/* This is where the abstract matrix operations are defined */ #include /*I "petscmat.h" I*/ #include /* Logging support */ PetscClassId MAT_CLASSID; PetscClassId MAT_FDCOLORING_CLASSID; PetscClassId MAT_TRANSPOSECOLORING_CLASSID; PetscLogEvent MAT_Mult, MAT_Mults, MAT_MultConstrained, MAT_MultAdd, MAT_MultTranspose; PetscLogEvent MAT_MultTransposeConstrained, MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve; PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic; PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor; PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin; PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_GetSubMatrices, MAT_GetColoring, MAT_GetOrdering, MAT_GetRedundantMatrix, MAT_GetSeqNonzeroStructure; PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate; PetscLogEvent MAT_FDColoringApply,MAT_Transpose,MAT_FDColoringFunction; PetscLogEvent MAT_TransposeColoringCreate; PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric; PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric,MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric; PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric; PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric; PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric; PetscLogEvent MAT_MultHermitianTranspose,MAT_MultHermitianTransposeAdd; PetscLogEvent MAT_Getsymtranspose, MAT_Getsymtransreduced, MAT_Transpose_SeqAIJ, MAT_GetBrowsOfAcols; PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym; PetscLogEvent MAT_Applypapt, MAT_Applypapt_numeric, MAT_Applypapt_symbolic, MAT_GetSequentialNonzeroStructure; PetscLogEvent MAT_GetMultiProcBlock; PetscLogEvent MAT_CUSPCopyToGPU, MAT_CUSPARSECopyToGPU, MAT_SetValuesBatch, MAT_SetValuesBatchI, MAT_SetValuesBatchII, MAT_SetValuesBatchIII, MAT_SetValuesBatchIV; PetscLogEvent MAT_Merge; const char *const MatFactorTypes[] = {"NONE","LU","CHOLESKY","ILU","ICC","ILUDT","MatFactorType","MAT_FACTOR_",0}; #undef __FUNCT__ #define __FUNCT__ "MatSetRandom" /*@ MatSetRandom - Sets all components of a matrix to random numbers. For sparse matrices that have been preallocated it randomly selects appropriate locations Logically Collective on Vec Input Parameters: + x - the vector - rctx - the random number context, formed by PetscRandomCreate(), or PETSC_NULL and it will create one internally. Output Parameter: . x - the vector Example of Usage: .vb PetscRandomCreate(PETSC_COMM_WORLD,&rctx); VecSetRandom(x,rctx); PetscRandomDestroy(rctx); .ve Level: intermediate Concepts: vector^setting to random Concepts: random^vector .seealso: MatZeroEntries(), MatSetValues(), PetscRandomCreate(), PetscRandomDestroy() @*/ PetscErrorCode MatSetRandom(Mat x,PetscRandom rctx) { PetscErrorCode ierr; PetscRandom randObj = PETSC_NULL; PetscFunctionBegin; PetscValidHeaderSpecific(x,MAT_CLASSID,1); if (rctx) PetscValidHeaderSpecific(rctx,PETSC_RANDOM_CLASSID,2); PetscValidType(x,1); if (!rctx) { MPI_Comm comm; ierr = PetscObjectGetComm((PetscObject)x,&comm);CHKERRQ(ierr); ierr = PetscRandomCreate(comm,&randObj);CHKERRQ(ierr); ierr = PetscRandomSetFromOptions(randObj);CHKERRQ(ierr); rctx = randObj; } ierr = PetscLogEventBegin(VEC_SetRandom,x,rctx,0,0);CHKERRQ(ierr); ierr = (*x->ops->setrandom)(x,rctx);CHKERRQ(ierr); ierr = PetscLogEventEnd(VEC_SetRandom,x,rctx,0,0);CHKERRQ(ierr); x->assembled = PETSC_TRUE; ierr = PetscRandomDestroy(&randObj);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatFindNonzeroRows" /*@ MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix Input Parameter: . A - the matrix Output Parameter: . keptrows - the rows that are not completely zero Level: intermediate @*/ PetscErrorCode MatFindNonzeroRows(Mat mat,IS *keptrows) { PetscErrorCode ierr; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->findnonzerorows) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Not coded for this matrix type"); ierr = (*mat->ops->findnonzerorows)(mat,keptrows);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetDiagonalBlock" /*@ MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling Not Collective Input Parameters: . A - the matrix Output Parameters: . a - the diagonal part (which is a SEQUENTIAL matrix) Notes: see the manual page for MatCreateAIJ() for more information on the "diagonal part" of the matrix. Level: advanced @*/ PetscErrorCode MatGetDiagonalBlock(Mat A,Mat *a) { PetscErrorCode ierr,(*f)(Mat,Mat*); PetscMPIInt size; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); PetscValidPointer(a,3); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); ierr = MPI_Comm_size(((PetscObject)A)->comm,&size);CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)A,"MatGetDiagonalBlock_C",(void (**)(void))&f);CHKERRQ(ierr); if (f) { ierr = (*f)(A,a);CHKERRQ(ierr); PetscFunctionReturn(0); } else if (size == 1) { *a = A; } else { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"Matrix type %s does not support getting diagonal block",mattype); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetTrace" /*@ MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries. Collective on Mat Input Parameters: . mat - the matrix Output Parameter: . trace - the sum of the diagonal entries Level: advanced @*/ PetscErrorCode MatGetTrace(Mat mat,PetscScalar *trace) { PetscErrorCode ierr; Vec diag; PetscFunctionBegin; ierr = MatGetVecs(mat,&diag,PETSC_NULL);CHKERRQ(ierr); ierr = MatGetDiagonal(mat,diag);CHKERRQ(ierr); ierr = VecSum(diag,trace);CHKERRQ(ierr); ierr = VecDestroy(&diag);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRealPart" /*@ MatRealPart - Zeros out the imaginary part of the matrix Logically Collective on Mat Input Parameters: . mat - the matrix Level: advanced .seealso: MatImaginaryPart() @*/ PetscErrorCode MatRealPart(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->realpart) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->realpart)(mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetGhosts" /*@C MatGetGhosts - Get the global index of all ghost nodes defined by the sparse matrix Collective on Mat Input Parameter: . mat - the matrix Output Parameters: + nghosts - number of ghosts (note for BAIJ matrices there is one ghost for each block) - ghosts - the global indices of the ghost points Notes: the nghosts and ghosts are suitable to pass into VecCreateGhost() Level: advanced @*/ PetscErrorCode MatGetGhosts(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getghosts) { if (nghosts) *nghosts = 0; if (ghosts) *ghosts = 0; } else { ierr = (*mat->ops->getghosts)(mat,nghosts,ghosts);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatImaginaryPart" /*@ MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part Logically Collective on Mat Input Parameters: . mat - the matrix Level: advanced .seealso: MatRealPart() @*/ PetscErrorCode MatImaginaryPart(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->imaginarypart) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->imaginarypart)(mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMissingDiagonal" /*@ MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for BAIJ matrices) Collective on Mat Input Parameter: . mat - the matrix Output Parameters: + missing - is any diagonal missing - dd - first diagonal entry that is missing (optional) Level: advanced .seealso: MatRealPart() @*/ PetscErrorCode MatMissingDiagonal(Mat mat,PetscBool *missing,PetscInt *dd) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->missingdiagonal) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = (*mat->ops->missingdiagonal)(mat,missing,dd);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRow" /*@C MatGetRow - Gets a row of a matrix. You MUST call MatRestoreRow() for each row that you get to ensure that your application does not bleed memory. Not Collective Input Parameters: + mat - the matrix - row - the row to get Output Parameters: + ncols - if not NULL, the number of nonzeros in the row . cols - if not NULL, the column numbers - vals - if not NULL, the values Notes: This routine is provided for people who need to have direct access to the structure of a matrix. We hope that we provide enough high-level matrix routines that few users will need it. MatGetRow() always returns 0-based column indices, regardless of whether the internal representation is 0-based (default) or 1-based. For better efficiency, set cols and/or vals to PETSC_NULL if you do not wish to extract these quantities. The user can only examine the values extracted with MatGetRow(); the values cannot be altered. To change the matrix entries, one must use MatSetValues(). You can only have one call to MatGetRow() outstanding for a particular matrix at a time, per processor. MatGetRow() can only obtain rows associated with the given processor, it cannot get rows from the other processors; for that we suggest using MatGetSubMatrices(), then MatGetRow() on the submatrix. The row indix passed to MatGetRows() is in the global number of rows. Fortran Notes: The calling sequence from Fortran is .vb MatGetRow(matrix,row,ncols,cols,values,ierr) Mat matrix (input) integer row (input) integer ncols (output) integer cols(maxcols) (output) double precision (or double complex) values(maxcols) output .ve where maxcols >= maximum nonzeros in any row of the matrix. Caution: Do not try to change the contents of the output arrays (cols and vals). In some cases, this may corrupt the matrix. Level: advanced Concepts: matrices^row access .