:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
5921:    but does not release memory.  For the dense and block diagonal
5922:    formats this does not alter the nonzero structure.

5924:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
5925:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
5926:    merely zeroed.

5928:    The user can set a value in the diagonal entry (or for the AIJ and
5929:    row formats can optionally remove the main diagonal entry from the
5930:    nonzero structure as well, by passing 0.0 as the final argument).

5932:    For the parallel case, all processes that share the matrix (i.e.,
5933:    those in the communicator used for matrix creation) MUST call this
5934:    routine, regardless of whether any rows being zeroed are owned by
5935:    them.

5937:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
5938:    list only rows local to itself).

5940:    The grid coordinates are across the entire grid, not just the local portion

5942:    In Fortran idxm and idxn should be declared as
5943: $     MatStencil idxm(4,m)
5944:    and the values inserted using
5945: $    idxm(MatStencil_i,1) = i
5946: $    idxm(MatStencil_j,1) = j
5947: $    idxm(MatStencil_k,1) = k
5948: $    idxm(MatStencil_c,1) = c
5949:    etc

5951:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
5952:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
5953:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
5954:    DM_BOUNDARY_PERIODIC boundary type.

5956:    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
5957:    a single value per point) you can skip filling those indices.

5959:    Level: intermediate

5961: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsl(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
5962:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
5963: @*/
5964: PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
5965: {
5966:   PetscInt       dim     = mat->stencil.dim;
5967:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
5968:   PetscInt       *dims   = mat->stencil.dims+1;
5969:   PetscInt       *starts = mat->stencil.starts;
5970:   PetscInt       *dxm    = (PetscInt*) rows;
5971:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


5979:   PetscMalloc1(numRows, &jdxm);
5980:   for (i = 0; i < numRows; ++i) {
5981:     /* Skip unused dimensions (they are ordered k, j, i, c) */
5982:     for (j = 0; j < 3-sdim; ++j) dxm++;
5983:     /* Local index in X dir */
5984:     tmp = *dxm++ - starts[0];
5985:     /* Loop over remaining dimensions */
5986:     for (j = 0; j < dim-1; ++j) {
5987:       /* If nonlocal, set index to be negative */
5988:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
5989:       /* Update local index */
5990:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
5991:     }
5992:     /* Skip component slot if necessary */
5993:     if (mat->stencil.noc) dxm++;
5994:     /* Local row number */
5995:     if (tmp >= 0) {
5996:       jdxm[numNewRows++] = tmp;
5997:     }
5998:   }
5999:   MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);
6000:   PetscFree(jdxm);
6001:   return(0);
6002: }

6004: /*@
6005:    MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6006:    of a set of rows and columns of a matrix.

6008:    Collective on Mat

6010:    Input Parameters:
6011: +  mat - the matrix
6012: .  numRows - the number of rows/columns to remove
6013: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
6014: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6015: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6016: -  b - optional vector of right hand side, that will be adjusted by provided solution

6018:    Notes:
6019:    For the AIJ and BAIJ matrix formats this removes the old nonzero structure,
6020:    but does not release memory.  For the dense and block diagonal
6021:    formats this does not alter the nonzero structure.

6023:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6024:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6025:    merely zeroed.

6027:    The user can set a value in the diagonal entry (or for the AIJ and
6028:    row formats can optionally remove the main diagonal entry from the
6029:    nonzero structure as well, by passing 0.0 as the final argument).

6031:    For the parallel case, all processes that share the matrix (i.e.,
6032:    those in the communicator used for matrix creation) MUST call this
6033:    routine, regardless of whether any rows being zeroed are owned by
6034:    them.

6036:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6037:    list only rows local to itself, but the row/column numbers are given in local numbering).

6039:    The grid coordinates are across the entire grid, not just the local portion

6041:    In Fortran idxm and idxn should be declared as
6042: $     MatStencil idxm(4,m)
6043:    and the values inserted using
6044: $    idxm(MatStencil_i,1) = i
6045: $    idxm(MatStencil_j,1) = j
6046: $    idxm(MatStencil_k,1) = k
6047: $    idxm(MatStencil_c,1) = c
6048:    etc

6050:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6051:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6052:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6053:    DM_BOUNDARY_PERIODIC boundary type.

6055:    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
6056:    a single value per point) you can skip filling those indices.

6058:    Level: intermediate

6060: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6061:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRows()
6062: @*/
6063: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b)
6064: {
6065:   PetscInt       dim     = mat->stencil.dim;
6066:   PetscInt       sdim    = dim - (1 - (PetscInt) mat->stencil.noc);
6067:   PetscInt       *dims   = mat->stencil.dims+1;
6068:   PetscInt       *starts = mat->stencil.starts;
6069:   PetscInt       *dxm    = (PetscInt*) rows;
6070:   PetscInt       *jdxm, i, j, tmp, numNewRows = 0;


6078:   PetscMalloc1(numRows, &jdxm);
6079:   for (i = 0; i < numRows; ++i) {
6080:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6081:     for (j = 0; j < 3-sdim; ++j) dxm++;
6082:     /* Local index in X dir */
6083:     tmp = *dxm++ - starts[0];
6084:     /* Loop over remaining dimensions */
6085:     for (j = 0; j < dim-1; ++j) {
6086:       /* If nonlocal, set index to be negative */
6087:       if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6088:       /* Update local index */
6089:       else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1];
6090:     }
6091:     /* Skip component slot if necessary */
6092:     if (mat->stencil.noc) dxm++;
6093:     /* Local row number */
6094:     if (tmp >= 0) {
6095:       jdxm[numNewRows++] = tmp;
6096:     }
6097:   }
6098:   MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);
6099:   PetscFree(jdxm);
6100:   return(0);
6101: }

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

6107:    Collective on Mat

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

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

6121:    For the AIJ matrix formats this removes the old nonzero structure,
6122:    but does not release memory.  For the dense and block diagonal
6123:    formats this does not alter the nonzero structure.

6125:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6126:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6127:    merely zeroed.

6129:    The user can set a value in the diagonal entry (or for the AIJ and
6130:    row formats can optionally remove the main diagonal entry from the
6131:    nonzero structure as well, by passing 0.0 as the final argument).

6133:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6134:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6136:    Level: intermediate

6138: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRows(), MatSetOption(),
6139:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6140: @*/
6141: PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6142: {

6149:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6150:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6151:   MatCheckPreallocated(mat,1);

6153:   if (mat->ops->zerorowslocal) {
6154:     (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);
6155:   } else {
6156:     IS             is, newis;
6157:     const PetscInt *newRows;

6159:     if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6160:     ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6161:     ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);
6162:     ISGetIndices(newis,&newRows);
6163:     (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);
6164:     ISRestoreIndices(newis,&newRows);
6165:     ISDestroy(&newis);
6166:     ISDestroy(&is);
6167:   }
6168:   PetscObjectStateIncrease((PetscObject)mat);
6169:   return(0);
6170: }

6172: /*@
6173:    MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6174:    of a set of rows of a matrix; using local numbering of rows.

6176:    Collective on Mat

6178:    Input Parameters:
6179: +  mat - the matrix
6180: .  is - index set of rows to remove
6181: .  diag - value put in all diagonals of eliminated rows
6182: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6183: -  b - optional vector of right hand side, that will be adjusted by provided solution

6185:    Notes:
6186:    Before calling MatZeroRowsLocalIS(), the user must first set the
6187:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6189:    For the AIJ matrix formats this removes the old nonzero structure,
6190:    but does not release memory.  For the dense and block diagonal
6191:    formats this does not alter the nonzero structure.

6193:    If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure
6194:    of the matrix is not changed (even for AIJ and BAIJ matrices) the values are
6195:    merely zeroed.

6197:    The user can set a value in the diagonal entry (or for the AIJ and
6198:    row formats can optionally remove the main diagonal entry from the
6199:    nonzero structure as well, by passing 0.0 as the final argument).

6201:    You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it
6202:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6204:    Level: intermediate

6206: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6207:           MatZeroRowsColumnsLocal(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6208: @*/
6209: PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6210: {
6212:   PetscInt       numRows;
6213:   const PetscInt *rows;

6219:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6220:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6221:   MatCheckPreallocated(mat,1);

6223:   ISGetLocalSize(is,&numRows);
6224:   ISGetIndices(is,&rows);
6225:   MatZeroRowsLocal(mat,numRows,rows,diag,x,b);
6226:   ISRestoreIndices(is,&rows);
6227:   return(0);
6228: }

6230: /*@
6231:    MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6232:    of a set of rows and columns of a matrix; using local numbering of rows.

6234:    Collective on Mat

6236:    Input Parameters:
6237: +  mat - the matrix
6238: .  numRows - the number of rows to remove
6239: .  rows - the global row indices
6240: .  diag - value put in all diagonals of eliminated rows
6241: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6242: -  b - optional vector of right hand side, that will be adjusted by provided solution

6244:    Notes:
6245:    Before calling MatZeroRowsColumnsLocal(), the user must first set the
6246:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6248:    The user can set a value in the diagonal entry (or for the AIJ and
6249:    row formats can optionally remove the main diagonal entry from the
6250:    nonzero structure as well, by passing 0.0 as the final argument).

6252:    Level: intermediate

6254: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6255:           MatZeroRows(), MatZeroRowsColumnsLocalIS(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6256: @*/
6257: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
6258: {
6260:   IS             is, newis;
6261:   const PetscInt *newRows;

6267:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6268:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6269:   MatCheckPreallocated(mat,1);

6271:   if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first");
6272:   ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);
6273:   ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);
6274:   ISGetIndices(newis,&newRows);
6275:   (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);
6276:   ISRestoreIndices(newis,&newRows);
6277:   ISDestroy(&newis);
6278:   ISDestroy(&is);
6279:   PetscObjectStateIncrease((PetscObject)mat);
6280:   return(0);
6281: }

6283: /*@
6284:    MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6285:    of a set of rows and columns of a matrix; using local numbering of rows.

6287:    Collective on Mat

6289:    Input Parameters:
6290: +  mat - the matrix
6291: .  is - index set of rows to remove
6292: .  diag - value put in all diagonals of eliminated rows
6293: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6294: -  b - optional vector of right hand side, that will be adjusted by provided solution

6296:    Notes:
6297:    Before calling MatZeroRowsColumnsLocalIS(), the user must first set the
6298:    local-to-global mapping by calling MatSetLocalToGlobalMapping().

6300:    The user can set a value in the diagonal entry (or for the AIJ and
6301:    row formats can optionally remove the main diagonal entry from the
6302:    nonzero structure as well, by passing 0.0 as the final argument).

6304:    Level: intermediate

6306: .seealso: MatZeroRowsIS(), MatZeroRowsColumns(), MatZeroRowsLocalIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(),
6307:           MatZeroRowsColumnsLocal(), MatZeroRows(), MatZeroRowsColumnsIS(), MatZeroRowsColumnsStencil()
6308: @*/
6309: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b)
6310: {
6312:   PetscInt       numRows;
6313:   const PetscInt *rows;

6319:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6320:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6321:   MatCheckPreallocated(mat,1);

6323:   ISGetLocalSize(is,&numRows);
6324:   ISGetIndices(is,&rows);
6325:   MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);
6326:   ISRestoreIndices(is,&rows);
6327:   return(0);
6328: }

6330: /*@C
6331:    MatGetSize - Returns the numbers of rows and columns in a matrix.

6333:    Not Collective

6335:    Input Parameter:
6336: .  mat - the matrix

6338:    Output Parameters:
6339: +  m - the number of global rows
6340: -  n - the number of global columns

6342:    Note: both output parameters can be NULL on input.

6344:    Level: beginner

6346: .seealso: MatGetLocalSize()
6347: @*/
6348: PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n)
6349: {
6352:   if (m) *m = mat->rmap->N;
6353:   if (n) *n = mat->cmap->N;
6354:   return(0);
6355: }

6357: /*@C
6358:    MatGetLocalSize - Returns the number of rows and columns in a matrix
6359:    stored locally.  This information may be implementation dependent, so
6360:    use with care.

6362:    Not Collective

6364:    Input Parameters:
6365: .  mat - the matrix

6367:    Output Parameters:
6368: +  m - the number of local rows
6369: -  n - the number of local columns

6371:    Note: both output parameters can be NULL on input.

6373:    Level: beginner

6375: .seealso: MatGetSize()
6376: @*/
6377: PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n)
6378: {
6383:   if (m) *m = mat->rmap->n;
6384:   if (n) *n = mat->cmap->n;
6385:   return(0);
6386: }

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

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

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

6397:    Output Parameters:
6398: +  m - the global index of the first local column
6399: -  n - one more than the global index of the last local column

6401:    Notes:
6402:     both output parameters can be NULL on input.

6404:    Level: developer

6406: .seealso:  MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn()

6408: @*/
6409: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n)
6410: {
6416:   MatCheckPreallocated(mat,1);
6417:   if (m) *m = mat->cmap->rstart;
6418:   if (n) *n = mat->cmap->rend;
6419:   return(0);
6420: }

6422: /*@C
6423:    MatGetOwnershipRange - Returns the range of matrix rows owned by
6424:    this processor, assuming that the matrix is laid out with the first
6425:    n1 rows on the first processor, the next n2 rows on the second, etc.
6426:    For certain parallel layouts this range may not be well defined.

6428:    Not Collective

6430:    Input Parameters:
6431: .  mat - the matrix

6433:    Output Parameters:
6434: +  m - the global index of the first local row
6435: -  n - one more than the global index of the last local row

6437:    Note: Both output parameters can be NULL on input.
6438: $  This function requires that the matrix be preallocated. If you have not preallocated, consider using
6439: $    PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N)
6440: $  and then MPI_Scan() to calculate prefix sums of the local sizes.

6442:    Level: beginner

6444: .seealso:   MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock()

6446: @*/
6447: PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n)
6448: {
6454:   MatCheckPreallocated(mat,1);
6455:   if (m) *m = mat->rmap->rstart;
6456:   if (n) *n = mat->rmap->rend;
6457:   return(0);
6458: }

6460: /*@C
6461:    MatGetOwnershipRanges - Returns the range of matrix rows owned by
6462:    each process

6464:    Not Collective, unless matrix has not been allocated, then collective on Mat

6466:    Input Parameters:
6467: .  mat - the matrix

6469:    Output Parameters:
6470: .  ranges - start of each processors portion plus one more than the total length at the end

6472:    Level: beginner

6474: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn()

6476: @*/
6477: PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges)
6478: {

6484:   MatCheckPreallocated(mat,1);
6485:   PetscLayoutGetRanges(mat->rmap,ranges);
6486:   return(0);
6487: }

6489: /*@C
6490:    MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by
6491:    this processor. (The columns of the "diagonal blocks" for each process)

6493:    Not Collective, unless matrix has not been allocated, then collective on Mat

6495:    Input Parameters:
6496: .  mat - the matrix

6498:    Output Parameters:
6499: .  ranges - start of each processors portion plus one more then the total length at the end

6501:    Level: beginner

6503: .seealso:   MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges()

6505: @*/
6506: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges)
6507: {

6513:   MatCheckPreallocated(mat,1);
6514:   PetscLayoutGetRanges(mat->cmap,ranges);
6515:   return(0);
6516: }

6518: /*@C
6519:    MatGetOwnershipIS - Get row and column ownership as index sets

6521:    Not Collective

6523:    Input Arguments:
6524: .  A - matrix of type Elemental

6526:    Output Arguments:
6527: +  rows - rows in which this process owns elements
6528: -  cols - columns in which this process owns elements

6530:    Level: intermediate

6532: .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL
6533: @*/
6534: PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols)
6535: {
6536:   PetscErrorCode ierr,(*f)(Mat,IS*,IS*);

6539:   MatCheckPreallocated(A,1);
6540:   PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",&f);
6541:   if (f) {
6542:     (*f)(A,rows,cols);
6543:   } else {   /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6544:     if (rows) {ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);}
6545:     if (cols) {ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);}
6546:   }
6547:   return(0);
6548: }

6550: /*@C
6551:    MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix.
6552:    Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric()
6553:    to complete the factorization.

6555:    Collective on Mat

6557:    Input Parameters:
6558: +  mat - the matrix
6559: .  row - row permutation
6560: .  column - column permutation
6561: -  info - structure containing
6562: $      levels - number of levels of fill.
6563: $      expected fill - as ratio of original fill.
6564: $      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6565:                 missing diagonal entries)

6567:    Output Parameters:
6568: .  fact - new matrix that has been symbolically factored

6570:    Notes:
6571:     See Users-Manual: ch_mat for additional information about choosing the fill factor for better efficiency.

6573:    Most users should employ the simplified KSP interface for linear solvers
6574:    instead of working directly with matrix algebra routines such as this.
6575:    See, e.g., KSPCreate().

6577:    Level: developer

6579: .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()
6580:           MatGetOrdering(), MatFactorInfo

6582:     Note: this uses the definition of level of fill as in Y. Saad, 2003

6584:     Developer Note: fortran interface is not autogenerated as the f90
6585:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6587:    References:
6588:      Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003
6589: @*/
6590: PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info)
6591: {

6601:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels);
6602:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6603:   if (!(fact)->ops->ilufactorsymbolic) {
6604:     MatSolverType spackage;
6605:     MatFactorGetSolverType(fact,&spackage);
6606:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage);
6607:   }
6608:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6609:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6610:   MatCheckPreallocated(mat,2);

6612:   PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);
6613:   (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);
6614:   PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);
6615:   return(0);
6616: }

6618: /*@C
6619:    MatICCFactorSymbolic - Performs symbolic incomplete
6620:    Cholesky factorization for a symmetric matrix.  Use
6621:    MatCholeskyFactorNumeric() to complete the factorization.

6623:    Collective on Mat

6625:    Input Parameters:
6626: +  mat - the matrix
6627: .  perm - row and column permutation
6628: -  info - structure containing
6629: $      levels - number of levels of fill.
6630: $      expected fill - as ratio of original fill.

6632:    Output Parameter:
6633: .  fact - the factored matrix

6635:    Notes:
6636:    Most users should employ the KSP interface for linear solvers
6637:    instead of working directly with matrix algebra routines such as this.
6638:    See, e.g., KSPCreate().

6640:    Level: developer

6642: .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo

6644:     Note: this uses the definition of level of fill as in Y. Saad, 2003

6646:     Developer Note: fortran interface is not autogenerated as the f90
6647:     interface defintion cannot be generated correctly [due to MatFactorInfo]

6649:    References:
6650:      Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003
6651: @*/
6652: PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info)
6653: {

6662:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6663:   if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels);
6664:   if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",(double)info->fill);
6665:   if (!(fact)->ops->iccfactorsymbolic) {
6666:     MatSolverType spackage;
6667:     MatFactorGetSolverType(fact,&spackage);
6668:     SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage);
6669:   }
6670:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6671:   MatCheckPreallocated(mat,2);

6673:   PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);
6674:   (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);
6675:   PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);
6676:   return(0);
6677: }

6679: /*@C
6680:    MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
6681:    points to an array of valid matrices, they may be reused to store the new
6682:    submatrices.

6684:    Collective on Mat

6686:    Input Parameters:
6687: +  mat - the matrix
6688: .  n   - the number of submatrixes to be extracted (on this processor, may be zero)
6689: .  irow, icol - index sets of rows and columns to extract
6690: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6692:    Output Parameter:
6693: .  submat - the array of submatrices

6695:    Notes:
6696:    MatCreateSubMatrices() can extract ONLY sequential submatrices
6697:    (from both sequential and parallel matrices). Use MatCreateSubMatrix()
6698:    to extract a parallel submatrix.

6700:    Some matrix types place restrictions on the row and column
6701:    indices, such as that they be sorted or that they be equal to each other.

6703:    The index sets may not have duplicate entries.

6705:    When extracting submatrices from a parallel matrix, each processor can
6706:    form a different submatrix by setting the rows and columns of its
6707:    individual index sets according to the local submatrix desired.

6709:    When finished using the submatrices, the user should destroy
6710:    them with MatDestroySubMatrices().

6712:    MAT_REUSE_MATRIX can only be used when the nonzero structure of the
6713:    original matrix has not changed from that last call to MatCreateSubMatrices().

6715:    This routine creates the matrices in submat; you should NOT create them before
6716:    calling it. It also allocates the array of matrix pointers submat.

6718:    For BAIJ matrices the index sets must respect the block structure, that is if they
6719:    request one row/column in a block, they must request all rows/columns that are in
6720:    that block. For example, if the block size is 2 you cannot request just row 0 and
6721:    column 0.

6723:    Fortran Note:
6724:    The Fortran interface is slightly different from that given below; it
6725:    requires one to pass in  as submat a Mat (integer) array of size at least n+1.

6727:    Level: advanced


6730: .seealso: MatDestroySubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6731: @*/
6732: PetscErrorCode MatCreateSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6733: {
6735:   PetscInt       i;
6736:   PetscBool      eq;

6741:   if (n) {
6746:   }
6748:   if (n && scall == MAT_REUSE_MATRIX) {
6751:   }
6752:   if (!mat->ops->createsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6753:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6754:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6755:   MatCheckPreallocated(mat,1);

6757:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6758:   (*mat->ops->createsubmatrices)(mat,n,irow,icol,scall,submat);
6759:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6760:   for (i=0; i<n; i++) {
6761:     (*submat)[i]->factortype = MAT_FACTOR_NONE;  /* in case in place factorization was previously done on submatrix */
6762:     ISEqualUnsorted(irow[i],icol[i],&eq);
6763:     if (eq) {
6764:       MatPropagateSymmetryOptions(mat,(*submat)[i]);
6765:     }
6766:   }
6767:   return(0);
6768: }

6770: /*@C
6771:    MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of IS that may live on subcomms).

6773:    Collective on Mat

6775:    Input Parameters:
6776: +  mat - the matrix
6777: .  n   - the number of submatrixes to be extracted
6778: .  irow, icol - index sets of rows and columns to extract
6779: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

6781:    Output Parameter:
6782: .  submat - the array of submatrices

6784:    Level: advanced


6787: .seealso: MatCreateSubMatrices(), MatCreateSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse
6788: @*/
6789: PetscErrorCode MatCreateSubMatricesMPI(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[])
6790: {
6792:   PetscInt       i;
6793:   PetscBool      eq;

6798:   if (n) {
6803:   }
6805:   if (n && scall == MAT_REUSE_MATRIX) {
6808:   }
6809:   if (!mat->ops->createsubmatricesmpi) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6810:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6811:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6812:   MatCheckPreallocated(mat,1);

6814:   PetscLogEventBegin(MAT_CreateSubMats,mat,0,0,0);
6815:   (*mat->ops->createsubmatricesmpi)(mat,n,irow,icol,scall,submat);
6816:   PetscLogEventEnd(MAT_CreateSubMats,mat,0,0,0);
6817:   for (i=0; i<n; i++) {
6818:     ISEqualUnsorted(irow[i],icol[i],&eq);
6819:     if (eq) {
6820:       MatPropagateSymmetryOptions(mat,(*submat)[i]);
6821:     }
6822:   }
6823:   return(0);
6824: }

6826: /*@C
6827:    MatDestroyMatrices - Destroys an array of matrices.

6829:    Collective on Mat

6831:    Input Parameters:
6832: +  n - the number of local matrices
6833: -  mat - the matrices (note that this is a pointer to the array of matrices)

6835:    Level: advanced

6837:     Notes:
6838:     Frees not only the matrices, but also the array that contains the matrices
6839:            In Fortran will not free the array.

6841: .seealso: MatCreateSubMatrices() MatDestroySubMatrices()
6842: @*/
6843: PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[])
6844: {
6846:   PetscInt       i;

6849:   if (!*mat) return(0);
6850:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6853:   for (i=0; i<n; i++) {
6854:     MatDestroy(&(*mat)[i]);
6855:   }

6857:   /* memory is allocated even if n = 0 */
6858:   PetscFree(*mat);
6859:   return(0);
6860: }

6862: /*@C
6863:    MatDestroySubMatrices - Destroys a set of matrices obtained with MatCreateSubMatrices().

6865:    Collective on Mat

6867:    Input Parameters:
6868: +  n - the number of local matrices
6869: -  mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling
6870:                        sequence of MatCreateSubMatrices())

6872:    Level: advanced

6874:     Notes:
6875:     Frees not only the matrices, but also the array that contains the matrices
6876:            In Fortran will not free the array.

6878: .seealso: MatCreateSubMatrices()
6879: @*/
6880: PetscErrorCode MatDestroySubMatrices(PetscInt n,Mat *mat[])
6881: {
6883:   Mat            mat0;

6886:   if (!*mat) return(0);
6887:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
6888:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n);

6891:   mat0 = (*mat)[0];
6892:   if (mat0 && mat0->ops->destroysubmatrices) {
6893:     (mat0->ops->destroysubmatrices)(n,mat);
6894:   } else {
6895:     MatDestroyMatrices(n,mat);
6896:   }
6897:   return(0);
6898: }

6900: /*@C
6901:    MatGetSeqNonzeroStructure - Extracts the sequential nonzero structure from a matrix.

6903:    Collective on Mat

6905:    Input Parameters:
6906: .  mat - the matrix

6908:    Output Parameter:
6909: .  matstruct - the sequential matrix with the nonzero structure of mat

6911:   Level: intermediate

6913: .seealso: MatDestroySeqNonzeroStructure(), MatCreateSubMatrices(), MatDestroyMatrices()
6914: @*/
6915: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat,Mat *matstruct)
6916: {


6924:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6925:   MatCheckPreallocated(mat,1);

6927:   if (!mat->ops->getseqnonzerostructure) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name);
6928:   PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6929:   (*mat->ops->getseqnonzerostructure)(mat,matstruct);
6930:   PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);
6931:   return(0);
6932: }

6934: /*@C
6935:    MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure().

6937:    Collective on Mat

6939:    Input Parameters:
6940: .  mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling
6941:                        sequence of MatGetSequentialNonzeroStructure())

6943:    Level: advanced

6945:     Notes:
6946:     Frees not only the matrices, but also the array that contains the matrices

6948: .seealso: MatGetSeqNonzeroStructure()
6949: @*/
6950: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
6951: {

6956:   MatDestroy(mat);
6957:   return(0);
6958: }

6960: /*@
6961:    MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
6962:    replaces the index sets by larger ones that represent submatrices with
6963:    additional overlap.

6965:    Collective on Mat

6967:    Input Parameters:
6968: +  mat - the matrix
6969: .  n   - the number of index sets
6970: .  is  - the array of index sets (these index sets will changed during the call)
6971: -  ov  - the additional overlap requested

6973:    Options Database:
6974: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

6976:    Level: developer


6979: .seealso: MatCreateSubMatrices()
6980: @*/
6981: PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov)
6982: {

6988:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
6989:   if (n) {
6992:   }
6993:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
6994:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
6995:   MatCheckPreallocated(mat,1);

6997:   if (!ov) return(0);
6998:   if (!mat->ops->increaseoverlap) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
6999:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
7000:   (*mat->ops->increaseoverlap)(mat,n,is,ov);
7001:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
7002:   return(0);
7003: }


7006: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat,IS*,PetscInt);

7008: /*@
7009:    MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7010:    a sub communicator, replaces the index sets by larger ones that represent submatrices with
7011:    additional overlap.

7013:    Collective on Mat

7015:    Input Parameters:
7016: +  mat - the matrix
7017: .  n   - the number of index sets
7018: .  is  - the array of index sets (these index sets will changed during the call)
7019: -  ov  - the additional overlap requested

7021:    Options Database:
7022: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7024:    Level: developer


7027: .seealso: MatCreateSubMatrices()
7028: @*/
7029: PetscErrorCode MatIncreaseOverlapSplit(Mat mat,PetscInt n,IS is[],PetscInt ov)
7030: {
7031:   PetscInt       i;

7037:   if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n);
7038:   if (n) {
7041:   }
7042:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
7043:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7044:   MatCheckPreallocated(mat,1);
7045:   if (!ov) return(0);
7046:   PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);
7047:   for(i=0; i<n; i++){
7048:          MatIncreaseOverlapSplit_Single(mat,&is[i],ov);
7049:   }
7050:   PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);
7051:   return(0);
7052: }




7057: /*@
7058:    MatGetBlockSize - Returns the matrix block size.

7060:    Not Collective

7062:    Input Parameter:
7063: .  mat - the matrix

7065:    Output Parameter:
7066: .  bs - block size

7068:    Notes:
7069:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.

7071:    If the block size has not been set yet this routine returns 1.

7073:    Level: intermediate

7075: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes()
7076: @*/
7077: PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs)
7078: {
7082:   *bs = PetscAbs(mat->rmap->bs);
7083:   return(0);
7084: }

7086: /*@
7087:    MatGetBlockSizes - Returns the matrix block row and column sizes.

7089:    Not Collective

7091:    Input Parameter:
7092: .  mat - the matrix

7094:    Output Parameter:
7095: +  rbs - row block size
7096: -  cbs - column block size

7098:    Notes:
7099:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7100:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7102:    If a block size has not been set yet this routine returns 1.

7104:    Level: intermediate

7106: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatSetBlockSizes()
7107: @*/
7108: PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs)
7109: {
7114:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7115:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7116:   return(0);
7117: }

7119: /*@
7120:    MatSetBlockSize - Sets the matrix block size.

7122:    Logically Collective on Mat

7124:    Input Parameters:
7125: +  mat - the matrix
7126: -  bs - block size

7128:    Notes:
7129:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7130:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7132:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block size
7133:     is compatible with the matrix local sizes.

7135:    Level: intermediate

7137: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes()
7138: @*/
7139: PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs)
7140: {

7146:   MatSetBlockSizes(mat,bs,bs);
7147:   return(0);
7148: }

7150: /*@
7151:    MatSetVariableBlockSizes - Sets a diagonal blocks of the matrix that need not be of the same size

7153:    Logically Collective on Mat

7155:    Input Parameters:
7156: +  mat - the matrix
7157: .  nblocks - the number of blocks on this process
7158: -  bsizes - the block sizes

7160:    Notes:
7161:     Currently used by PCVPBJACOBI for SeqAIJ matrices

7163:    Level: intermediate

7165: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes(), MatGetVariableBlockSizes()
7166: @*/
7167: PetscErrorCode MatSetVariableBlockSizes(Mat mat,PetscInt nblocks,PetscInt *bsizes)
7168: {
7170:   PetscInt       i,ncnt = 0, nlocal;

7174:   if (nblocks < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number of local blocks must be great than or equal to zero");
7175:   MatGetLocalSize(mat,&nlocal,NULL);
7176:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
7177:   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);
7178:   PetscFree(mat->bsizes);
7179:   mat->nblocks = nblocks;
7180:   PetscMalloc1(nblocks,&mat->bsizes);
7181:   PetscArraycpy(mat->bsizes,bsizes,nblocks);
7182:   return(0);
7183: }

7185: /*@C
7186:    MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7188:    Logically Collective on Mat

7190:    Input Parameters:
7191: .  mat - the matrix

7193:    Output Parameters:
7194: +  nblocks - the number of blocks on this process
7195: -  bsizes - the block sizes

7197:    Notes: Currently not supported from Fortran

7199:    Level: intermediate

7201: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes(), MatGetBlockSizes(), MatSetVariableBlockSizes()
7202: @*/
7203: PetscErrorCode MatGetVariableBlockSizes(Mat mat,PetscInt *nblocks,const PetscInt **bsizes)
7204: {
7207:   *nblocks = mat->nblocks;
7208:   *bsizes  = mat->bsizes;
7209:   return(0);
7210: }

7212: /*@
7213:    MatSetBlockSizes - Sets the matrix block row and column sizes.