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatGetSubMatrices(), MatGetDiagonal() @*/ PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) { PetscErrorCode ierr; PetscInt incols; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getrow)(mat,row,&incols,(PetscInt**)cols,(PetscScalar**)vals);CHKERRQ(ierr); if (ncols) *ncols = incols; ierr = PetscLogEventEnd(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatConjugate" /*@ MatConjugate - replaces the matrix values with their complex conjugates Logically Collective on Mat Input Parameters: . mat - the matrix Level: advanced .seealso: VecConjugate() @*/ PetscErrorCode MatConjugate(Mat mat) { #if defined(PETSC_USE_COMPLEX) PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->conjugate) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Not provided for this matrix format, send email to petsc-maint@mcs.anl.gov"); ierr = (*mat->ops->conjugate)(mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); #else return 0; #endif } #undef __FUNCT__ #define __FUNCT__ "MatRestoreRow" /*@C MatRestoreRow - Frees any temporary space allocated by MatGetRow(). Not Collective Input Parameters: + mat - the matrix . row - the row to get . ncols, cols - the number of nonzeros and their columns - vals - if nonzero the column values Notes: This routine should be called after you have finished examining the entries. Fortran Notes: The calling sequence from Fortran is .vb MatRestoreRow(matrix,row,ncols,cols,values,ierr) Mat matrix (input) integer row (input) integer ncols (output) integer cols(maxcols) (output) double precision (or double complex) values(maxcols) output .ve Where maxcols >= maximum nonzeros in any row of the matrix. In Fortran MatRestoreRow() MUST be called after MatGetRow() before another call to MatGetRow() can be made. Level: advanced .seealso: MatGetRow() @*/ PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (ncols) PetscValidIntPointer(ncols,3); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->restorerow) PetscFunctionReturn(0); ierr = (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowUpperTriangular" /*@ MatGetRowUpperTriangular - Sets a flag to enable calls to MatGetRow() for matrix in MATSBAIJ format. You should call MatRestoreRowUpperTriangular() after calling MatGetRow/MatRestoreRow() to disable the flag. Not Collective Input Parameters: + mat - the matrix Notes: The flag is to ensure that users are aware of MatGetRow() only provides the upper trianglular part of the row for the matrices in MATSBAIJ format. Level: advanced Concepts: matrices^row access .seealso: MatRestoreRowRowUpperTriangular() @*/ PetscErrorCode MatGetRowUpperTriangular(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getrowuppertriangular) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->getrowuppertriangular)(mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestoreRowUpperTriangular" /*@ MatRestoreRowUpperTriangular - Disable calls to MatGetRow() for matrix in MATSBAIJ format. Not Collective Input Parameters: + mat - the matrix Notes: This routine should be called after you have finished MatGetRow/MatRestoreRow(). Level: advanced .seealso: MatGetRowUpperTriangular() @*/ PetscErrorCode MatRestoreRowUpperTriangular(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(0); ierr = (*mat->ops->restorerowuppertriangular)(mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetOptionsPrefix" /*@C MatSetOptionsPrefix - Sets the prefix used for searching for all Mat options in the database. Logically Collective on Mat Input Parameter: + A - the Mat context - prefix - the prefix to prepend to all option names Notes: A hyphen (-) must NOT be given at the beginning of the prefix name. The first character of all runtime options is AUTOMATICALLY the hyphen. Level: advanced .keywords: Mat, set, options, prefix, database .seealso: MatSetFromOptions() @*/ PetscErrorCode MatSetOptionsPrefix(Mat A,const char prefix[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); ierr = PetscObjectSetOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatAppendOptionsPrefix" /*@C MatAppendOptionsPrefix - Appends to the prefix used for searching for all Mat options in the database. Logically Collective on Mat Input Parameters: + A - the Mat context - prefix - the prefix to prepend to all option names Notes: A hyphen (-) must NOT be given at the beginning of the prefix name. The first character of all runtime options is AUTOMATICALLY the hyphen. Level: advanced .keywords: Mat, append, options, prefix, database .seealso: MatGetOptionsPrefix() @*/ PetscErrorCode MatAppendOptionsPrefix(Mat A,const char prefix[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); ierr = PetscObjectAppendOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOptionsPrefix" /*@C MatGetOptionsPrefix - Sets the prefix used for searching for all Mat options in the database. Not Collective Input Parameter: . A - the Mat context Output Parameter: . prefix - pointer to the prefix string used Notes: On the fortran side, the user should pass in a string 'prefix' of sufficient length to hold the prefix. Level: advanced .keywords: Mat, get, options, prefix, database .seealso: MatAppendOptionsPrefix() @*/ PetscErrorCode MatGetOptionsPrefix(Mat A,const char *prefix[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); ierr = PetscObjectGetOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetUp" /*@ MatSetUp - Sets up the internal matrix data structures for the later use. Collective on Mat Input Parameters: . A - the Mat context Notes: If the user has not set preallocation for this matrix then a default preallocation that is likely to be inefficient is used. If a suitable preallocation routine is used, this function does not need to be called. See the Performance chapter of the PETSc users manual for how to preallocate matrices Level: beginner .keywords: Mat, setup .seealso: MatCreate(), MatDestroy() @*/ PetscErrorCode MatSetUp(Mat A) { PetscMPIInt size; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); if (!