7215:    Logically Collective on Mat

7217:    Input Parameters:
7218: +  mat - the matrix
7219: .  rbs - row block size
7220: -  cbs - column block size

7222:    Notes:
7223:     Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ. These formats ALWAYS have square block storage in the matrix.
7224:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7225:     This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7227:     For MATMPIAIJ and MATSEQAIJ matrix formats, this function can be called at a later stage, provided that the specified block sizes
7228:     are compatible with the matrix local sizes.

7230:     The row and column block size determine the blocksize of the "row" and "column" vectors returned by MatCreateVecs().

7232:    Level: intermediate

7234: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSize(), MatGetBlockSizes()
7235: @*/
7236: PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs)
7237: {

7244:   if (mat->ops->setblocksizes) {
7245:     (*mat->ops->setblocksizes)(mat,rbs,cbs);
7246:   }
7247:   if (mat->rmap->refcnt) {
7248:     ISLocalToGlobalMapping l2g = NULL;
7249:     PetscLayout            nmap = NULL;

7251:     PetscLayoutDuplicate(mat->rmap,&nmap);
7252:     if (mat->rmap->mapping) {
7253:       ISLocalToGlobalMappingDuplicate(mat->rmap->mapping,&l2g);
7254:     }
7255:     PetscLayoutDestroy(&mat->rmap);
7256:     mat->rmap = nmap;
7257:     mat->rmap->mapping = l2g;
7258:   }
7259:   if (mat->cmap->refcnt) {
7260:     ISLocalToGlobalMapping l2g = NULL;
7261:     PetscLayout            nmap = NULL;

7263:     PetscLayoutDuplicate(mat->cmap,&nmap);
7264:     if (mat->cmap->mapping) {
7265:       ISLocalToGlobalMappingDuplicate(mat->cmap->mapping,&l2g);
7266:     }
7267:     PetscLayoutDestroy(&mat->cmap);
7268:     mat->cmap = nmap;
7269:     mat->cmap->mapping = l2g;
7270:   }
7271:   PetscLayoutSetBlockSize(mat->rmap,rbs);
7272:   PetscLayoutSetBlockSize(mat->cmap,cbs);
7273:   return(0);
7274: }

7276: /*@
7277:    MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7279:    Logically Collective on Mat

7281:    Input Parameters:
7282: +  mat - the matrix
7283: .  fromRow - matrix from which to copy row block size
7284: -  fromCol - matrix from which to copy column block size (can be same as fromRow)

7286:    Level: developer

7288: .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize(), MatSetBlockSizes()
7289: @*/
7290: PetscErrorCode MatSetBlockSizesFromMats(Mat mat,Mat fromRow,Mat fromCol)
7291: {

7298:   if (fromRow->rmap->bs > 0) {PetscLayoutSetBlockSize(mat->rmap,fromRow->rmap->bs);}
7299:   if (fromCol->cmap->bs > 0) {PetscLayoutSetBlockSize(mat->cmap,fromCol->cmap->bs);}
7300:   return(0);
7301: }

7303: /*@
7304:    MatResidual - Default routine to calculate the residual.

7306:    Collective on Mat

7308:    Input Parameters:
7309: +  mat - the matrix
7310: .  b   - the right-hand-side
7311: -  x   - the approximate solution

7313:    Output Parameter:
7314: .  r - location to store the residual

7316:    Level: developer

7318: .seealso: PCMGSetResidual()
7319: @*/
7320: PetscErrorCode MatResidual(Mat mat,Vec b,Vec x,Vec r)
7321: {

7330:   MatCheckPreallocated(mat,1);
7331:   PetscLogEventBegin(MAT_Residual,mat,0,0,0);
7332:   if (!mat->ops->residual) {
7333:     MatMult(mat,x,r);
7334:     VecAYPX(r,-1.0,b);
7335:   } else {
7336:     (*mat->ops->residual)(mat,b,x,r);
7337:   }
7338:   PetscLogEventEnd(MAT_Residual,mat,0,0,0);
7339:   return(0);
7340: }

7342: /*@C
7343:     MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices.

7345:    Collective on Mat

7347:     Input Parameters:
7348: +   mat - the matrix
7349: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7350: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be   symmetrized
7351: -   inodecompressed - PETSC_TRUE or PETSC_FALSE  indicating if the nonzero structure of the
7352:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7353:                  always used.

7355:     Output Parameters:
7356: +   n - number of rows in the (possibly compressed) matrix
7357: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7358: .   ja - the column indices
7359: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7360:            are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set

7362:     Level: developer

7364:     Notes:
7365:     You CANNOT change any of the ia[] or ja[] values.

7367:     Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values.

7369:     Fortran Notes:
7370:     In Fortran use
7371: $
7372: $      PetscInt ia(1), ja(1)
7373: $      PetscOffset iia, jja
7374: $      call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr)
7375: $      ! Access the ith and jth entries via ia(iia + i) and ja(jja + j)

7377:      or
7378: $
7379: $    PetscInt, pointer :: ia(:),ja(:)
7380: $    call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7381: $    ! Access the ith and jth entries via ia(i) and ja(j)

7383: .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray()
7384: @*/
7385: PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7386: {

7396:   MatCheckPreallocated(mat,1);
7397:   if (!mat->ops->getrowij) *done = PETSC_FALSE;
7398:   else {
7399:     *done = PETSC_TRUE;
7400:     PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);
7401:     (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7402:     PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);
7403:   }
7404:   return(0);
7405: }

7407: /*@C
7408:     MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7410:     Collective on Mat

7412:     Input Parameters:
7413: +   mat - the matrix
7414: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7415: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7416:                 symmetrized
7417: .   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7418:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7419:                  always used.
7420: .   n - number of columns in the (possibly compressed) matrix
7421: .   ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
7422: -   ja - the row indices

7424:     Output Parameters:
7425: .   done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned

7427:     Level: developer

7429: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7430: @*/
7431: PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7432: {

7442:   MatCheckPreallocated(mat,1);
7443:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
7444:   else {
7445:     *done = PETSC_TRUE;
7446:     (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7447:   }
7448:   return(0);
7449: }

7451: /*@C
7452:     MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with
7453:     MatGetRowIJ().

7455:     Collective on Mat

7457:     Input Parameters:
7458: +   mat - the matrix
7459: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7460: .   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7461:                 symmetrized
7462: .   inodecompressed -  PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7463:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7464:                  always used.
7465: .   n - size of (possibly compressed) matrix
7466: .   ia - the row pointers
7467: -   ja - the column indices

7469:     Output Parameters:
7470: .   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7472:     Note:
7473:     This routine zeros out n, ia, and ja. This is to prevent accidental
7474:     us of the array after it has been restored. If you pass NULL, it will
7475:     not zero the pointers.  Use of ia or ja after MatRestoreRowIJ() is invalid.

7477:     Level: developer

7479: .seealso: MatGetRowIJ(), MatRestoreColumnIJ()
7480: @*/
7481: PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7482: {

7491:   MatCheckPreallocated(mat,1);

7493:   if (!mat->ops->restorerowij) *done = PETSC_FALSE;
7494:   else {
7495:     *done = PETSC_TRUE;
7496:     (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7497:     if (n)  *n = 0;
7498:     if (ia) *ia = NULL;
7499:     if (ja) *ja = NULL;
7500:   }
7501:   return(0);
7502: }

7504: /*@C
7505:     MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with
7506:     MatGetColumnIJ().

7508:     Collective on Mat

7510:     Input Parameters:
7511: +   mat - the matrix
7512: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7513: -   symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be
7514:                 symmetrized
7515: -   inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the
7516:                  inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is
7517:                  always used.

7519:     Output Parameters:
7520: +   n - size of (possibly compressed) matrix
7521: .   ia - the column pointers
7522: .   ja - the row indices
7523: -   done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned

7525:     Level: developer

7527: .seealso: MatGetColumnIJ(), MatRestoreRowIJ()
7528: @*/
7529: PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
7530: {

7539:   MatCheckPreallocated(mat,1);

7541:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
7542:   else {
7543:     *done = PETSC_TRUE;
7544:     (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);
7545:     if (n)  *n = 0;
7546:     if (ia) *ia = NULL;
7547:     if (ja) *ja = NULL;
7548:   }
7549:   return(0);
7550: }

7552: /*@C
7553:     MatColoringPatch -Used inside matrix coloring routines that
7554:     use MatGetRowIJ() and/or MatGetColumnIJ().

7556:     Collective on Mat

7558:     Input Parameters:
7559: +   mat - the matrix
7560: .   ncolors - max color value
7561: .   n   - number of entries in colorarray
7562: -   colorarray - array indicating color for each column

7564:     Output Parameters:
7565: .   iscoloring - coloring generated using colorarray information

7567:     Level: developer

7569: .seealso: MatGetRowIJ(), MatGetColumnIJ()

7571: @*/
7572: PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring)
7573: {

7581:   MatCheckPreallocated(mat,1);

7583:   if (!mat->ops->coloringpatch) {
7584:     ISColoringCreate(PetscObjectComm((PetscObject)mat),ncolors,n,colorarray,PETSC_OWN_POINTER,iscoloring);
7585:   } else {
7586:     (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);
7587:   }
7588:   return(0);
7589: }


7592: /*@
7593:    MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

7595:    Logically Collective on Mat

7597:    Input Parameter:
7598: .  mat - the factored matrix to be reset

7600:    Notes:
7601:    This routine should be used only with factored matrices formed by in-place
7602:    factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE
7603:    format).  This option can save memory, for example, when solving nonlinear
7604:    systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
7605:    ILU(0) preconditioner.

7607:    Note that one can specify in-place ILU(0) factorization by calling
7608: .vb
7609:      PCType(pc,PCILU);
7610:      PCFactorSeUseInPlace(pc);
7611: .ve
7612:    or by using the options -pc_type ilu -pc_factor_in_place

7614:    In-place factorization ILU(0) can also be used as a local
7615:    solver for the blocks within the block Jacobi or additive Schwarz
7616:    methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
7617:    for details on setting local solver options.

7619:    Most users should employ the simplified KSP interface for linear solvers
7620:    instead of working directly with matrix algebra routines such as this.
7621:    See, e.g., KSPCreate().

7623:    Level: developer

7625: .seealso: PCFactorSetUseInPlace(), PCFactorGetUseInPlace()

7627: @*/
7628: PetscErrorCode MatSetUnfactored(Mat mat)
7629: {

7635:   MatCheckPreallocated(mat,1);
7636:   mat->factortype = MAT_FACTOR_NONE;
7637:   if (!mat->ops->setunfactored) return(0);
7638:   (*mat->ops->setunfactored)(mat);
7639:   return(0);
7640: }

7642: /*MC
7643:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran90.

7645:     Synopsis:
7646:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7648:     Not collective

7650:     Input Parameter:
7651: .   x - matrix

7653:     Output Parameters:
7654: +   xx_v - the Fortran90 pointer to the array
7655: -   ierr - error code

7657:     Example of Usage:
7658: .vb
7659:       PetscScalar, pointer xx_v(:,:)
7660:       ....
7661:       call MatDenseGetArrayF90(x,xx_v,ierr)
7662:       a = xx_v(3)
7663:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
7664: .ve

7666:     Level: advanced

7668: .seealso:  MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJGetArrayF90()

7670: M*/

7672: /*MC
7673:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
7674:     accessed with MatDenseGetArrayF90().

7676:     Synopsis:
7677:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

7679:     Not collective

7681:     Input Parameters:
7682: +   x - matrix
7683: -   xx_v - the Fortran90 pointer to the array

7685:     Output Parameter:
7686: .   ierr - error code

7688:     Example of Usage:
7689: .vb
7690:        PetscScalar, pointer xx_v(:,:)
7691:        ....
7692:        call MatDenseGetArrayF90(x,xx_v,ierr)
7693:        a = xx_v(3)
7694:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
7695: .ve

7697:     Level: advanced

7699: .seealso:  MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJRestoreArrayF90()

7701: M*/


7704: /*MC
7705:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran90.

7707:     Synopsis:
7708:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7710:     Not collective

7712:     Input Parameter:
7713: .   x - matrix

7715:     Output Parameters:
7716: +   xx_v - the Fortran90 pointer to the array
7717: -   ierr - error code

7719:     Example of Usage:
7720: .vb
7721:       PetscScalar, pointer xx_v(:)
7722:       ....
7723:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7724:       a = xx_v(3)
7725:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7726: .ve

7728:     Level: advanced

7730: .seealso:  MatSeqAIJRestoreArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseGetArrayF90()

7732: M*/

7734: /*MC
7735:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
7736:     accessed with MatSeqAIJGetArrayF90().

7738:     Synopsis:
7739:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

7741:     Not collective

7743:     Input Parameters:
7744: +   x - matrix
7745: -   xx_v - the Fortran90 pointer to the array

7747:     Output Parameter:
7748: .   ierr - error code

7750:     Example of Usage:
7751: .vb
7752:        PetscScalar, pointer xx_v(:)
7753:        ....
7754:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
7755:        a = xx_v(3)
7756:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
7757: .ve

7759:     Level: advanced

7761: .seealso:  MatSeqAIJGetArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseRestoreArrayF90()

7763: M*/


7766: /*@
7767:     MatCreateSubMatrix - Gets a single submatrix on the same number of processors
7768:                       as the original matrix.

7770:     Collective on Mat

7772:     Input Parameters:
7773: +   mat - the original matrix
7774: .   isrow - parallel IS containing the rows this processor should obtain
7775: .   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.
7776: -   cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

7778:     Output Parameter:
7779: .   newmat - the new submatrix, of the same type as the old

7781:     Level: advanced

7783:     Notes:
7784:     The submatrix will be able to be multiplied with vectors using the same layout as iscol.

7786:     Some matrix types place restrictions on the row and column indices, such
7787:     as that they be sorted or that they be equal to each other.

7789:     The index sets may not have duplicate entries.

7791:       The first time this is called you should use a cll of MAT_INITIAL_MATRIX,
7792:    the MatCreateSubMatrix() routine will create the newmat for you. Any additional calls
7793:    to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX
7794:    will reuse the matrix generated the first time.  You should call MatDestroy() on newmat when
7795:    you are finished using it.

7797:     The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
7798:     the input matrix.

7800:     If iscol is NULL then all columns are obtained (not supported in Fortran).