((PetscObject)A)->type_name) { ierr = MPI_Comm_size(((PetscObject)A)->comm, &size);CHKERRQ(ierr); if (size == 1) { ierr = MatSetType(A, MATSEQAIJ);CHKERRQ(ierr); } else { ierr = MatSetType(A, MATMPIAIJ);CHKERRQ(ierr); } } if (!A->preallocated && A->ops->setup) { ierr = PetscInfo(A,"Warning not preallocating matrix storage\n");CHKERRQ(ierr); ierr = (*A->ops->setup)(A);CHKERRQ(ierr); } A->preallocated = PETSC_TRUE; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatView" /*@C MatView - Visualizes a matrix object. Collective on Mat Input Parameters: + mat - the matrix - viewer - visualization context Notes: The available visualization contexts include + PETSC_VIEWER_STDOUT_SELF - standard output (default) . PETSC_VIEWER_STDOUT_WORLD - synchronized standard output where only the first processor opens the file. All other processors send their data to the first processor to print. - PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure The user can open alternative visualization contexts with + PetscViewerASCIIOpen() - Outputs matrix to a specified file . PetscViewerBinaryOpen() - Outputs matrix in binary to a specified file; corresponding input uses MatLoad() . PetscViewerDrawOpen() - Outputs nonzero matrix structure to an X window display - PetscViewerSocketOpen() - Outputs matrix to Socket viewer. Currently only the sequential dense and AIJ matrix types support the Socket viewer. The user can call PetscViewerSetFormat() to specify the output format of ASCII printed objects (when using PETSC_VIEWER_STDOUT_SELF, PETSC_VIEWER_STDOUT_WORLD and PetscViewerASCIIOpen). Available formats include + PETSC_VIEWER_DEFAULT - default, prints matrix contents . PETSC_VIEWER_ASCII_MATLAB - prints matrix contents in Matlab format . PETSC_VIEWER_ASCII_DENSE - prints entire matrix including zeros . PETSC_VIEWER_ASCII_COMMON - prints matrix contents, using a sparse format common among all matrix types . PETSC_VIEWER_ASCII_IMPL - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default) . PETSC_VIEWER_ASCII_INFO - prints basic information about the matrix size and structure (not the matrix entries) . PETSC_VIEWER_ASCII_INFO_DETAIL - prints more detailed information about the matrix structure Options Database Keys: + -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly() . -mat_view ::ascii_info_detail - Prints more detailed info . -mat_view - Prints matrix in ASCII format . -mat_view ::ascii_matlab - Prints matrix in Matlab format . -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). . -display - Sets display name (default is host) . -draw_pause - Sets number of seconds to pause after display . -mat_view socket - Sends matrix to socket, can be accessed from Matlab (see the users manual for details). . -viewer_socket_machine . -viewer_socket_port . -mat_view binary - save matrix to file in binary format - -viewer_binary_filename Level: beginner Notes: see the manual page for MatLoad() for the exact format of the binary file when the binary viewer is used. See bin/matlab/PetscBinaryRead.m for a Matlab code that can read in the binary file when the binary viewer is used. Concepts: matrices^viewing Concepts: matrices^plotting Concepts: matrices^printing .seealso: PetscViewerSetFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(), PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad() @*/ PetscErrorCode MatView(Mat mat,PetscViewer viewer) { PetscErrorCode ierr; PetscInt rows,cols,bs; PetscBool iascii; PetscViewerFormat format; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!viewer) { ierr = PetscViewerASCIIGetStdout(((PetscObject)mat)->comm,&viewer);CHKERRQ(ierr); } PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); PetscCheckSameComm(mat,1,viewer,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_View,mat,viewer,0,0);CHKERRQ(ierr); ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); if (iascii) { ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { ierr = PetscObjectPrintClassNamePrefixType((PetscObject)mat,viewer,"Matrix Object");CHKERRQ(ierr); ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); ierr = MatGetSize(mat,&rows,&cols);CHKERRQ(ierr); ierr = MatGetBlockSize(mat,&bs);CHKERRQ(ierr); if (bs != 1) { ierr = PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, bs=%D\n",rows,cols,bs);CHKERRQ(ierr); } else { ierr = PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D\n",rows,cols);CHKERRQ(ierr); } if (mat->factortype) { const MatSolverPackage solver; ierr = MatFactorGetSolverPackage(mat,&solver);CHKERRQ(ierr); ierr = PetscViewerASCIIPrintf(viewer,"package used to perform factorization: %s\n",solver);CHKERRQ(ierr); } if (mat->ops->getinfo) { MatInfo info; ierr = MatGetInfo(mat,MAT_GLOBAL_SUM,&info);CHKERRQ(ierr); ierr = PetscViewerASCIIPrintf(viewer,"total: nonzeros=%lld, allocated nonzeros=%lld\n",(Petsc64bitInt)info.nz_used,(Petsc64bitInt)info.nz_allocated);CHKERRQ(ierr); ierr = PetscViewerASCIIPrintf(viewer,"total number of mallocs used during MatSetValues calls =%D\n",(PetscInt)info.mallocs);CHKERRQ(ierr); } } } if (mat->ops->view) { ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); ierr = (*mat->ops->view)(mat,viewer);CHKERRQ(ierr); ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); } else if (!iascii) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Viewer type %s not supported",((PetscObject)viewer)->type_name); if (iascii) { ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_View,mat,viewer,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #if defined(PETSC_USE_DEBUG) #include <../src/sys/totalview/tv_data_display.h> PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat) { TV_add_row("Local rows", "int", &mat->rmap->n); TV_add_row("Local columns", "int", &mat->cmap->n); TV_add_row("Global rows", "int", &mat->rmap->N); TV_add_row("Global columns", "int", &mat->cmap->N); TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name); return TV_format_OK; } #endif #undef __FUNCT__ #define __FUNCT__ "MatLoad" /*@C MatLoad - Loads a matrix that has been stored in binary format with MatView(). The matrix format is determined from the options database. Generates a parallel MPI matrix if the communicator has more than one processor. The default matrix type is AIJ. Collective on PetscViewer Input Parameters: + newmat - the newly loaded matrix, this needs to have been created with MatCreate() or some related function before a call to MatLoad() - viewer - binary file viewer, created with PetscViewerBinaryOpen() Options Database Keys: Used with block matrix formats (MATSEQBAIJ, ...) to specify block size . -matload_block_size Level: beginner Notes: If the Mat type has not yet been given then MATAIJ is used, call MatSetFromOptions() on the Mat before calling this routine if you wish to set it from the options database. MatLoad() automatically loads into the options database any options given in the file filename.info where filename is the name of the file that was passed to the PetscViewerBinaryOpen(). The options in the info file will be ignored if you use the -viewer_binary_skip_info option. If the type or size of newmat is not set before a call to MatLoad, PETSc sets the default matrix type AIJ and sets the local and global sizes. If type and/or size is already set, then the same are used. In parallel, each processor can load a subset of rows (or the entire matrix). This routine is especially useful when a large matrix is stored on disk and only part of it is desired on each processor. For example, a parallel solver may access only some of the rows from each processor. The algorithm used here reads relatively small blocks of data rather than reading the entire matrix and then subsetting it. Notes for advanced users: Most users should not need to know the details of the binary storage format, since MatLoad() and MatView() completely hide these details. But for anyone who's interested, the standard binary matrix storage format is $ int MAT_FILE_CLASSID $ int number of rows $ int number of columns $ int total number of nonzeros $ int *number nonzeros in each row $ int *column indices of all nonzeros (starting index is zero) $ PetscScalar *values of all nonzeros PETSc automatically does the byte swapping for machines that store the bytes reversed, e.g. DEC alpha, freebsd, linux, Windows and the paragon; thus if you write your own binary read/write routines you have to swap the bytes; see PetscBinaryRead() and PetscBinaryWrite() to see how this may be done. .keywords: matrix, load, binary, input .seealso: PetscViewerBinaryOpen(), MatView(), VecLoad() @*/ PetscErrorCode MatLoad(Mat newmat,PetscViewer viewer) { PetscErrorCode ierr; PetscBool isbinary,flg; PetscFunctionBegin; PetscValidHeaderSpecific(newmat,MAT_CLASSID,1); PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); if (!isbinary) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid viewer; open viewer with PetscViewerBinaryOpen()"); if (!((PetscObject)newmat)->type_name) { ierr = MatSetType(newmat,MATAIJ);CHKERRQ(ierr); } if (!newmat->ops->load) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatLoad is not supported for type"); ierr = PetscLogEventBegin(MAT_Load,viewer,0,0,0);CHKERRQ(ierr); ierr = (*newmat->ops->load)(newmat,viewer);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Load,viewer,0,0,0);CHKERRQ(ierr); flg = PETSC_FALSE; ierr = PetscOptionsGetBool(((PetscObject)newmat)->prefix,"-matload_symmetric",&flg,PETSC_NULL);CHKERRQ(ierr); if (flg) { ierr = MatSetOption(newmat,MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); ierr = MatSetOption(newmat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE);CHKERRQ(ierr); } flg = PETSC_FALSE; ierr = PetscOptionsGetBool(((PetscObject)newmat)->prefix,"-matload_spd",&flg,PETSC_NULL);CHKERRQ(ierr); if (flg) { ierr = MatSetOption(newmat,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDestroy" /*@ MatDestroy - Frees space taken by a matrix. Collective on Mat Input Parameter: . A - the matrix Level: beginner @*/ PetscErrorCode MatDestroy(Mat *A) { PetscErrorCode ierr; PetscFunctionBegin; if (!*A) PetscFunctionReturn(0); PetscValidHeaderSpecific(*A,MAT_CLASSID,1); if (--((PetscObject)(*A))->refct > 0) {*A = PETSC_NULL; PetscFunctionReturn(0);} ierr = PetscViewerDestroy(&(*A)->viewonassembly);CHKERRQ(ierr); /* if memory was published with AMS then destroy it */ ierr = PetscObjectDepublish(*A);CHKERRQ(ierr); if ((*A)->ops->destroy) { ierr = (*(*A)->ops->destroy)(*A);CHKERRQ(ierr); } ierr = MatNullSpaceDestroy(&(*A)->nullsp);CHKERRQ(ierr); ierr = MatNullSpaceDestroy(&(*A)->nearnullsp);CHKERRQ(ierr); ierr = PetscLayoutDestroy(&(*A)->rmap);CHKERRQ(ierr); ierr = PetscLayoutDestroy(&(*A)->cmap);CHKERRQ(ierr); ierr = PetscHeaderDestroy(A);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValues" /*@ MatSetValues - Inserts or adds a block of values into a matrix. These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() MUST be called after all calls to MatSetValues() have been completed. Not Collective Input Parameters: + mat - the matrix . v - a logically two-dimensional array of values . m, idxm - the number of rows and their global indices . n, idxn - the number of columns and their global indices - addv - either ADD_VALUES or INSERT_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or MatSetUp() before using this routine By default the values, v, are row-oriented. See MatSetOption() for other options. Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. MatSetValues() uses 0-based row and column numbers in Fortran as well as in C. Negative indices may be passed in idxm and idxn, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices with homogeneous Dirchlet boundary conditions that you don't want represented in the matrix. Efficiency Alert: The routine MatSetValuesBlocked() may offer much better efficiency for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). Level: beginner Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), InsertMode, INSERT_VALUES, ADD_VALUES @*/ PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; #if defined(PETSC_USE_DEBUG) PetscInt i,j; #endif PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); if (v) PetscValidScalarPointer(v,6); MatCheckPreallocated(mat,1); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_DEBUG) else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (v) { for (i=0; iassembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesRowLocal" /*@ MatSetValuesRowLocal - Inserts a row (block row for BAIJ matrices) of nonzero values into a matrix Not Collective Input Parameters: + mat - the matrix . row - the (block) row to set - v - a logically two-dimensional array of values Notes: By the values, v, are column-oriented (for the block version) and sorted All the nonzeros in the row must be provided The matrix must have previously had its column indices set The row must belong to this process Level: intermediate Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues(), MatSetValuesRow(), MatSetLocalToGlobalMapping() @*/ PetscErrorCode MatSetValuesRowLocal(Mat mat,PetscInt row,const PetscScalar v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidScalarPointer(v,2); ierr = MatSetValuesRow(mat, mat->rmap->mapping->indices[row],v);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesRow" /*@ MatSetValuesRow - Inserts a row (block row for BAIJ matrices) of nonzero values into a matrix Not Collective Input Parameters: + mat - the matrix . row - the (block) row to set - v - a logically two-dimensional array of values Notes: The values, v, are column-oriented for the block version. All the nonzeros in the row must be provided THE MATRIX MUSAT HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually MatSetValues() is used. The row must belong to this process Level: advanced Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues() @*/ PetscErrorCode MatSetValuesRow(Mat mat,PetscInt row,const PetscScalar v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); PetscValidScalarPointer(v,2); #if defined(PETSC_USE_DEBUG) if (mat->insertmode == ADD_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add and insert values"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #endif mat->insertmode = INSERT_VALUES; if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (!mat->ops->setvaluesrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = (*mat->ops->setvaluesrow)(mat,row,v);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesStencil" /*@ MatSetValuesStencil - Inserts or adds a block of values into a matrix. Using structured grid indexing Not Collective Input Parameters: + mat - the matrix . m - number of rows being entered . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered . n - number of columns being entered . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered . v - a logically two-dimensional array of values - addv - either ADD_VALUES or INSERT_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: By default the values, v, are row-oriented. See MatSetOption() for other options. Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. The grid coordinates are across the entire grid, not just the local portion MatSetValuesStencil() uses 0-based row and column numbers in Fortran as well as in C. For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine In order to use this routine you must either obtain the matrix with DMCreateMatrix() or call MatSetLocalToGlobalMapping() and MatSetStencil() first. The columns and rows in the stencil passed in MUST be contained within the ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example, if you create a DMDA with an overlap of one grid level and on a particular process its first local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the first i index you can use in your column and row indices in MatSetStencil() is 5. In Fortran idxm and idxn should be declared as $ MatStencil idxm(4,m),idxn(4,n) and the values inserted using $ idxm(MatStencil_i,1) = i $ idxm(MatStencil_j,1) = j $ idxm(MatStencil_k,1) = k $ idxm(MatStencil_c,1) = c etc For periodic boundary conditions use negative indices for values to the left (below 0; that are to be obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the DMDA_BOUNDARY_PERIODIC boundary type. 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 a single value per point) you can skip filling those indices. Inspired by the structured grid interface to the HYPRE package (http://www.llnl.gov/CASC/hypre) Efficiency Alert: The routine MatSetValuesBlockedStencil() may offer much better efficiency for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). Level: beginner Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil @*/ PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; PetscInt buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn; PetscInt j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); PetscFunctionBegin; if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { jdxm = buf; jdxn = buf+m; } else { ierr = PetscMalloc2(m,PetscInt,&bufm,n,PetscInt,&bufn);CHKERRQ(ierr); jdxm = bufm; jdxn = bufn; } for (i=0; istencil.noc) dxm++; jdxm[i] = tmp; } for (i=0; istencil.noc) dxn++; jdxn[i] = tmp; } ierr = MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);CHKERRQ(ierr); ierr = PetscFree2(bufm,bufn);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesBlockedStencil" /*@C MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix. Using structured grid indexing Not Collective Input Parameters: + mat - the matrix . m - number of rows being entered . idxm - grid coordinates for matrix rows being entered . n - number of columns being entered . idxn - grid coordinates for matrix columns being entered . v - a logically two-dimensional array of values - addv - either ADD_VALUES or INSERT_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: By default the values, v, are row-oriented and unsorted. See MatSetOption() for other options. Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. The grid coordinates are across the entire grid, not just the local portion MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran as well as in C. For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine In order to use this routine you must either obtain the matrix with DMCreateMatrix() or call MatSetBlockSize(), MatSetLocalToGlobalMapping() and MatSetStencil() first. The columns and rows in the stencil passed in MUST be contained within the ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example, if you create a DMDA with an overlap of one grid level and on a particular process its first local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the first i index you can use in your column and row indices in MatSetStencil() is 5. In Fortran idxm and idxn should be declared as $ MatStencil idxm(4,m),idxn(4,n) and the values inserted using $ idxm(MatStencil_i,1) = i $ idxm(MatStencil_j,1) = j $ idxm(MatStencil_k,1) = k etc Negative indices may be passed in idxm and idxn, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices with homogeneous Dirchlet boundary conditions that you don't want represented in the matrix. Inspired by the structured grid interface to the HYPRE package (http://www.llnl.gov/CASC/hypre) Level: beginner Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil, MatSetBlockSize(), MatSetLocalToGlobalMapping() @*/ PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; PetscInt buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn; PetscInt j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); PetscFunctionBegin; if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { jdxm = buf; jdxn = buf+m; } else { ierr = PetscMalloc2(m,PetscInt,&bufm,n,PetscInt,&bufn);CHKERRQ(ierr); jdxm = bufm; jdxn = bufn; } for (i=0; ivalid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetStencil" /*@ MatSetStencil - Sets the grid information for setting values into a matrix via MatSetValuesStencil() Not Collective Input Parameters: + mat - the matrix . dim - dimension of the grid 1, 2, or 3 . dims - number of grid points in x, y, and z direction, including ghost points on your processor . starts - starting point of ghost nodes on your processor in x, y, and z direction - dof - number of degrees of freedom per node Inspired by the structured grid interface to the HYPRE package (www.llnl.gov/CASC/hyper) For matrices generated with DMCreateMatrix() this routine is automatically called and so not needed by the user. Level: beginner Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil() @*/ PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof) { PetscInt i; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidIntPointer(dims,3); PetscValidIntPointer(starts,4); mat->stencil.dim = dim + (dof > 1); for (i=0; istencil.dims[i] = dims[dim-i-1]; /* copy the values in backwards */ mat->stencil.starts[i] = starts[dim-i-1]; } mat->stencil.dims[dim] = dof; mat->stencil.starts[dim] = 0; mat->stencil.noc = (PetscBool)(dof == 1); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesBlocked" /*@ MatSetValuesBlocked - Inserts or adds a block of values into a matrix. Not Collective Input Parameters: + mat - the matrix . v - a logically two-dimensional array of values . m, idxm - the number of block rows and their global block indices . n, idxn - the number of block columns and their global block indices - addv - either ADD_VALUES or INSERT_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or MatSetUp() before using this routine. The m and n count the NUMBER of blocks in the row direction and column direction, NOT the total number of rows/columns; for example, if the block size is 2 and you are passing in values for rows 2,3,4,5 then m would be 2 (not 4). The values in idxm would be 1 2; that is the first index for each block divided by the block size. Note that you must call MatSetBlockSize() when constructing this matrix (after preallocating it). By default the values, v, are row-oriented, so the layout of v is the same as for MatSetValues(). See MatSetOption() for other options. Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. MatSetValuesBlocked() uses 0-based row and column numbers in Fortran as well as in C. Negative indices may be passed in idxm and idxn, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices with homogeneous Dirchlet boundary conditions that you don't want represented in the matrix. Each time an entry is set within a sparse matrix via MatSetValues(), internal searching must be done to determine where to place the the data in the matrix storage space. By instead inserting blocks of entries via MatSetValuesBlocked(), the overhead of matrix assembly is reduced. Example: $ Suppose m=n=2 and block size(bs) = 2 The array is $ $ 1 2 | 3 4 $ 5 6 | 7 8 $ - - - | - - - $ 9 10 | 11 12 $ 13 14 | 15 16 $ $ v[] should be passed in like $ v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] $ $ If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then $ v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16] Level: intermediate Concepts: matrices^putting entries in blocked .seealso: MatSetBlockSize(), MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal() @*/ PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); MatCheckPreallocated(mat,1); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_DEBUG) else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->setvaluesblocked && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); #endif if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->setvaluesblocked) { ierr = (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); } else { PetscInt buf[8192],*bufr=0,*bufc=0,*iidxm,*iidxn; PetscInt i,j,bs=mat->rmap->bs; if ((m+n)*bs <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { iidxm = buf; iidxn = buf + m*bs; } else { ierr = PetscMalloc2(m*bs,PetscInt,&bufr,n*bs,PetscInt,&bufc);CHKERRQ(ierr); iidxm = bufr; iidxn = bufc; } for (i=0; ivalid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetValues" /*@ MatGetValues - Gets a block of values from a matrix. Not Collective; currently only returns a local block Input Parameters: + mat - the matrix . v - a logically two-dimensional array for storing the values . m, idxm - the number of rows and their global indices - n, idxn - the number of columns and their global indices Notes: The user must allocate space (m*n PetscScalars) for the values, v. The values, v, are then returned in a row-oriented format, analogous to that used by default in MatSetValues(). MatGetValues() uses 0-based row and column numbers in Fortran as well as in C. MatGetValues() requires that the matrix has been assembled with MatAssemblyBegin()/MatAssemblyEnd(). Thus, calls to MatSetValues() and MatGetValues() CANNOT be made in succession without intermediate matrix assembly. Negative row or column indices will be ignored and those locations in v[] will be left unchanged. Level: advanced Concepts: matrices^accessing values .seealso: MatGetRow(), MatGetSubMatrices(), MatSetValues() @*/ PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!m || !n) PetscFunctionReturn(0); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesBatch" /*@ MatSetValuesBatch - Adds (ADD_VALUES) many blocks of values into a matrix at once. The blocks must all be square and the same size. Currently, this can only be called once and creates the given matrix. Not Collective Input Parameters: + mat - the matrix . nb - the number of blocks . bs - the number of rows (and columns) in each block . rows - a concatenation of the rows for each block - v - a concatenation of logically two-dimensional arrays of values Notes: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix. Level: advanced Concepts: matrices^putting entries in .