7802:    Example usage:
7803:    Consider the following 8x8 matrix with 34 non-zero values, that is
7804:    assembled across 3 processors. Let's assume that proc0 owns 3 rows,
7805:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
7806:    as follows:

7808: .vb
7809:             1  2  0  |  0  3  0  |  0  4
7810:     Proc0   0  5  6  |  7  0  0  |  8  0
7811:             9  0 10  | 11  0  0  | 12  0
7812:     -------------------------------------
7813:            13  0 14  | 15 16 17  |  0  0
7814:     Proc1   0 18  0  | 19 20 21  |  0  0
7815:             0  0  0  | 22 23  0  | 24  0
7816:     -------------------------------------
7817:     Proc2  25 26 27  |  0  0 28  | 29  0
7818:            30  0  0  | 31 32 33  |  0 34
7819: .ve

7821:     Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6].  The resulting submatrix is

7823: .vb
7824:             2  0  |  0  3  0  |  0
7825:     Proc0   5  6  |  7  0  0  |  8
7826:     -------------------------------
7827:     Proc1  18  0  | 19 20 21  |  0
7828:     -------------------------------
7829:     Proc2  26 27  |  0  0 28  | 29
7830:             0  0  | 31 32 33  |  0
7831: .ve


7834: .seealso: MatCreateSubMatrices(), MatCreateSubMatricesMPI(), MatCreateSubMatrixVirtual(), MatSubMatrixVirtualUpdate()
7835: @*/
7836: PetscErrorCode MatCreateSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat)
7837: {
7839:   PetscMPIInt    size;
7840:   Mat            *local;
7841:   IS             iscoltmp;
7842:   PetscBool      flg;

7851:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
7852:   if (cll == MAT_IGNORE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Cannot use MAT_IGNORE_MATRIX");

7854:   MatCheckPreallocated(mat,1);
7855:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);

7857:   if (!iscol || isrow == iscol) {
7858:     PetscBool   stride;
7859:     PetscMPIInt grabentirematrix = 0,grab;
7860:     PetscObjectTypeCompare((PetscObject)isrow,ISSTRIDE,&stride);
7861:     if (stride) {
7862:       PetscInt first,step,n,rstart,rend;
7863:       ISStrideGetInfo(isrow,&first,&step);
7864:       if (step == 1) {
7865:         MatGetOwnershipRange(mat,&rstart,&rend);
7866:         if (rstart == first) {
7867:           ISGetLocalSize(isrow,&n);
7868:           if (n == rend-rstart) {
7869:             grabentirematrix = 1;
7870:           }
7871:         }
7872:       }
7873:     }
7874:     MPIU_Allreduce(&grabentirematrix,&grab,1,MPI_INT,MPI_MIN,PetscObjectComm((PetscObject)mat));
7875:     if (grab) {
7876:       PetscInfo(mat,"Getting entire matrix as submatrix\n");
7877:       if (cll == MAT_INITIAL_MATRIX) {
7878:         *newmat = mat;
7879:         PetscObjectReference((PetscObject)mat);
7880:       }
7881:       return(0);
7882:     }
7883:   }

7885:   if (!iscol) {
7886:     ISCreateStride(PetscObjectComm((PetscObject)mat),mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);
7887:   } else {
7888:     iscoltmp = iscol;
7889:   }

7891:   /* if original matrix is on just one processor then use submatrix generated */
7892:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
7893:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);
7894:     goto setproperties;
7895:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
7896:     MatCreateSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);
7897:     *newmat = *local;
7898:     PetscFree(local);
7899:     goto setproperties;
7900:   } else if (!mat->ops->createsubmatrix) {
7901:     /* Create a new matrix type that implements the operation using the full matrix */
7902:     PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7903:     switch (cll) {
7904:     case MAT_INITIAL_MATRIX:
7905:       MatCreateSubMatrixVirtual(mat,isrow,iscoltmp,newmat);
7906:       break;
7907:     case MAT_REUSE_MATRIX:
7908:       MatSubMatrixVirtualUpdate(*newmat,mat,isrow,iscoltmp);
7909:       break;
7910:     default: SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
7911:     }
7912:     PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);
7913:     goto setproperties;
7914:   }

7916:   if (!mat->ops->createsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
7917:   PetscLogEventBegin(MAT_CreateSubMat,mat,0,0,0);
7918:   (*mat->ops->createsubmatrix)(mat,isrow,iscoltmp,cll,newmat);
7919:   PetscLogEventEnd(MAT_CreateSubMat,mat,0,0,0);

7921: setproperties:
7922:   ISEqualUnsorted(isrow,iscoltmp,&flg);
7923:   if (flg) {
7924:     MatPropagateSymmetryOptions(mat,*newmat);
7925:   }
7926:   if (!iscol) {ISDestroy(&iscoltmp);}
7927:   if (*newmat && cll == MAT_INITIAL_MATRIX) {PetscObjectStateIncrease((PetscObject)*newmat);}
7928:   return(0);
7929: }

7931: /*@
7932:    MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

7934:    Not Collective

7936:    Input Parameters:
7937: +  A - the matrix we wish to propagate options from
7938: -  B - the matrix we wish to propagate options to

7940:    Level: beginner

7942:    Notes: Propagates the options associated to MAT_SYMMETRY_ETERNAL, MAT_STRUCTURALLY_SYMMETRIC, MAT_HERMITIAN, MAT_SPD and MAT_SYMMETRIC

7944: .seealso: MatSetOption()
7945: @*/
7946: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
7947: {

7953:   if (A->symmetric_eternal) { /* symmetric_eternal does not have a corresponding *set flag */
7954:     MatSetOption(B,MAT_SYMMETRY_ETERNAL,A->symmetric_eternal);
7955:   }
7956:   if (A->structurally_symmetric_set) {
7957:     MatSetOption(B,MAT_STRUCTURALLY_SYMMETRIC,A->structurally_symmetric);
7958:   }
7959:   if (A->hermitian_set) {
7960:     MatSetOption(B,MAT_HERMITIAN,A->hermitian);
7961:   }
7962:   if (A->spd_set) {
7963:     MatSetOption(B,MAT_SPD,A->spd);
7964:   }
7965:   if (A->symmetric_set) {
7966:     MatSetOption(B,MAT_SYMMETRIC,A->symmetric);
7967:   }
7968:   return(0);
7969: }

7971: /*@
7972:    MatStashSetInitialSize - sets the sizes of the matrix stash, that is
7973:    used during the assembly process to store values that belong to
7974:    other processors.

7976:    Not Collective

7978:    Input Parameters:
7979: +  mat   - the matrix
7980: .  size  - the initial size of the stash.
7981: -  bsize - the initial size of the block-stash(if used).

7983:    Options Database Keys:
7984: +   -matstash_initial_size <size> or <size0,size1,...sizep-1>
7985: -   -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1>

7987:    Level: intermediate

7989:    Notes:
7990:      The block-stash is used for values set with MatSetValuesBlocked() while
7991:      the stash is used for values set with MatSetValues()

7993:      Run with the option -info and look for output of the form
7994:      MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
7995:      to determine the appropriate value, MM, to use for size and
7996:      MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
7997:      to determine the value, BMM to use for bsize


8000: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo()

8002: @*/
8003: PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize)
8004: {

8010:   MatStashSetInitialSize_Private(&mat->stash,size);
8011:   MatStashSetInitialSize_Private(&mat->bstash,bsize);
8012:   return(0);
8013: }

8015: /*@
8016:    MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
8017:      the matrix

8019:    Neighbor-wise Collective on Mat

8021:    Input Parameters:
8022: +  mat   - the matrix
8023: .  x,y - the vectors
8024: -  w - where the result is stored

8026:    Level: intermediate

8028:    Notes:
8029:     w may be the same vector as y.

8031:     This allows one to use either the restriction or interpolation (its transpose)
8032:     matrix to do the interpolation

8034: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

8036: @*/
8037: PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w)
8038: {
8040:   PetscInt       M,N,Ny;

8048:   MatCheckPreallocated(A,1);
8049:   MatGetSize(A,&M,&N);
8050:   VecGetSize(y,&Ny);
8051:   if (M == Ny) {
8052:     MatMultAdd(A,x,y,w);
8053:   } else {
8054:     MatMultTransposeAdd(A,x,y,w);
8055:   }
8056:   return(0);
8057: }

8059: /*@
8060:    MatInterpolate - y = A*x or A'*x depending on the shape of
8061:      the matrix

8063:    Neighbor-wise Collective on Mat

8065:    Input Parameters:
8066: +  mat   - the matrix
8067: -  x,y - the vectors

8069:    Level: intermediate

8071:    Notes:
8072:     This allows one to use either the restriction or interpolation (its transpose)
8073:     matrix to do the interpolation

8075: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict()

8077: @*/
8078: PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y)
8079: {
8081:   PetscInt       M,N,Ny;

8088:   MatCheckPreallocated(A,1);
8089:   MatGetSize(A,&M,&N);
8090:   VecGetSize(y,&Ny);
8091:   if (M == Ny) {
8092:     MatMult(A,x,y);
8093:   } else {
8094:     MatMultTranspose(A,x,y);
8095:   }
8096:   return(0);
8097: }

8099: /*@
8100:    MatRestrict - y = A*x or A'*x

8102:    Neighbor-wise Collective on Mat

8104:    Input Parameters:
8105: +  mat   - the matrix
8106: -  x,y - the vectors

8108:    Level: intermediate

8110:    Notes:
8111:     This allows one to use either the restriction or interpolation (its transpose)
8112:     matrix to do the restriction

8114: .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate()

8116: @*/
8117: PetscErrorCode MatRestrict(Mat A,Vec x,Vec y)
8118: {
8120:   PetscInt       M,N,Ny;

8127:   MatCheckPreallocated(A,1);

8129:   MatGetSize(A,&M,&N);
8130:   VecGetSize(y,&Ny);
8131:   if (M == Ny) {
8132:     MatMult(A,x,y);
8133:   } else {
8134:     MatMultTranspose(A,x,y);
8135:   }
8136:   return(0);
8137: }

8139: /*@
8140:    MatGetNullSpace - retrieves the null space of a matrix.

8142:    Logically Collective on Mat

8144:    Input Parameters:
8145: +  mat - the matrix
8146: -  nullsp - the null space object

8148:    Level: developer

8150: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetNullSpace()
8151: @*/
8152: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8153: {
8157:   *nullsp = (mat->symmetric_set && mat->symmetric && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8158:   return(0);
8159: }

8161: /*@
8162:    MatSetNullSpace - attaches a null space to a matrix.

8164:    Logically Collective on Mat

8166:    Input Parameters:
8167: +  mat - the matrix
8168: -  nullsp - the null space object

8170:    Level: advanced

8172:    Notes:
8173:       This null space is used by the linear solvers. Overwrites any previous null space that may have been attached

8175:       For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) you also likely should
8176:       call MatSetTransposeNullSpace(). This allows the linear system to be solved in a least squares sense.

8178:       You can remove the null space by calling this routine with an nullsp of NULL


8181:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8182:    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).
8183:    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
8184:    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
8185:    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).

8187:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8189:     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
8190:     routine also automatically calls MatSetTransposeNullSpace().

8192: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetTransposeNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8193: @*/
8194: PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp)
8195: {

8201:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8202:   MatNullSpaceDestroy(&mat->nullsp);
8203:   mat->nullsp = nullsp;
8204:   if (mat->symmetric_set && mat->symmetric) {
8205:     MatSetTransposeNullSpace(mat,nullsp);
8206:   }
8207:   return(0);
8208: }

8210: /*@
8211:    MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8213:    Logically Collective on Mat

8215:    Input Parameters:
8216: +  mat - the matrix
8217: -  nullsp - the null space object

8219:    Level: developer

8221: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatSetTransposeNullSpace(), MatSetNullSpace(), MatGetNullSpace()
8222: @*/
8223: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8224: {
8229:   *nullsp = (mat->symmetric_set && mat->symmetric && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8230:   return(0);
8231: }

8233: /*@
8234:    MatSetTransposeNullSpace - attaches a null space to a matrix.

8236:    Logically Collective on Mat

8238:    Input Parameters:
8239: +  mat - the matrix
8240: -  nullsp - the null space object

8242:    Level: advanced

8244:    Notes:
8245:       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.
8246:       You must also call MatSetNullSpace()


8249:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8250:    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).
8251:    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
8252:    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
8253:    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).

8255:       Krylov solvers can produce the minimal norm solution to the least squares problem by utilizing MatNullSpaceRemove().

8257: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove()
8258: @*/
8259: PetscErrorCode MatSetTransposeNullSpace(Mat mat,MatNullSpace nullsp)
8260: {

8266:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8267:   MatNullSpaceDestroy(&mat->transnullsp);
8268:   mat->transnullsp = nullsp;
8269:   return(0);
8270: }

8272: /*@
8273:    MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8274:         This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8276:    Logically Collective on Mat

8278:    Input Parameters:
8279: +  mat - the matrix
8280: -  nullsp - the null space object

8282:    Level: advanced

8284:    Notes:
8285:       Overwrites any previous near null space that may have been attached

8287:       You can remove the null space by calling this routine with an nullsp of NULL

8289: .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace(), MatNullSpaceCreateRigidBody(), MatGetNearNullSpace()
8290: @*/
8291: PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp)
8292: {

8299:   MatCheckPreallocated(mat,1);
8300:   if (nullsp) {PetscObjectReference((PetscObject)nullsp);}
8301:   MatNullSpaceDestroy(&mat->nearnullsp);
8302:   mat->nearnullsp = nullsp;
8303:   return(0);
8304: }

8306: /*@
8307:    MatGetNearNullSpace - Get null space attached with MatSetNearNullSpace()

8309:    Not Collective

8311:    Input Parameter:
8312: .  mat - the matrix

8314:    Output Parameter:
8315: .  nullsp - the null space object, NULL if not set

8317:    Level: developer

8319: .seealso: MatSetNearNullSpace(), MatGetNullSpace(), MatNullSpaceCreate()
8320: @*/
8321: PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp)
8322: {
8327:   MatCheckPreallocated(mat,1);
8328:   *nullsp = mat->nearnullsp;
8329:   return(0);
8330: }

8332: /*@C
8333:    MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8335:    Collective on Mat

8337:    Input Parameters:
8338: +  mat - the matrix
8339: .  row - row/column permutation
8340: .  fill - expected fill factor >= 1.0
8341: -  level - level of fill, for ICC(k)

8343:    Notes:
8344:    Probably really in-place only when level of fill is zero, otherwise allocates
8345:    new space to store factored matrix and deletes previous memory.

8347:    Most users should employ the simplified KSP interface for linear solvers
8348:    instead of working directly with matrix algebra routines such as this.
8349:    See, e.g., KSPCreate().

8351:    Level: developer


8354: .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()

8356:     Developer Note: fortran interface is not autogenerated as the f90
8357:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8359: @*/
8360: PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info)
8361: {

8369:   if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square");
8370:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
8371:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
8372:   if (!mat->ops->iccfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8373:   MatCheckPreallocated(mat,1);
8374:   (*mat->ops->iccfactor)(mat,row,info);
8375:   PetscObjectStateIncrease((PetscObject)mat);
8376:   return(0);
8377: }

8379: /*@
8380:    MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
8381:          ghosted ones.