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues() @*/ PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidScalarPointer(rows,4); PetscValidScalarPointer(v,5); #if defined(PETSC_USE_DEBUG) if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #endif ierr = PetscLogEventBegin(MAT_SetValuesBatch,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->setvaluesbatch) { ierr = (*mat->ops->setvaluesbatch)(mat,nb,bs,rows,v);CHKERRQ(ierr); } else { PetscInt b; for (b = 0; b < nb; ++b) { ierr = MatSetValues(mat, bs, &rows[b*bs], bs, &rows[b*bs], &v[b*bs*bs], ADD_VALUES);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_SetValuesBatch,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetLocalToGlobalMapping" /*@ MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by the routine MatSetValuesLocal() to allow users to insert matrix entries using a local (per-processor) numbering. Not Collective Input Parameters: + x - the matrix . rmapping - row mapping created with ISLocalToGlobalMappingCreate() or ISLocalToGlobalMappingCreateIS() - cmapping - column mapping Level: intermediate Concepts: matrices^local to global mapping Concepts: local to global mapping^for matrices .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal() @*/ PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(x,MAT_CLASSID,1); PetscValidType(x,1); PetscValidHeaderSpecific(rmapping,IS_LTOGM_CLASSID,2); PetscValidHeaderSpecific(cmapping,IS_LTOGM_CLASSID,3); if (x->ops->setlocaltoglobalmapping) { ierr = (*x->ops->setlocaltoglobalmapping)(x,rmapping,cmapping);CHKERRQ(ierr); } else { ierr = PetscLayoutSetISLocalToGlobalMapping(x->rmap,rmapping);CHKERRQ(ierr); ierr = PetscLayoutSetISLocalToGlobalMapping(x->cmap,cmapping);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetLocalToGlobalMappingBlock" /*@ MatSetLocalToGlobalMappingBlock - Sets a local-to-global numbering for use by the routine MatSetValuesBlockedLocal() to allow users to insert matrix entries using a local (per-processor) numbering. Not Collective Input Parameters: + x - the matrix . rmapping - row mapping created with ISLocalToGlobalMappingCreate() or ISLocalToGlobalMappingCreateIS() - cmapping - column mapping Level: intermediate Concepts: matrices^local to global mapping blocked Concepts: local to global mapping^for matrices, blocked .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal(), MatSetValuesBlocked(), MatSetValuesLocal() @*/ PetscErrorCode MatSetLocalToGlobalMappingBlock(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(x,MAT_CLASSID,1); PetscValidType(x,1); PetscValidHeaderSpecific(rmapping,IS_LTOGM_CLASSID,2); PetscValidHeaderSpecific(cmapping,IS_LTOGM_CLASSID,3); ierr = PetscLayoutSetISLocalToGlobalMappingBlock(x->rmap,rmapping);CHKERRQ(ierr); ierr = PetscLayoutSetISLocalToGlobalMappingBlock(x->cmap,cmapping);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetLocalToGlobalMapping" /*@ MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by MatSetLocalToGlobalMapping() Not Collective Input Parameters: . A - the matrix Output Parameters: + rmapping - row mapping - cmapping - column mapping Level: advanced Concepts: matrices^local to global mapping Concepts: local to global mapping^for matrices .seealso: MatSetValuesLocal(), MatGetLocalToGlobalMappingBlock() @*/ PetscErrorCode MatGetLocalToGlobalMapping(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (rmapping) PetscValidPointer(rmapping,2); if (cmapping) PetscValidPointer(cmapping,3); if (rmapping) *rmapping = A->rmap->mapping; if (cmapping) *cmapping = A->cmap->mapping; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetLocalToGlobalMappingBlock" /*@ MatGetLocalToGlobalMappingBlock - Gets the local-to-global numbering set by MatSetLocalToGlobalMappingBlock() Not Collective Input Parameters: . A - the matrix Output Parameters: + rmapping - row mapping - cmapping - column mapping Level: advanced Concepts: matrices^local to global mapping blocked Concepts: local to global mapping^for matrices, blocked .seealso: MatSetValuesBlockedLocal(), MatGetLocalToGlobalMapping() @*/ PetscErrorCode MatGetLocalToGlobalMappingBlock(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (rmapping) PetscValidPointer(rmapping,2); if (cmapping) PetscValidPointer(cmapping,3); if (rmapping) *rmapping = A->rmap->bmapping; if (cmapping) *cmapping = A->cmap->bmapping; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesLocal" /*@ MatSetValuesLocal - Inserts or adds values into certain locations of a matrix, using a local ordering of the nodes. Not Collective Input Parameters: + x - the matrix . nrow, irow - number of rows and their local indices . ncol, icol - number of columns and their local indices . y - a logically two-dimensional array of values - addv - either INSERT_VALUES or ADD_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or MatSetUp() before using this routine If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() MUST be called after all calls to MatSetValuesLocal() have been completed. Level: intermediate Concepts: matrices^putting entries in with local numbering .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(), MatSetValueLocal() @*/ PetscErrorCode MatSetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); if (!nrow || !ncol) PetscFunctionReturn(0); /* no values to insert */ PetscValidIntPointer(irow,3); PetscValidIntPointer(icol,5); PetscValidScalarPointer(y,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_DEBUG) else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); #endif if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->setvalueslocal) { ierr = (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); } else { PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm; if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { irowm = buf; icolm = buf+nrow; } else { ierr = PetscMalloc2(nrow,PetscInt,&bufr,ncol,PetscInt,&bufc);CHKERRQ(ierr); irowm = bufr; icolm = bufc; } ierr = ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);CHKERRQ(ierr); ierr = MatSetValues(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesBlockedLocal" /*@ MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix, using a local ordering of the nodes a block at a time. Not Collective Input Parameters: + x - the matrix . nrow, irow - number of rows and their local indices . ncol, icol - number of columns and their local indices . y - a logically two-dimensional array of values - addv - either INSERT_VALUES or ADD_VALUES, where ADD_VALUES adds values to any existing entries, and INSERT_VALUES replaces existing entries with new values Notes: If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or MatSetUp() before using this routine If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetBlockSize() and MatSetLocalToGlobalMappingBlock() before using this routineBefore calling MatSetValuesLocal(), the user must first set the Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES options cannot be mixed without intervening calls to the assembly routines. These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() MUST be called after all calls to MatSetValuesBlockedLocal() have been completed. Level: intermediate Concepts: matrices^putting blocked values in with local numbering .seealso: MatSetBlockSize(), MatSetLocalToGlobalMappingBlock(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesLocal(), MatSetLocalToGlobalMappingBlock(), MatSetValuesBlocked() @*/ PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); if (!nrow || !ncol) PetscFunctionReturn(0); /* no values to insert */ PetscValidIntPointer(irow,3); PetscValidIntPointer(icol,5); PetscValidScalarPointer(y,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_DEBUG) else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 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); #endif if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->setvaluesblockedlocal) { ierr = (*mat->ops->setvaluesblockedlocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); } else { PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm; if (mat->rmap->bmapping && mat->cmap->bmapping) { if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { irowm = buf; icolm = buf + nrow; } else { ierr = PetscMalloc2(nrow,PetscInt,&bufr,ncol,PetscInt,&bufc);CHKERRQ(ierr); irowm = bufr; icolm = bufc; } ierr = ISLocalToGlobalMappingApply(mat->rmap->bmapping,nrow,irow,irowm);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApply(mat->cmap->bmapping,ncol,icol,icolm);CHKERRQ(ierr); ierr = MatSetValuesBlocked(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); } else { PetscInt i,j,bs=mat->rmap->bs; if ((nrow+ncol)*bs <=(PetscInt)(sizeof(buf)/sizeof(PetscInt))) { irowm = buf; icolm = buf + nrow; } else { ierr = PetscMalloc2(nrow*bs,PetscInt,&bufr,ncol*bs,PetscInt,&bufc);CHKERRQ(ierr); irowm = bufr; icolm = bufc; } for (i=0; ivalid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultDiagonalBlock" /*@ MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multiplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMult(A,y,y). Level: developer Concepts: matrix-vector product .