8383:    Not Collective

8385:    Input Parameters:
8386: +  mat - the matrix
8387: -  diag = the diagonal values, including ghost ones

8389:    Level: developer

8391:    Notes:
8392:     Works only for MPIAIJ and MPIBAIJ matrices

8394: .seealso: MatDiagonalScale()
8395: @*/
8396: PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag)
8397: {
8399:   PetscMPIInt    size;


8406:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled");
8407:   PetscLogEventBegin(MAT_Scale,mat,0,0,0);
8408:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
8409:   if (size == 1) {
8410:     PetscInt n,m;
8411:     VecGetSize(diag,&n);
8412:     MatGetSize(mat,0,&m);
8413:     if (m == n) {
8414:       MatDiagonalScale(mat,0,diag);
8415:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions");
8416:   } else {
8417:     PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));
8418:   }
8419:   PetscLogEventEnd(MAT_Scale,mat,0,0,0);
8420:   PetscObjectStateIncrease((PetscObject)mat);
8421:   return(0);
8422: }

8424: /*@
8425:    MatGetInertia - Gets the inertia from a factored matrix

8427:    Collective on Mat

8429:    Input Parameter:
8430: .  mat - the matrix

8432:    Output Parameters:
8433: +   nneg - number of negative eigenvalues
8434: .   nzero - number of zero eigenvalues
8435: -   npos - number of positive eigenvalues

8437:    Level: advanced

8439:    Notes:
8440:     Matrix must have been factored by MatCholeskyFactor()


8443: @*/
8444: PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
8445: {

8451:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8452:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled");
8453:   if (!mat->ops->getinertia) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8454:   (*mat->ops->getinertia)(mat,nneg,nzero,npos);
8455:   return(0);
8456: }

8458: /* ----------------------------------------------------------------*/
8459: /*@C
8460:    MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

8462:    Neighbor-wise Collective on Mats

8464:    Input Parameters:
8465: +  mat - the factored matrix
8466: -  b - the right-hand-side vectors

8468:    Output Parameter:
8469: .  x - the result vectors

8471:    Notes:
8472:    The vectors b and x cannot be the same.  I.e., one cannot
8473:    call MatSolves(A,x,x).

8475:    Notes:
8476:    Most users should employ the simplified KSP interface for linear solvers
8477:    instead of working directly with matrix algebra routines such as this.
8478:    See, e.g., KSPCreate().

8480:    Level: developer

8482: .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve()
8483: @*/
8484: PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x)
8485: {

8491:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
8492:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix");
8493:   if (!mat->rmap->N && !mat->cmap->N) return(0);

8495:   if (!mat->ops->solves) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name);
8496:   MatCheckPreallocated(mat,1);
8497:   PetscLogEventBegin(MAT_Solves,mat,0,0,0);
8498:   (*mat->ops->solves)(mat,b,x);
8499:   PetscLogEventEnd(MAT_Solves,mat,0,0,0);
8500:   return(0);
8501: }

8503: /*@
8504:    MatIsSymmetric - Test whether a matrix is symmetric

8506:    Collective on Mat

8508:    Input Parameter:
8509: +  A - the matrix to test
8510: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

8512:    Output Parameters:
8513: .  flg - the result

8515:    Notes:
8516:     For real numbers MatIsSymmetric() and MatIsHermitian() return identical results

8518:    Level: intermediate

8520: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown()
8521: @*/
8522: PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool  *flg)
8523: {


8530:   if (!A->symmetric_set) {
8531:     if (!A->ops->issymmetric) {
8532:       MatType mattype;
8533:       MatGetType(A,&mattype);
8534:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type %s does not support checking for symmetric",mattype);
8535:     }
8536:     (*A->ops->issymmetric)(A,tol,flg);
8537:     if (!tol) {
8538:       MatSetOption(A,MAT_SYMMETRIC,*flg);
8539:     }
8540:   } else if (A->symmetric) {
8541:     *flg = PETSC_TRUE;
8542:   } else if (!tol) {
8543:     *flg = PETSC_FALSE;
8544:   } else {
8545:     if (!A->ops->issymmetric) {
8546:       MatType mattype;
8547:       MatGetType(A,&mattype);
8548:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type %s does not support checking for symmetric",mattype);
8549:     }
8550:     (*A->ops->issymmetric)(A,tol,flg);
8551:   }
8552:   return(0);
8553: }

8555: /*@
8556:    MatIsHermitian - Test whether a matrix is Hermitian

8558:    Collective on Mat

8560:    Input Parameter:
8561: +  A - the matrix to test
8562: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

8564:    Output Parameters:
8565: .  flg - the result

8567:    Level: intermediate

8569: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(),
8570:           MatIsSymmetricKnown(), MatIsSymmetric()
8571: @*/
8572: PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool  *flg)
8573: {


8580:   if (!A->hermitian_set) {
8581:     if (!A->ops->ishermitian) {
8582:       MatType mattype;
8583:       MatGetType(A,&mattype);
8584:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type %s does not support checking for hermitian",mattype);
8585:     }
8586:     (*A->ops->ishermitian)(A,tol,flg);
8587:     if (!tol) {
8588:       MatSetOption(A,MAT_HERMITIAN,*flg);
8589:     }
8590:   } else if (A->hermitian) {
8591:     *flg = PETSC_TRUE;
8592:   } else if (!tol) {
8593:     *flg = PETSC_FALSE;
8594:   } else {
8595:     if (!A->ops->ishermitian) {
8596:       MatType mattype;
8597:       MatGetType(A,&mattype);
8598:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type %s does not support checking for hermitian",mattype);
8599:     }
8600:     (*A->ops->ishermitian)(A,tol,flg);
8601:   }
8602:   return(0);
8603: }

8605: /*@
8606:    MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric.

8608:    Not Collective

8610:    Input Parameter:
8611: .  A - the matrix to check

8613:    Output Parameters:
8614: +  set - if the symmetric flag is set (this tells you if the next flag is valid)
8615: -  flg - the result

8617:    Level: advanced

8619:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric()
8620:          if you want it explicitly checked

8622: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8623: @*/
8624: PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool  *set,PetscBool  *flg)
8625: {
8630:   if (A->symmetric_set) {
8631:     *set = PETSC_TRUE;
8632:     *flg = A->symmetric;
8633:   } else {
8634:     *set = PETSC_FALSE;
8635:   }
8636:   return(0);
8637: }

8639: /*@
8640:    MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian.

8642:    Not Collective

8644:    Input Parameter:
8645: .  A - the matrix to check

8647:    Output Parameters:
8648: +  set - if the hermitian flag is set (this tells you if the next flag is valid)
8649: -  flg - the result

8651:    Level: advanced

8653:    Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian()
8654:          if you want it explicitly checked

8656: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric()
8657: @*/
8658: PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool *set,PetscBool *flg)
8659: {
8664:   if (A->hermitian_set) {
8665:     *set = PETSC_TRUE;
8666:     *flg = A->hermitian;
8667:   } else {
8668:     *set = PETSC_FALSE;
8669:   }
8670:   return(0);
8671: }

8673: /*@
8674:    MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

8676:    Collective on Mat

8678:    Input Parameter:
8679: .  A - the matrix to test

8681:    Output Parameters:
8682: .  flg - the result

8684:    Level: intermediate

8686: .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption()
8687: @*/
8688: PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool *flg)
8689: {

8695:   if (!A->structurally_symmetric_set) {
8696:     if (!A->ops->isstructurallysymmetric) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix of type %s does not support checking for structural symmetric",((PetscObject)A)->type_name);
8697:     (*A->ops->isstructurallysymmetric)(A,flg);
8698:     MatSetOption(A,MAT_STRUCTURALLY_SYMMETRIC,*flg);
8699:   } else *flg = A->structurally_symmetric;
8700:   return(0);
8701: }

8703: /*@
8704:    MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
8705:        to be communicated to other processors during the MatAssemblyBegin/End() process

8707:     Not collective

8709:    Input Parameter:
8710: .   vec - the vector

8712:    Output Parameters:
8713: +   nstash   - the size of the stash
8714: .   reallocs - the number of additional mallocs incurred.
8715: .   bnstash   - the size of the block stash
8716: -   breallocs - the number of additional mallocs incurred.in the block stash

8718:    Level: advanced

8720: .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize()

8722: @*/
8723: PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs)
8724: {

8728:   MatStashGetInfo_Private(&mat->stash,nstash,reallocs);
8729:   MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);
8730:   return(0);
8731: }

8733: /*@C
8734:    MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
8735:      parallel layout

8737:    Collective on Mat

8739:    Input Parameter:
8740: .  mat - the matrix

8742:    Output Parameter:
8743: +   right - (optional) vector that the matrix can be multiplied against
8744: -   left - (optional) vector that the matrix vector product can be stored in

8746:    Notes:
8747:     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().

8749:   Notes:
8750:     These are new vectors which are not owned by the Mat, they should be destroyed in VecDestroy() when no longer needed

8752:   Level: advanced

8754: .seealso: MatCreate(), VecDestroy()
8755: @*/
8756: PetscErrorCode MatCreateVecs(Mat mat,Vec *right,Vec *left)
8757: {

8763:   if (mat->ops->getvecs) {
8764:     (*mat->ops->getvecs)(mat,right,left);
8765:   } else {
8766:     PetscInt rbs,cbs;
8767:     MatGetBlockSizes(mat,&rbs,&cbs);
8768:     if (right) {
8769:       if (mat->cmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for columns not yet setup");
8770:       VecCreate(PetscObjectComm((PetscObject)mat),right);
8771:       VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
8772:       VecSetBlockSize(*right,cbs);
8773:       VecSetType(*right,mat->defaultvectype);
8774:       PetscLayoutReference(mat->cmap,&(*right)->map);
8775:     }
8776:     if (left) {
8777:       if (mat->rmap->n < 0) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"PetscLayout for rows not yet setup");
8778:       VecCreate(PetscObjectComm((PetscObject)mat),left);
8779:       VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
8780:       VecSetBlockSize(*left,rbs);
8781:       VecSetType(*left,mat->defaultvectype);
8782:       PetscLayoutReference(mat->rmap,&(*left)->map);
8783:     }
8784:   }
8785:   return(0);
8786: }

8788: /*@C
8789:    MatFactorInfoInitialize - Initializes a MatFactorInfo data structure
8790:      with default values.

8792:    Not Collective

8794:    Input Parameters:
8795: .    info - the MatFactorInfo data structure


8798:    Notes:
8799:     The solvers are generally used through the KSP and PC objects, for example
8800:           PCLU, PCILU, PCCHOLESKY, PCICC

8802:    Level: developer

8804: .seealso: MatFactorInfo

8806:     Developer Note: fortran interface is not autogenerated as the f90
8807:     interface defintion cannot be generated correctly [due to MatFactorInfo]

8809: @*/

8811: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
8812: {

8816:   PetscMemzero(info,sizeof(MatFactorInfo));
8817:   return(0);
8818: }

8820: /*@
8821:    MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

8823:    Collective on Mat

8825:    Input Parameters:
8826: +  mat - the factored matrix
8827: -  is - the index set defining the Schur indices (0-based)

8829:    Notes:
8830:     Call MatFactorSolveSchurComplement() or MatFactorSolveSchurComplementTranspose() after this call to solve a Schur complement system.

8832:    You can call MatFactorGetSchurComplement() or MatFactorCreateSchurComplement() after this call.

8834:    Level: developer

8836: .seealso: MatGetFactor(), MatFactorGetSchurComplement(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSolveSchurComplement(),
8837:           MatFactorSolveSchurComplementTranspose(), MatFactorSolveSchurComplement()

8839: @*/
8840: PetscErrorCode MatFactorSetSchurIS(Mat mat,IS is)
8841: {
8842:   PetscErrorCode ierr,(*f)(Mat,IS);

8850:   if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix");
8851:   PetscObjectQueryFunction((PetscObject)mat,"MatFactorSetSchurIS_C",&f);
8852:   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");
8853:   MatDestroy(&mat->schur);
8854:   (*f)(mat,is);
8855:   if (!mat->schur) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_PLIB,"Schur complement has not been created");
8856:   return(0);
8857: }

8859: /*@
8860:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

8862:    Logically Collective on Mat

8864:    Input Parameters:
8865: +  F - the factored matrix obtained by calling MatGetFactor() from PETSc-MUMPS interface
8866: .  S - location where to return the Schur complement, can be NULL
8867: -  status - the status of the Schur complement matrix, can be NULL

8869:    Notes:
8870:    You must call MatFactorSetSchurIS() before calling this routine.

8872:    The routine provides a copy of the Schur matrix stored within the solver data structures.
8873:    The caller must destroy the object when it is no longer needed.
8874:    If MatFactorInvertSchurComplement() has been called, the routine gets back the inverse.

8876:    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)

8878:    Developer Notes:
8879:     The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
8880:    matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

8882:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

8884:    Level: advanced

8886:    References:

8888: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorSchurStatus
8889: @*/
8890: PetscErrorCode MatFactorCreateSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
8891: {

8898:   if (S) {
8899:     PetscErrorCode (*f)(Mat,Mat*);

8901:     PetscObjectQueryFunction((PetscObject)F,"MatFactorCreateSchurComplement_C",&f);
8902:     if (f) {
8903:       (*f)(F,S);
8904:     } else {
8905:       MatDuplicate(F->schur,MAT_COPY_VALUES,S);
8906:     }
8907:   }
8908:   if (status) *status = F->schur_status;
8909:   return(0);
8910: }

8912: /*@
8913:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

8915:    Logically Collective on Mat

8917:    Input Parameters:
8918: +  F - the factored matrix obtained by calling MatGetFactor()
8919: .  *S - location where to return the Schur complement, can be NULL
8920: -  status - the status of the Schur complement matrix, can be NULL

8922:    Notes:
8923:    You must call MatFactorSetSchurIS() before calling this routine.

8925:    Schur complement mode is currently implemented for sequential matrices.
8926:    The routine returns a the Schur Complement stored within the data strutures of the solver.
8927:    If MatFactorInvertSchurComplement() has previously been called, the returned matrix is actually the inverse of the Schur complement.
8928:    The returned matrix should not be destroyed; the caller should call MatFactorRestoreSchurComplement() when the object is no longer needed.

8930:    Use MatFactorCreateSchurComplement() to create a copy of the Schur complement matrix that is within a factored matrix

8932:    See MatCreateSchurComplement() or MatGetSchurComplement() for ways to create virtual or approximate Schur complements.

8934:    Level: advanced

8936:    References:

8938: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
8939: @*/
8940: PetscErrorCode MatFactorGetSchurComplement(Mat F,Mat* S,MatFactorSchurStatus* status)
8941: {
8946:   if (S) *S = F->schur;
8947:   if (status) *status = F->schur_status;
8948:   return(0);
8949: }

8951: /*@
8952:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to MatFactorGetSchurComplement

8954:    Logically Collective on Mat

8956:    Input Parameters:
8957: +  F - the factored matrix obtained by calling MatGetFactor()
8958: .  *S - location where the Schur complement is stored
8959: -  status - the status of the Schur complement matrix (see MatFactorSchurStatus)

8961:    Notes:

8963:    Level: advanced

8965:    References:

8967: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorRestoreSchurComplement(), MatFactorCreateSchurComplement(), MatFactorSchurStatus
8968: @*/
8969: PetscErrorCode MatFactorRestoreSchurComplement(Mat F,Mat* S,MatFactorSchurStatus status)
8970: {

8975:   if (S) {
8977:     *S = NULL;
8978:   }
8979:   F->schur_status = status;
8980:   MatFactorUpdateSchurStatus_Private(F);
8981:   return(0);
8982: }

8984: /*@
8985:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

8987:    Logically Collective on Mat

8989:    Input Parameters:
8990: +  F - the factored matrix obtained by calling MatGetFactor()
8991: .  rhs - location where the right hand side of the Schur complement system is stored
8992: -  sol - location where the solution of the Schur complement system has to be returned

8994:    Notes:
8995:    The sizes of the vectors should match the size of the Schur complement

8997:    Must be called after MatFactorSetSchurIS()

8999:    Level: advanced

9001:    References:

9003: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplement()
9004: @*/
9005: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9006: {

9018:   MatFactorFactorizeSchurComplement(F);
9019:   switch (F->schur_status) {
9020:   case MAT_FACTOR_SCHUR_FACTORED:
9021:     MatSolveTranspose(F->schur,rhs,sol);
9022:     break;
9023:   case MAT_FACTOR_SCHUR_INVERTED:
9024:     MatMultTranspose(F->schur,rhs,sol);
9025:     break;
9026:   default:
9027:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
9028:     break;
9029:   }
9030:   return(0);
9031: }

9033: /*@
9034:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9036:    Logically Collective on Mat

9038:    Input Parameters:
9039: +  F - the factored matrix obtained by calling MatGetFactor()
9040: .  rhs - location where the right hand side of the Schur complement system is stored
9041: -  sol - location where the solution of the Schur complement system has to be returned

9043:    Notes:
9044:    The sizes of the vectors should match the size of the Schur complement

9046:    Must be called after MatFactorSetSchurIS()

9048:    Level: advanced

9050:    References:

9052: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorSolveSchurComplementTranspose()
9053: @*/
9054: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9055: {

9067:   MatFactorFactorizeSchurComplement(F);
9068:   switch (F->schur_status) {
9069:   case MAT_FACTOR_SCHUR_FACTORED:
9070:     MatSolve(F->schur,rhs,sol);
9071:     break;
9072:   case MAT_FACTOR_SCHUR_INVERTED:
9073:     MatMult(F->schur,rhs,sol);
9074:     break;
9075:   default:
9076:     SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_SUP,"Unhandled MatFactorSchurStatus %D",F->schur_status);
9077:     break;
9078:   }
9079:   return(0);
9080: }

9082: /*@
9083:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9085:    Logically Collective on Mat

9087:    Input Parameters:
9088: .  F - the factored matrix obtained by calling MatGetFactor()

9090:    Notes:
9091:     Must be called after MatFactorSetSchurIS().

9093:    Call MatFactorGetSchurComplement() or  MatFactorCreateSchurComplement() AFTER this call to actually compute the inverse and get access to it.

9095:    Level: advanced

9097:    References:

9099: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorGetSchurComplement(), MatFactorCreateSchurComplement()
9100: @*/
9101: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9102: {

9108:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) return(0);
9109:   MatFactorFactorizeSchurComplement(F);
9110:   MatFactorInvertSchurComplement_Private(F);
9111:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9112:   return(0);
9113: }

9115: /*@
9116:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9118:    Logically Collective on Mat

9120:    Input Parameters:
9121: .  F - the factored matrix obtained by calling MatGetFactor()

9123:    Notes:
9124:     Must be called after MatFactorSetSchurIS().

9126:    Level: advanced

9128:    References:

9130: .seealso: MatGetFactor(), MatFactorSetSchurIS(), MatFactorInvertSchurComplement()
9131: @*/
9132: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9133: {

9139:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) return(0);
9140:   MatFactorFactorizeSchurComplement_Private(F);
9141:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9142:   return(0);
9143: }

9145: /*@
9146:    MatPtAP - Creates the matrix product C = P^T * A * P

9148:    Neighbor-wise Collective on Mat

9150:    Input Parameters:
9151: +  A - the matrix
9152: .  P - the projection matrix
9153: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9154: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use PETSC_DEFAULT if you do not have a good estimate
9155:           if the result is a dense matrix this is irrelevent

9157:    Output Parameters:
9158: .  C - the product matrix

9160:    Notes:
9161:    C will be created and must be destroyed by the user with MatDestroy().

9163:    For matrix types without special implementation the function fallbacks to MatMatMult() followed by MatTransposeMatMult().

9165:    Level: intermediate

9167: .seealso: MatMatMult(), MatRARt()
9168: @*/
9169: PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C)
9170: {

9174:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");

9176:   if (scall == MAT_INITIAL_MATRIX) {
9177:     MatProductCreate(A,P,NULL,C);
9178:     MatProductSetType(*C,MATPRODUCT_PtAP);
9179:     MatProductSetAlgorithm(*C,"default");
9180:     MatProductSetFill(*C,fill);

9182:     (*C)->product->api_user = PETSC_TRUE;
9183:     MatProductSetFromOptions(*C);
9184:     MatProductSymbolic(*C);
9185:   } else {
9186:     Mat_Product *product = (*C)->product;
9187:     if (product) { /* user may chage input matrices A or B when REUSE */
9188:       MatProductReplaceMats(A,P,NULL,*C);
9189:     } else SETERRQ(PetscObjectComm((PetscObject)(*C)),PETSC_ERR_SUP,"Call MatProductCreate() or MatProductReplaceProduct() first");
9190:   }

9192:   MatProductNumeric(*C);
9193:   if (A->symmetric_set && A->symmetric) {
9194:     MatSetOption(*C,MAT_SYMMETRIC,PETSC_TRUE);
9195:   }
9196:   return(0);
9197: }

9199: /*@
9200:    MatRARt - Creates the matrix product C = R * A * R^T

9202:    Neighbor-wise Collective on Mat

9204:    Input Parameters:
9205: +  A - the matrix
9206: .  R - the projection matrix
9207: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9208: -  fill - expected fill as ratio of nnz(C)/nnz(A), use PETSC_DEFAULT if you do not have a good estimate
9209:           if the result is a dense matrix this is irrelevent

9211:    Output Parameters:
9212: .  C - the product matrix

9214:    Notes:
9215:    C will be created and must be destroyed by the user with MatDestroy().

9217:    This routine is currently only implemented for pairs of AIJ matrices and classes
9218:    which inherit from AIJ. Due to PETSc sparse matrix block row distribution among processes,
9219:    parallel MatRARt is implemented via explicit transpose of R, which could be very expensive.
9220:    We recommend using MatPtAP().

9222:    Level: intermediate

9224: .seealso: MatMatMult(), MatPtAP()
9225: @*/
9226: PetscErrorCode MatRARt(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C)
9227: {

9231:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");

9233:   if (scall == MAT_INITIAL_MATRIX) {
9234:     MatProductCreate(A,R,NULL,C);
9235:     MatProductSetType(*C,MATPRODUCT_RARt);
9236:     MatProductSetAlgorithm(*C,"default");
9237:     MatProductSetFill(*C,fill);

9239:     (*C)->product->api_user = PETSC_TRUE;
9240:     MatProductSetFromOptions(*C);
9241:     MatProductSymbolic(*C);
9242:   } else { /* scall == MAT_REUSE_MATRIX */
9243:     Mat_Product *product = (*C)->product;
9244:     if (product) {
9245:       /* user may chage input matrices A or R when REUSE */
9246:       MatProductReplaceMats(A,R,NULL,*C);
9247:     } else SETERRQ(PetscObjectComm((PetscObject)(*C)),PETSC_ERR_SUP,"Call MatProductCreate() or MatProductReplaceProduct() first");
9248:   }

9250:   MatProductNumeric(*C);
9251:   return(0);
9252: }


9255: static PetscErrorCode MatProduct_Private(Mat A,Mat B,MatReuse scall,PetscReal fill,MatProductType ptype, Mat *C)
9256: {
9257:   PetscBool      clearproduct = PETSC_FALSE;

9261:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");

9263:   if (scall == MAT_INITIAL_MATRIX) {
9264:     MatProductCreate(A,B,NULL,C);
9265:     MatProductSetType(*C,ptype);
9266:     MatProductSetAlgorithm(*C,"default");
9267:     MatProductSetFill(*C,fill);

9269:     (*C)->product->api_user = PETSC_TRUE;
9270:     MatProductSetFromOptions(*C);
9271:     MatProductSymbolic(*C);
9272:   } else { /* scall == MAT_REUSE_MATRIX */
9273:     Mat_Product *product = (*C)->product;
9274:     if (!product) {
9275:       /* user provide the dense matrix *C without calling MatProductCreate() */
9276:       PetscBool seqdense,mpidense,dense;
9277: #if defined(PETSC_HAVE_CUDA)
9278:       PetscBool seqdensecuda;
9279: #endif
9280:       PetscObjectTypeCompare((PetscObject)(*C),MATSEQDENSE,&seqdense);
9281:       PetscObjectTypeCompare((PetscObject)(*C),MATMPIDENSE,&mpidense);
9282:       PetscObjectTypeCompare((PetscObject)(*C),MATDENSE,&dense);
9283: #if defined(PETSC_HAVE_CUDA)
9284:       PetscObjectTypeCompare((PetscObject)(*C),MATSEQDENSECUDA,&seqdensecuda);
9285:       if (seqdense || mpidense || dense || seqdensecuda) {
9286: #else
9287:       if (seqdense || mpidense || dense) {
9288: #endif
9289:         /* user wants to reuse an assembled dense matrix */
9290:         /* Create product -- see MatCreateProduct() */
9291:         MatProductCreate_Private(A,B,NULL,*C);
9292:         product = (*C)->product;
9293:         product->fill     = fill;
9294:         product->api_user = PETSC_TRUE;

9296:         MatProductSetType(*C,ptype);
9297:         MatProductSetFromOptions(*C);
9298:         MatProductSymbolic(*C);
9299:       } else SETERRQ(PetscObjectComm((PetscObject)(*C)),PETSC_ERR_SUP,"Call MatProductCreate() or MatProductReplaceProduct() first");
9300:       clearproduct = PETSC_TRUE;
9301:     } else { /* user may chage input matrices A or B when REUSE */
9302:       MatProductReplaceMats(A,B,NULL,*C);
9303:     }
9304:   }
9305:   MatProductNumeric(*C);
9306:   if (clearproduct) {
9307:     MatProductClear(*C);
9308:   }
9309:   return(0);
9310: }

9312: /*@
9313:    MatMatMult - Performs Matrix-Matrix Multiplication C=A*B.

9315:    Neighbor-wise Collective on Mat

9317:    Input Parameters:
9318: +  A - the left matrix
9319: .  B - the right matrix
9320: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9321: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate
9322:           if the result is a dense matrix this is irrelevent

9324:    Output Parameters:
9325: .  C - the product matrix

9327:    Notes:
9328:    Unless scall is MAT_REUSE_MATRIX C will be created.

9330:    MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call and C was obtained from a previous
9331:    call to this function with MAT_INITIAL_MATRIX.

9333:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed.

9335:    If you have many matrices with the same non-zero structure to multiply, you should use MatProductCreate()/MatProductSymbolic(C)/ReplaceMats(), and call MatProductNumeric() repeatedly.

9337:    In the special case where matrix B (and hence C) are dense you can create the correctly sized matrix C yourself and then call this routine with MAT_REUSE_MATRIX, rather than first having MatMatMult() create it for you. You can NEVER do this if the matrix C is sparse.

9339:    Level: intermediate

9341: .seealso: MatTransposeMatMult(), MatMatTransposeMult(), MatPtAP()
9342: @*/
9343: PetscErrorCode MatMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
9344: {

9348:   MatProduct_Private(A,B,scall,fill,MATPRODUCT_AB,C);
9349:   return(0);
9350: }

9352: /*@
9353:    MatMatTransposeMult - Performs Matrix-Matrix Multiplication C=A*B^T.

9355:    Neighbor-wise Collective on Mat

9357:    Input Parameters:
9358: +  A - the left matrix
9359: .  B - the right matrix
9360: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9361: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known

9363:    Output Parameters:
9364: .  C - the product matrix

9366:    Notes:
9367:    C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy().

9369:    MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call

9371:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
9372:    actually needed.

9374:    This routine is currently only implemented for pairs of SeqAIJ matrices, for the SeqDense class,
9375:    and for pairs of MPIDense matrices.

9377:    Options Database Keys:
9378: .  -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorthims for MPIDense matrices: the
9379:                                                                 first redundantly copies the transposed B matrix on each process and requiers O(log P) communication complexity;
9380:                                                                 the second never stores more than one portion of the B matrix at a time by requires O(P) communication complexity.

9382:    Level: intermediate

9384: .seealso: MatMatMult(), MatTransposeMatMult() MatPtAP()
9385: @*/
9386: PetscErrorCode MatMatTransposeMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
9387: {

9391:   MatProduct_Private(A,B,scall,fill,MATPRODUCT_ABt,C);
9392:   return(0);
9393: }

9395: /*@
9396:    MatTransposeMatMult - Performs Matrix-Matrix Multiplication C=A^T*B.

9398:    Neighbor-wise Collective on Mat

9400:    Input Parameters:
9401: +  A - the left matrix
9402: .  B - the right matrix
9403: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9404: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known

9406:    Output Parameters:
9407: .  C - the product matrix

9409:    Notes:
9410:    C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy().

9412:    MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

9414:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
9415:    actually needed.

9417:    This routine is currently implemented for pairs of AIJ matrices and pairs of SeqDense matrices and classes
9418:    which inherit from SeqAIJ.  C will be of same type as the input matrices.

9420:    Level: intermediate

9422: .seealso: MatMatMult(), MatMatTransposeMult(), MatPtAP()
9423: @*/
9424: PetscErrorCode MatTransposeMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
9425: {

9429:   MatProduct_Private(A,B,scall,fill,MATPRODUCT_AtB,C);
9430:   return(0);
9431: }

9433: /*@
9434:    MatMatMatMult - Performs Matrix-Matrix-Matrix Multiplication D=A*B*C.

9436:    Neighbor-wise Collective on Mat

9438:    Input Parameters:
9439: +  A - the left matrix
9440: .  B - the middle matrix
9441: .  C - the right matrix
9442: .  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
9443: -  fill - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use PETSC_DEFAULT if you do not have a good estimate
9444:           if the result is a dense matrix this is irrelevent

9446:    Output Parameters:
9447: .  D - the product matrix

9449:    Notes:
9450:    Unless scall is MAT_REUSE_MATRIX D will be created.

9452:    MAT_REUSE_MATRIX can only be used if the matrices A, B and C have the same nonzero pattern as in the previous call

9454:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
9455:    actually needed.

9457:    If you have many matrices with the same non-zero structure to multiply, you
9458:    should use MAT_REUSE_MATRIX in all calls but the first or

9460:    Level: intermediate

9462: .seealso: MatMatMult, MatPtAP()
9463: @*/
9464: PetscErrorCode MatMatMatMult(Mat A,Mat B,Mat C,MatReuse scall,PetscReal fill,Mat *D)
9465: {

9469:   if (scall == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");

9471:   if (scall == MAT_INITIAL_MATRIX) {
9472:     MatProductCreate(A,B,C,D);
9473:     MatProductSetType(*D,MATPRODUCT_ABC);
9474:     MatProductSetAlgorithm(*D,"default");
9475:     MatProductSetFill(*D,fill);

9477:     (*D)->product->api_user = PETSC_TRUE;
9478:     MatProductSetFromOptions(*D);

9480:     MatProductSymbolic(*D);
9481:   } else { /* user may chage input matrices when REUSE */
9482:     MatProductReplaceMats(A,B,C,*D);
9483:   }

9485:   MatProductNumeric(*D);
9486:   return(0);
9487: }

9489: /*@
9490:    MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

9492:    Collective on Mat

9494:    Input Parameters:
9495: +  mat - the matrix
9496: .  nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
9497: .  subcomm - MPI communicator split from the communicator where mat resides in (or MPI_COMM_NULL if nsubcomm is used)
9498: -  reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

9500:    Output Parameter:
9501: .  matredundant - redundant matrix

9503:    Notes:
9504:    MAT_REUSE_MATRIX can only be used when the nonzero structure of the
9505:    original matrix has not changed from that last call to MatCreateRedundantMatrix().

9507:    This routine creates the duplicated matrices in subcommunicators; you should NOT create them before
9508:    calling it.