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatMultDiagonalBlock(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); MatCheckPreallocated(mat,1); if (!mat->ops->multdiagonalblock) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"This matrix type does not have a multiply defined"); ierr = (*mat->ops->multdiagonalblock)(mat,x,y);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } /* --------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatMult" /*@ MatMult - Computes the matrix-vector product, y = Ax. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multiplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMult(A,y,y). Level: beginner Concepts: matrix-vector product .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatMult(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); #if !defined(PETSC_HAVE_CONSTRAINTS) 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); 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); 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); #endif VecValidValues(x,2,PETSC_TRUE); MatCheckPreallocated(mat,1); if (!mat->ops->mult) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"This matrix type does not have a multiply defined"); ierr = PetscLogEventBegin(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); ierr = (*mat->ops->mult)(mat,x,y);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); VecValidValues(y,3,PETSC_FALSE); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultTranspose" /*@ MatMultTranspose - Computes matrix transpose times a vector. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multilplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMultTranspose(A,y,y). For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple, use MatMultHermitianTranspose() Level: beginner Concepts: matrix vector product^transpose .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd(), MatMultHermitianTranspose(), MatTranspose() @*/ PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); #if !defined(PETSC_HAVE_CONSTRAINTS) 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); 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); #endif VecValidValues(x,2,PETSC_TRUE); MatCheckPreallocated(mat,1); if (!mat->ops->multtranspose) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"This matrix type does not have a multiply tranpose defined"); ierr = PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); ierr = (*mat->ops->multtranspose)(mat,x,y);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); VecValidValues(y,3,PETSC_FALSE); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultHermitianTranspose" /*@ MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multilplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMultHermitianTranspose(A,y,y). Also called the conjugate transpose, complex conjugate transpose, or adjoint. For real numbers MatMultTranspose() and MatMultHermitianTranspose() are identical. Level: beginner Concepts: matrix vector product^transpose .seealso: MatMult(), MatMultAdd(), MatMultHermitianTransposeAdd(), MatMultTranspose() @*/ PetscErrorCode MatMultHermitianTranspose(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; Vec w; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); #if !defined(PETSC_HAVE_CONSTRAINTS) 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); 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); #endif MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_MultHermitianTranspose,mat,x,y,0);CHKERRQ(ierr); if (mat->ops->multhermitiantranspose) { ierr = (*mat->ops->multhermitiantranspose)(mat,x,y);CHKERRQ(ierr); } else { ierr = VecDuplicate(x,&w);CHKERRQ(ierr); ierr = VecCopy(x,w);CHKERRQ(ierr); ierr = VecConjugate(w);CHKERRQ(ierr); ierr = MatMultTranspose(mat,w,y);CHKERRQ(ierr); ierr = VecDestroy(&w);CHKERRQ(ierr); ierr = VecConjugate(y);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_MultHermitianTranspose,mat,x,y,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultAdd" /*@ MatMultAdd - Computes v3 = v2 + A * v1. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - v1, v2 - the vectors Output Parameters: . v3 - the result Notes: The vectors v1 and v3 cannot be the same. I.e., one cannot call MatMultAdd(A,v1,v2,v1). Level: beginner Concepts: matrix vector product^addition .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd() @*/ PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v1,VEC_CLASSID,2); PetscValidHeaderSpecific(v2,VEC_CLASSID,3); PetscValidHeaderSpecific(v3,VEC_CLASSID,4); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->cmap->N != v1->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->cmap->N,v1->map->N); /* 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); 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); */ 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); 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); if (v1 == v3) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); MatCheckPreallocated(mat,1); if (!mat->ops->multadd) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"No MatMultAdd() for matrix type '%s'",((PetscObject)mat)->type_name); ierr = PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = (*mat->ops->multadd)(mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultTransposeAdd" /*@ MatMultTransposeAdd - Computes v3 = v2 + A' * v1. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - v1, v2 - the vectors Output Parameters: . v3 - the result Notes: The vectors v1 and v3 cannot be the same. I.e., one cannot call MatMultTransposeAdd(A,v1,v2,v1). Level: beginner Concepts: matrix vector product^transpose and addition .seealso: MatMultTranspose(), MatMultAdd(), MatMult() @*/ PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v1,VEC_CLASSID,2); PetscValidHeaderSpecific(v2,VEC_CLASSID,3); PetscValidHeaderSpecific(v3,VEC_CLASSID,4); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->multtransposeadd) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (v1 == v3) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); if (mat->rmap->N != v1->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N); if (mat->cmap->N != v2->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N); if (mat->cmap->N != v3->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = (*mat->ops->multtransposeadd)(mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultHermitianTransposeAdd" /*@ MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - v1, v2 - the vectors Output Parameters: . v3 - the result Notes: The vectors v1 and v3 cannot be the same. I.e., one cannot call MatMultHermitianTransposeAdd(A,v1,v2,v1). Level: beginner Concepts: matrix vector product^transpose and addition .seealso: MatMultHermitianTranspose(), MatMultTranspose(), MatMultAdd(), MatMult() @*/ PetscErrorCode MatMultHermitianTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v1,VEC_CLASSID,2); PetscValidHeaderSpecific(v2,VEC_CLASSID,3); PetscValidHeaderSpecific(v3,VEC_CLASSID,4); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->multhermitiantransposeadd) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (v1 == v3) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); if (mat->rmap->N != v1->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N); if (mat->cmap->N != v2->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N); if (mat->cmap->N != v3->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = (*mat->ops->multhermitiantransposeadd)(mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultConstrained" /*@ MatMultConstrained - The inner multiplication routine for a constrained matrix P^T A P. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multilplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMult(A,y,y). Level: beginner .keywords: matrix, multiply, matrix-vector product, constraint .