9510:    Level: advanced


9513: .seealso: MatDestroy()
9514: @*/
9515: PetscErrorCode MatCreateRedundantMatrix(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,MatReuse reuse,Mat *matredundant)
9516: {
9518:   MPI_Comm       comm;
9519:   PetscMPIInt    size;
9520:   PetscInt       mloc_sub,nloc_sub,rstart,rend,M=mat->rmap->N,N=mat->cmap->N,bs=mat->rmap->bs;
9521:   Mat_Redundant  *redund=NULL;
9522:   PetscSubcomm   psubcomm=NULL;
9523:   MPI_Comm       subcomm_in=subcomm;
9524:   Mat            *matseq;
9525:   IS             isrow,iscol;
9526:   PetscBool      newsubcomm=PETSC_FALSE;

9530:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
9533:   }

9535:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
9536:   if (size == 1 || nsubcomm == 1) {
9537:     if (reuse == MAT_INITIAL_MATRIX) {
9538:       MatDuplicate(mat,MAT_COPY_VALUES,matredundant);
9539:     } else {
9540:       if (*matredundant == mat) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
9541:       MatCopy(mat,*matredundant,SAME_NONZERO_PATTERN);
9542:     }
9543:     return(0);
9544:   }

9546:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9547:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9548:   MatCheckPreallocated(mat,1);

9550:   PetscLogEventBegin(MAT_RedundantMat,mat,0,0,0);
9551:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
9552:     /* create psubcomm, then get subcomm */
9553:     PetscObjectGetComm((PetscObject)mat,&comm);
9554:     MPI_Comm_size(comm,&size);
9555:     if (nsubcomm < 1 || nsubcomm > size) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"nsubcomm must between 1 and %D",size);

9557:     PetscSubcommCreate(comm,&psubcomm);
9558:     PetscSubcommSetNumber(psubcomm,nsubcomm);
9559:     PetscSubcommSetType(psubcomm,PETSC_SUBCOMM_CONTIGUOUS);
9560:     PetscSubcommSetFromOptions(psubcomm);
9561:     PetscCommDuplicate(PetscSubcommChild(psubcomm),&subcomm,NULL);
9562:     newsubcomm = PETSC_TRUE;
9563:     PetscSubcommDestroy(&psubcomm);
9564:   }

9566:   /* get isrow, iscol and a local sequential matrix matseq[0] */
9567:   if (reuse == MAT_INITIAL_MATRIX) {
9568:     mloc_sub = PETSC_DECIDE;
9569:     nloc_sub = PETSC_DECIDE;
9570:     if (bs < 1) {
9571:       PetscSplitOwnership(subcomm,&mloc_sub,&M);
9572:       PetscSplitOwnership(subcomm,&nloc_sub,&N);
9573:     } else {
9574:       PetscSplitOwnershipBlock(subcomm,bs,&mloc_sub,&M);
9575:       PetscSplitOwnershipBlock(subcomm,bs,&nloc_sub,&N);
9576:     }
9577:     MPI_Scan(&mloc_sub,&rend,1,MPIU_INT,MPI_SUM,subcomm);
9578:     rstart = rend - mloc_sub;
9579:     ISCreateStride(PETSC_COMM_SELF,mloc_sub,rstart,1,&isrow);
9580:     ISCreateStride(PETSC_COMM_SELF,N,0,1,&iscol);
9581:   } else { /* reuse == MAT_REUSE_MATRIX */
9582:     if (*matredundant == mat) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
9583:     /* retrieve subcomm */
9584:     PetscObjectGetComm((PetscObject)(*matredundant),&subcomm);
9585:     redund = (*matredundant)->redundant;
9586:     isrow  = redund->isrow;
9587:     iscol  = redund->iscol;
9588:     matseq = redund->matseq;
9589:   }
9590:   MatCreateSubMatrices(mat,1,&isrow,&iscol,reuse,&matseq);

9592:   /* get matredundant over subcomm */
9593:   if (reuse == MAT_INITIAL_MATRIX) {
9594:     MatCreateMPIMatConcatenateSeqMat(subcomm,matseq[0],nloc_sub,reuse,matredundant);

9596:     /* create a supporting struct and attach it to C for reuse */
9597:     PetscNewLog(*matredundant,&redund);
9598:     (*matredundant)->redundant = redund;
9599:     redund->isrow              = isrow;
9600:     redund->iscol              = iscol;
9601:     redund->matseq             = matseq;
9602:     if (newsubcomm) {
9603:       redund->subcomm          = subcomm;
9604:     } else {
9605:       redund->subcomm          = MPI_COMM_NULL;
9606:     }
9607:   } else {
9608:     MatCreateMPIMatConcatenateSeqMat(subcomm,matseq[0],PETSC_DECIDE,reuse,matredundant);
9609:   }
9610:   PetscLogEventEnd(MAT_RedundantMat,mat,0,0,0);
9611:   return(0);
9612: }

9614: /*@C
9615:    MatGetMultiProcBlock - Create multiple [bjacobi] 'parallel submatrices' from
9616:    a given 'mat' object. Each submatrix can span multiple procs.

9618:    Collective on Mat

9620:    Input Parameters:
9621: +  mat - the matrix
9622: .  subcomm - the subcommunicator obtained by com_split(comm)
9623: -  scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

9625:    Output Parameter:
9626: .  subMat - 'parallel submatrices each spans a given subcomm

9628:   Notes:
9629:   The submatrix partition across processors is dictated by 'subComm' a
9630:   communicator obtained by com_split(comm). The comm_split
9631:   is not restriced to be grouped with consecutive original ranks.

9633:   Due the comm_split() usage, the parallel layout of the submatrices
9634:   map directly to the layout of the original matrix [wrt the local
9635:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
9636:   into the 'DiagonalMat' of the subMat, hence it is used directly from
9637:   the subMat. However the offDiagMat looses some columns - and this is
9638:   reconstructed with MatSetValues()

9640:   Level: advanced


9643: .seealso: MatCreateSubMatrices()
9644: @*/
9645: PetscErrorCode   MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall,Mat *subMat)
9646: {
9648:   PetscMPIInt    commsize,subCommSize;

9651:   MPI_Comm_size(PetscObjectComm((PetscObject)mat),&commsize);
9652:   MPI_Comm_size(subComm,&subCommSize);
9653:   if (subCommSize > commsize) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"CommSize %D < SubCommZize %D",commsize,subCommSize);

9655:   if (scall == MAT_REUSE_MATRIX && *subMat == mat) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
9656:   PetscLogEventBegin(MAT_GetMultiProcBlock,mat,0,0,0);
9657:   (*mat->ops->getmultiprocblock)(mat,subComm,scall,subMat);
9658:   PetscLogEventEnd(MAT_GetMultiProcBlock,mat,0,0,0);
9659:   return(0);
9660: }

9662: /*@
9663:    MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

9665:    Not Collective

9667:    Input Arguments:
9668: +  mat - matrix to extract local submatrix from
9669: .  isrow - local row indices for submatrix
9670: -  iscol - local column indices for submatrix

9672:    Output Arguments:
9673: .  submat - the submatrix

9675:    Level: intermediate

9677:    Notes:
9678:    The submat should be returned with MatRestoreLocalSubMatrix().

9680:    Depending on the format of mat, the returned submat may not implement MatMult().  Its communicator may be
9681:    the same as mat, it may be PETSC_COMM_SELF, or some other subcomm of mat's.

9683:    The submat always implements MatSetValuesLocal().  If isrow and iscol have the same block size, then
9684:    MatSetValuesBlockedLocal() will also be implemented.

9686:    The mat must have had a ISLocalToGlobalMapping provided to it with MatSetLocalToGlobalMapping(). Note that
9687:    matrices obtained with DMCreateMatrix() generally already have the local to global mapping provided.

9689: .seealso: MatRestoreLocalSubMatrix(), MatCreateLocalRef(), MatSetLocalToGlobalMapping()
9690: @*/
9691: PetscErrorCode MatGetLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat)
9692: {

9701:   if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must have local to global mapping provided before this call");

9703:   if (mat->ops->getlocalsubmatrix) {
9704:     (*mat->ops->getlocalsubmatrix)(mat,isrow,iscol,submat);
9705:   } else {
9706:     MatCreateLocalRef(mat,isrow,iscol,submat);
9707:   }
9708:   return(0);
9709: }

9711: /*@
9712:    MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering

9714:    Not Collective

9716:    Input Arguments:
9717:    mat - matrix to extract local submatrix from
9718:    isrow - local row indices for submatrix
9719:    iscol - local column indices for submatrix
9720:    submat - the submatrix

9722:    Level: intermediate

9724: .seealso: MatGetLocalSubMatrix()
9725: @*/
9726: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat)
9727: {

9736:   if (*submat) {
9738:   }

9740:   if (mat->ops->restorelocalsubmatrix) {
9741:     (*mat->ops->restorelocalsubmatrix)(mat,isrow,iscol,submat);
9742:   } else {
9743:     MatDestroy(submat);
9744:   }
9745:   *submat = NULL;
9746:   return(0);
9747: }

9749: /* --------------------------------------------------------*/
9750: /*@
9751:    MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

9753:    Collective on Mat

9755:    Input Parameter:
9756: .  mat - the matrix

9758:    Output Parameter:
9759: .  is - if any rows have zero diagonals this contains the list of them

9761:    Level: developer

9763: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
9764: @*/
9765: PetscErrorCode MatFindZeroDiagonals(Mat mat,IS *is)
9766: {

9772:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9773:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9775:   if (!mat->ops->findzerodiagonals) {
9776:     Vec                diag;
9777:     const PetscScalar *a;
9778:     PetscInt          *rows;
9779:     PetscInt           rStart, rEnd, r, nrow = 0;

9781:     MatCreateVecs(mat, &diag, NULL);
9782:     MatGetDiagonal(mat, diag);
9783:     MatGetOwnershipRange(mat, &rStart, &rEnd);
9784:     VecGetArrayRead(diag, &a);
9785:     for (r = 0; r < rEnd-rStart; ++r) if (a[r] == 0.0) ++nrow;
9786:     PetscMalloc1(nrow, &rows);
9787:     nrow = 0;
9788:     for (r = 0; r < rEnd-rStart; ++r) if (a[r] == 0.0) rows[nrow++] = r+rStart;
9789:     VecRestoreArrayRead(diag, &a);
9790:     VecDestroy(&diag);
9791:     ISCreateGeneral(PetscObjectComm((PetscObject) mat), nrow, rows, PETSC_OWN_POINTER, is);
9792:   } else {
9793:     (*mat->ops->findzerodiagonals)(mat, is);
9794:   }
9795:   return(0);
9796: }

9798: /*@
9799:    MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

9801:    Collective on Mat

9803:    Input Parameter:
9804: .  mat - the matrix

9806:    Output Parameter:
9807: .  is - contains the list of rows with off block diagonal entries

9809:    Level: developer

9811: .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd()
9812: @*/
9813: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat,IS *is)
9814: {

9820:   if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9821:   if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

9823:   if (!mat->ops->findoffblockdiagonalentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s does not have a find off block diagonal entries defined",((PetscObject)mat)->type_name);
9824:   (*mat->ops->findoffblockdiagonalentries)(mat,is);
9825:   return(0);
9826: }

9828: /*@C
9829:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

9831:   Collective on Mat

9833:   Input Parameters:
9834: . mat - the matrix

9836:   Output Parameters:
9837: . values - the block inverses in column major order (FORTRAN-like)

9839:    Note:
9840:    This routine is not available from Fortran.

9842:   Level: advanced

9844: .seealso: MatInvertBockDiagonalMat
9845: @*/
9846: PetscErrorCode MatInvertBlockDiagonal(Mat mat,const PetscScalar **values)
9847: {

9852:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9853:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9854:   if (!mat->ops->invertblockdiagonal) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for type %s",((PetscObject)mat)->type_name);
9855:   (*mat->ops->invertblockdiagonal)(mat,values);
9856:   return(0);
9857: }

9859: /*@C
9860:   MatInvertVariableBlockDiagonal - Inverts the block diagonal entries.

9862:   Collective on Mat

9864:   Input Parameters:
9865: + mat - the matrix
9866: . nblocks - the number of blocks
9867: - bsizes - the size of each block

9869:   Output Parameters:
9870: . values - the block inverses in column major order (FORTRAN-like)

9872:    Note:
9873:    This routine is not available from Fortran.

9875:   Level: advanced

9877: .seealso: MatInvertBockDiagonal()
9878: @*/
9879: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *values)
9880: {

9885:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
9886:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
9887:   if (!mat->ops->invertvariableblockdiagonal) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for type",((PetscObject)mat)->type_name);
9888:   (*mat->ops->invertvariableblockdiagonal)(mat,nblocks,bsizes,values);
9889:   return(0);
9890: }

9892: /*@
9893:   MatInvertBlockDiagonalMat - set matrix C to be the inverted block diagonal of matrix A

9895:   Collective on Mat

9897:   Input Parameters:
9898: . A - the matrix

9900:   Output Parameters:
9901: . C - matrix with inverted block diagonal of A.  This matrix should be created and may have its type set.

9903:   Notes: the blocksize of the matrix is used to determine the blocks on the diagonal of C

9905:   Level: advanced

9907: .seealso: MatInvertBockDiagonal()
9908: @*/
9909: PetscErrorCode MatInvertBlockDiagonalMat(Mat A,Mat C)
9910: {
9911:   PetscErrorCode     ierr;
9912:   const PetscScalar *vals;
9913:   PetscInt          *dnnz;
9914:   PetscInt           M,N,m,n,rstart,rend,bs,i,j;

9917:   MatInvertBlockDiagonal(A,&vals);
9918:   MatGetBlockSize(A,&bs);
9919:   MatGetSize(A,&M,&N);
9920:   MatGetLocalSize(A,&m,&n);
9921:   MatSetSizes(C,m,n,M,N);
9922:   MatSetBlockSize(C,bs);
9923:   PetscMalloc1(m/bs,&dnnz);
9924:   for (j = 0; j < m/bs; j++) dnnz[j] = 1;
9925:   MatXAIJSetPreallocation(C,bs,dnnz,NULL,NULL,NULL);
9926:   PetscFree(dnnz);
9927:   MatGetOwnershipRange(C,&rstart,&rend);
9928:   MatSetOption(C,MAT_ROW_ORIENTED,PETSC_FALSE);
9929:   for (i = rstart/bs; i < rend/bs; i++) {
9930:     MatSetValuesBlocked(C,1,&i,1,&i,&vals[(i-rstart/bs)*bs*bs],INSERT_VALUES);
9931:   }
9932:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
9933:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
9934:   MatSetOption(C,MAT_ROW_ORIENTED,PETSC_TRUE);
9935:   return(0);
9936: }

9938: /*@C
9939:     MatTransposeColoringDestroy - Destroys a coloring context for matrix product C=A*B^T that was created
9940:     via MatTransposeColoringCreate().

9942:     Collective on MatTransposeColoring

9944:     Input Parameter:
9945: .   c - coloring context

9947:     Level: intermediate

9949: .seealso: MatTransposeColoringCreate()
9950: @*/
9951: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
9952: {
9953:   PetscErrorCode       ierr;
9954:   MatTransposeColoring matcolor=*c;

9957:   if (!matcolor) return(0);
9958:   if (--((PetscObject)matcolor)->refct > 0) {matcolor = 0; return(0);}

9960:   PetscFree3(matcolor->ncolumns,matcolor->nrows,matcolor->colorforrow);
9961:   PetscFree(matcolor->rows);
9962:   PetscFree(matcolor->den2sp);
9963:   PetscFree(matcolor->colorforcol);
9964:   PetscFree(matcolor->columns);
9965:   if (matcolor->brows>0) {
9966:     PetscFree(matcolor->lstart);
9967:   }
9968:   PetscHeaderDestroy(c);
9969:   return(0);
9970: }

9972: /*@C
9973:     MatTransColoringApplySpToDen - Given a symbolic matrix product C=A*B^T for which
9974:     a MatTransposeColoring context has been created, computes a dense B^T by Apply
9975:     MatTransposeColoring to sparse B.

9977:     Collective on MatTransposeColoring

9979:     Input Parameters:
9980: +   B - sparse matrix B
9981: .   Btdense - symbolic dense matrix B^T
9982: -   coloring - coloring context created with MatTransposeColoringCreate()

9984:     Output Parameter:
9985: .   Btdense - dense matrix B^T

9987:     Level: advanced

9989:      Notes:
9990:     These are used internally for some implementations of MatRARt()

9992: .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy(), MatTransColoringApplyDenToSp()

9994: @*/
9995: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring,Mat B,Mat Btdense)
9996: {


10004:   if (!B->ops->transcoloringapplysptoden) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)B)->type_name);
10005:   (B->ops->transcoloringapplysptoden)(coloring,B,Btdense);
10006:   return(0);
10007: }

10009: /*@C
10010:     MatTransColoringApplyDenToSp - Given a symbolic matrix product Csp=A*B^T for which
10011:     a MatTransposeColoring context has been created and a dense matrix Cden=A*Btdense
10012:     in which Btdens is obtained from MatTransColoringApplySpToDen(), recover sparse matrix
10013:     Csp from Cden.

10015:     Collective on MatTransposeColoring

10017:     Input Parameters:
10018: +   coloring - coloring context created with MatTransposeColoringCreate()
10019: -   Cden - matrix product of a sparse matrix and a dense matrix Btdense

10021:     Output Parameter:
10022: .   Csp - sparse matrix

10024:     Level: advanced

10026:      Notes:
10027:     These are used internally for some implementations of MatRARt()

10029: .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy(), MatTransColoringApplySpToDen()

10031: @*/
10032: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring,Mat Cden,Mat Csp)
10033: {


10041:   if (!Csp->ops->transcoloringapplydentosp) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)Csp)->type_name);
10042:   (Csp->ops->transcoloringapplydentosp)(matcoloring,Cden,Csp);
10043:   MatAssemblyBegin(Csp,MAT_FINAL_ASSEMBLY);
10044:   MatAssemblyEnd(Csp,MAT_FINAL_ASSEMBLY);
10045:   return(0);
10046: }

10048: /*@C
10049:    MatTransposeColoringCreate - Creates a matrix coloring context for matrix product C=A*B^T.

10051:    Collective on Mat

10053:    Input Parameters:
10054: +  mat - the matrix product C
10055: -  iscoloring - the coloring of the matrix; usually obtained with MatColoringCreate() or DMCreateColoring()

10057:     Output Parameter:
10058: .   color - the new coloring context

10060:     Level: intermediate

10062: .seealso: MatTransposeColoringDestroy(),  MatTransColoringApplySpToDen(),
10063:            MatTransColoringApplyDenToSp()
10064: @*/
10065: PetscErrorCode MatTransposeColoringCreate(Mat mat,ISColoring iscoloring,MatTransposeColoring *color)
10066: {
10067:   MatTransposeColoring c;
10068:   MPI_Comm             comm;
10069:   PetscErrorCode       ierr;

10072:   PetscLogEventBegin(MAT_TransposeColoringCreate,mat,0,0,0);
10073:   PetscObjectGetComm((PetscObject)mat,&comm);
10074:   PetscHeaderCreate(c,MAT_TRANSPOSECOLORING_CLASSID,"MatTransposeColoring","Matrix product C=A*B^T via coloring","Mat",comm,MatTransposeColoringDestroy,NULL);

10076:   c->ctype = iscoloring->ctype;
10077:   if (mat->ops->transposecoloringcreate) {
10078:     (*mat->ops->transposecoloringcreate)(mat,iscoloring,c);
10079:   } else SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Code not yet written for matrix type %s",((PetscObject)mat)->type_name);

10081:   *color = c;
10082:   PetscLogEventEnd(MAT_TransposeColoringCreate,mat,0,0,0);
10083:   return(0);
10084: }

10086: /*@
10087:       MatGetNonzeroState - Returns a 64 bit integer representing the current state of nonzeros in the matrix. If the
10088:         matrix has had no new nonzero locations added to the matrix since the previous call then the value will be the
10089:         same, otherwise it will be larger

10091:      Not Collective

10093:   Input Parameter:
10094: .    A  - the matrix

10096:   Output Parameter:
10097: .    state - the current state

10099:   Notes:
10100:     You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10101:          different matrices

10103:   Level: intermediate

10105: @*/
10106: PetscErrorCode MatGetNonzeroState(Mat mat,PetscObjectState *state)
10107: {
10110:   *state = mat->nonzerostate;
10111:   return(0);
10112: }

10114: /*@
10115:       MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10116:                  matrices from each processor

10118:     Collective

10120:    Input Parameters:
10121: +    comm - the communicators the parallel matrix will live on
10122: .    seqmat - the input sequential matrices
10123: .    n - number of local columns (or PETSC_DECIDE)
10124: -    reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX

10126:    Output Parameter:
10127: .    mpimat - the parallel matrix generated

10129:     Level: advanced

10131:    Notes:
10132:     The number of columns of the matrix in EACH processor MUST be the same.

10134: @*/
10135: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm,Mat seqmat,PetscInt n,MatReuse reuse,Mat *mpimat)
10136: {

10140:   if (!seqmat->ops->creatempimatconcatenateseqmat) SETERRQ1(PetscObjectComm((PetscObject)seqmat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)seqmat)->type_name);
10141:   if (reuse == MAT_REUSE_MATRIX && seqmat == *mpimat) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");

10143:   PetscLogEventBegin(MAT_Merge,seqmat,0,0,0);
10144:   (*seqmat->ops->creatempimatconcatenateseqmat)(comm,seqmat,n,reuse,mpimat);
10145:   PetscLogEventEnd(MAT_Merge,seqmat,0,0,0);
10146:   return(0);
10147: }

10149: /*@
10150:      MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent
10151:                  ranks' ownership ranges.

10153:     Collective on A

10155:    Input Parameters:
10156: +    A   - the matrix to create subdomains from
10157: -    N   - requested number of subdomains


10160:    Output Parameters:
10161: +    n   - number of subdomains resulting on this rank
10162: -    iss - IS list with indices of subdomains on this rank

10164:     Level: advanced

10166:     Notes:
10167:     number of subdomains must be smaller than the communicator size
10168: @*/
10169: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A,PetscInt N,PetscInt *n,IS *iss[])
10170: {
10171:   MPI_Comm        comm,subcomm;
10172:   PetscMPIInt     size,rank,color;
10173:   PetscInt        rstart,rend,k;
10174:   PetscErrorCode  ierr;

10177:   PetscObjectGetComm((PetscObject)A,&comm);
10178:   MPI_Comm_size(comm,&size);
10179:   MPI_Comm_rank(comm,&rank);
10180:   if (N < 1 || N >= (PetscInt)size) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"number of subdomains must be > 0 and < %D, got N = %D",size,N);
10181:   *n = 1;
10182:   k = ((PetscInt)size)/N + ((PetscInt)size%N>0); /* There are up to k ranks to a color */
10183:   color = rank/k;
10184:   MPI_Comm_split(comm,color,rank,&subcomm);
10185:   PetscMalloc1(1,iss);
10186:   MatGetOwnershipRange(A,&rstart,&rend);
10187:   ISCreateStride(subcomm,rend-rstart,rstart,1,iss[0]);
10188:   MPI_Comm_free(&subcomm);
10189:   return(0);
10190: }

10192: /*@
10193:    MatGalerkin - Constructs the coarse grid problem via Galerkin projection.

10195:    If the interpolation and restriction operators are the same, uses MatPtAP.
10196:    If they are not the same, use MatMatMatMult.

10198:    Once the coarse grid problem is constructed, correct for interpolation operators
10199:    that are not of full rank, which can legitimately happen in the case of non-nested
10200:    geometric multigrid.

10202:    Input Parameters:
10203: +  restrct - restriction operator
10204: .  dA - fine grid matrix
10205: .  interpolate - interpolation operator
10206: .  reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
10207: -  fill - expected fill, use PETSC_DEFAULT if you do not have a good estimate

10209:    Output Parameters:
10210: .  A - the Galerkin coarse matrix

10212:    Options Database Key:
10213: .  -pc_mg_galerkin <both,pmat,mat,none>

10215:    Level: developer

10217: .seealso: MatPtAP(), MatMatMatMult()
10218: @*/
10219: PetscErrorCode  MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
10220: {
10222:   IS             zerorows;
10223:   Vec            diag;

10226:   if (reuse == MAT_INPLACE_MATRIX) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Inplace product not supported");
10227:   /* Construct the coarse grid matrix */
10228:   if (interpolate == restrct) {
10229:     MatPtAP(dA,interpolate,reuse,fill,A);
10230:   } else {
10231:     MatMatMatMult(restrct,dA,interpolate,reuse,fill,A);
10232:   }

10234:   /* If the interpolation matrix is not of full rank, A will have zero rows.
10235:      This can legitimately happen in the case of non-nested geometric multigrid.
10236:      In that event, we set the rows of the matrix to the rows of the identity,
10237:      ignoring the equations (as the RHS will also be zero). */

10239:   MatFindZeroRows(*A, &zerorows);

10241:   if (zerorows != NULL) { /* if there are any zero rows */
10242:     MatCreateVecs(*A, &diag, NULL);
10243:     MatGetDiagonal(*A, diag);
10244:     VecISSet(diag, zerorows, 1.0);
10245:     MatDiagonalSet(*A, diag, INSERT_VALUES);
10246:     VecDestroy(&diag);
10247:     ISDestroy(&zerorows);
10248:   }
10249:   return(0);
10250: }

10252: /*@C
10253:     MatSetOperation - Allows user to set a matrix operation for any matrix type

10255:    Logically Collective on Mat

10257:     Input Parameters:
10258: +   mat - the matrix
10259: .   op - the name of the operation
10260: -   f - the function that provides the operation

10262:    Level: developer

10264:     Usage:
10265: $      extern PetscErrorCode usermult(Mat,Vec,Vec);
10266: $      MatCreateXXX(comm,...&A);
10267: $      MatSetOperation(A,MATOP_MULT,(void(*)(void))usermult);

10269:     Notes:
10270:     See the file include/petscmat.h for a complete list of matrix
10271:     operations, which all have the form MATOP_<OPERATION>, where
10272:     <OPERATION> is the name (in all capital letters) of the
10273:     user interface routine (e.g., MatMult() -> MATOP_MULT).

10275:     All user-provided functions (except for MATOP_DESTROY) should have the same calling
10276:     sequence as the usual matrix interface routines, since they
10277:     are intended to be accessed via the usual matrix interface
10278:     routines, e.g.,
10279: $       MatMult(Mat,Vec,Vec) -> usermult(Mat,Vec,Vec)

10281:     In particular each function MUST return an error code of 0 on success and
10282:     nonzero on failure.

10284:     This routine is distinct from MatShellSetOperation() in that it can be called on any matrix type.

10286: .seealso: MatGetOperation(), MatCreateShell(), MatShellSetContext(), MatShellSetOperation()
10287: @*/
10288: PetscErrorCode MatSetOperation(Mat mat,MatOperation op,void (*f)(void))
10289: {
10292:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))(mat->ops->view)) {
10293:     mat->ops->viewnative = mat->ops->view;
10294:   }
10295:   (((void(**)(void))mat->ops)[op]) = f;
10296:   return(0);
10297: }

10299: /*@C
10300:     MatGetOperation - Gets a matrix operation for any matrix type.

10302:     Not Collective

10304:     Input Parameters:
10305: +   mat - the matrix
10306: -   op - the name of the operation

10308:     Output Parameter:
10309: .   f - the function that provides the operation

10311:     Level: developer

10313:     Usage:
10314: $      PetscErrorCode (*usermult)(Mat,Vec,Vec);
10315: $      MatGetOperation(A,MATOP_MULT,(void(**)(void))&usermult);

10317:     Notes:
10318:     See the file include/petscmat.h for a complete list of matrix
10319:     operations, which all have the form MATOP_<OPERATION>, where
10320:     <OPERATION> is the name (in all capital letters) of the
10321:     user interface routine (e.g., MatMult() -> MATOP_MULT).

10323:     This routine is distinct from MatShellGetOperation() in that it can be called on any matrix type.

10325: .seealso: MatSetOperation(), MatCreateShell(), MatShellGetContext(), MatShellGetOperation()
10326: @*/
10327: PetscErrorCode MatGetOperation(Mat mat,MatOperation op,void(**f)(void))
10328: {
10331:   *f = (((void (**)(void))mat->ops)[op]);
10332:   return(0);
10333: }

10335: /*@
10336:     MatHasOperation - Determines whether the given matrix supports the particular
10337:     operation.

10339:    Not Collective

10341:    Input Parameters:
10342: +  mat - the matrix
10343: -  op - the operation, for example, MATOP_GET_DIAGONAL

10345:    Output Parameter:
10346: .  has - either PETSC_TRUE or PETSC_FALSE

10348:    Level: advanced

10350:    Notes:
10351:    See the file include/petscmat.h for a complete list of matrix
10352:    operations, which all have the form MATOP_<OPERATION>, where
10353:    <OPERATION> is the name (in all capital letters) of the
10354:    user-level routine.  E.g., MatNorm() -> MATOP_NORM.

10356: .seealso: MatCreateShell()
10357: @*/
10358: PetscErrorCode MatHasOperation(Mat mat,MatOperation op,PetscBool *has)
10359: {

10366:   if (mat->ops->hasoperation) {
10367:     (*mat->ops->hasoperation)(mat,op,has);
10368:   } else {
10369:     if (((void**)mat->ops)[op]) *has =  PETSC_TRUE;
10370:     else {
10371:       *has = PETSC_FALSE;
10372:       if (op == MATOP_CREATE_SUBMATRIX) {
10373:         PetscMPIInt size;

10375:         MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
10376:         if (size == 1) {
10377:           MatHasOperation(mat,MATOP_CREATE_SUBMATRICES,has);
10378:         }
10379:       }
10380:     }
10381:   }
10382:   return(0);
10383: }

10385: /*@
10386:     MatHasCongruentLayouts - Determines whether the rows and columns layouts
10387:     of the matrix are congruent

10389:    Collective on mat

10391:    Input Parameters:
10392: .  mat - the matrix

10394:    Output Parameter:
10395: .  cong - either PETSC_TRUE or PETSC_FALSE

10397:    Level: beginner

10399:    Notes:

10401: .seealso: MatCreate(), MatSetSizes()
10402: @*/
10403: PetscErrorCode MatHasCongruentLayouts(Mat mat,PetscBool *cong)
10404: {

10411:   if (!mat->rmap || !mat->cmap) {
10412:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
10413:     return(0);
10414:   }
10415:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
10416:     PetscLayoutCompare(mat->rmap,mat->cmap,cong);
10417:     if (*cong) mat->congruentlayouts = 1;
10418:     else       mat->congruentlayouts = 0;
10419:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
10420:   return(0);
10421: }

10423: /*@
10424:     MatFreeIntermediateDataStructures - Free intermediate data structures created for reuse,
10425:     e.g., matrx product of MatPtAP.

10427:    Collective on mat

10429:    Input Parameters:
10430: .  mat - the matrix

10432:    Output Parameter:
10433: .  mat - the matrix with intermediate data structures released

10435:    Level: advanced

10437:    Notes:

10439: .seealso: MatPtAP(), MatMatMult()
10440: @*/
10441: PetscErrorCode MatFreeIntermediateDataStructures(Mat mat)
10442: {

10448:   if (mat->ops->freeintermediatedatastructures) {
10449:     (*mat->ops->freeintermediatedatastructures)(mat);
10450:   }
10451:   return(0);
10452: }