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 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); 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); 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); ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); ierr = (*mat->ops->multconstrained)(mat,x,y);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultTransposeConstrained" /*@ MatMultTransposeConstrained - The inner multiplication routine for a constrained matrix P^T A^T P. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix - x - the vector to be multilplied Output Parameters: . y - the result Notes: The vectors x and y cannot be the same. I.e., one cannot call MatMult(A,y,y). Level: beginner .keywords: matrix, multiply, matrix-vector product, constraint .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 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); 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); ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); ierr = (*mat->ops->multtransposeconstrained)(mat,x,y);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetFactorType" /*@C MatGetFactorType - gets the type of factorization it is Note Collective as the flag Input Parameters: . mat - the matrix Output Parameters: . t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT Level: intermediate .seealso: MatFactorType, MatGetFactor() @*/ PetscErrorCode MatGetFactorType(Mat mat,MatFactorType *t) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); *t = mat->factortype; PetscFunctionReturn(0); } /* ------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatGetInfo" /*@C MatGetInfo - Returns information about matrix storage (number of nonzeros, memory, etc.). Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used as the flag Input Parameters: . mat - the matrix Output Parameters: + flag - flag indicating the type of parameters to be returned (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors, MAT_GLOBAL_SUM - sum over all processors) - info - matrix information context Notes: The MatInfo context contains a variety of matrix data, including number of nonzeros allocated and used, number of mallocs during matrix assembly, etc. Additional information for factored matrices is provided (such as the fill ratio, number of mallocs during factorization, etc.). Much of this info is printed to PETSC_STDOUT when using the runtime options $ -info -mat_view ::ascii_info Example for C/C++ Users: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of data within the MatInfo context. For example, .vb MatInfo info; Mat A; double mal, nz_a, nz_u; MatGetInfo(A,MAT_LOCAL,&info); mal = info.mallocs; nz_a = info.nz_allocated; .ve Example for Fortran Users: Fortran users should declare info as a double precision array of dimension MAT_INFO_SIZE, and then extract the parameters of interest. See the file ${PETSC_DIR}/include/finclude/petscmat.h a complete list of parameter names. .vb double precision info(MAT_INFO_SIZE) double precision mal, nz_a Mat A integer ierr call MatGetInfo(A,MAT_LOCAL,info,ierr) mal = info(MAT_INFO_MALLOCS) nz_a = info(MAT_INFO_NZ_ALLOCATED) .ve Level: intermediate Concepts: matrices^getting information on Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatInfo] .seealso: MatStashGetInfo() @*/ PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(info,3); if (!mat->ops->getinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->getinfo)(mat,flag,info);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatLUFactor" /*@C MatLUFactor - Performs in-place LU factorization of matrix. Collective on Mat Input Parameters: + mat - the matrix . row - row permutation . col - column permutation - info - options for factorization, includes $ fill - expected fill as ratio of original fill. $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) $ Run with the option -info to determine an optimal value to use Notes: Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). This changes the state of the matrix to a factored matrix; it cannot be used for example with MatSetValues() unless one first calls MatSetUnfactored(). Level: developer Concepts: matrices^LU factorization .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatGetOrdering(), MatSetUnfactored(), MatFactorInfo, MatGetFactor() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info) { PetscErrorCode ierr; MatFactorInfo tinfo; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); if (info) PetscValidPointer(info,4); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->lufactor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); if (!info) { ierr = MatFactorInfoInitialize(&tinfo);CHKERRQ(ierr); info = &tinfo; } ierr = PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = (*mat->ops->lufactor)(mat,row,col,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatILUFactor" /*@C MatILUFactor - Performs in-place ILU factorization of matrix. Collective on Mat Input Parameters: + mat - the matrix . row - row permutation . col - column permutation - info - structure containing $ levels - number of levels of fill. $ expected fill - as ratio of original fill. $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices missing diagonal entries) Notes: Probably really in-place only when level of fill is zero, otherwise allocates new space to store factored matrix and deletes previous memory. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^ILU factorization .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); PetscValidPointer(info,4); PetscValidType(mat,1); if (mat->rmap->N != mat->cmap->N) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONG,"matrix must be square"); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->ilufactor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = (*mat->ops->ilufactor)(mat,row,col,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatLUFactorSymbolic" /*@C MatLUFactorSymbolic - Performs symbolic LU factorization of matrix. Call this routine before calling MatLUFactorNumeric(). Collective on Mat Input Parameters: + fact - the factor matrix obtained with MatGetFactor() . mat - the matrix . row, col - row and column permutations - info - options for factorization, includes $ fill - expected fill as ratio of original fill. $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) $ Run with the option -info to determine an optimal value to use Notes: See the users manual for additional information about choosing the fill factor for better efficiency. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^LU symbolic factorization .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatLUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); if (info) PetscValidPointer(info,4); PetscValidType(mat,1); PetscValidPointer(fact,5); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!(fact)->ops->lufactorsymbolic) { const MatSolverPackage spackage; ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Matrix type %s symbolic LU using solver package %s",((PetscObject)mat)->type_name,spackage); } MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = (fact->ops->lufactorsymbolic)(fact,mat,row,col,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatLUFactorNumeric" /*@C MatLUFactorNumeric - Performs numeric LU factorization of a matrix. Call this routine after first calling MatLUFactorSymbolic(). Collective on Mat Input Parameters: + fact - the factor matrix obtained with MatGetFactor() . mat - the matrix - info - options for factorization Notes: See MatLUFactor() for in-place factorization. See MatCholeskyFactorNumeric() for the symmetric, positive definite case. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^LU numeric factorization .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatLUFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(fact,2); PetscValidHeaderSpecific(fact,MAT_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(((PetscObject)mat)->comm,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); if (!(fact)->ops->lufactornumeric) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s numeric LU",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_LUFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); ierr = (fact->ops->lufactornumeric)(fact,mat,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_LUFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); if (fact->viewonassembly) { ierr = PetscViewerPushFormat(fact->viewonassembly,fact->viewformatonassembly);CHKERRQ(ierr); ierr = MatView(fact,fact->viewonassembly);CHKERRQ(ierr); ierr = PetscViewerPopFormat(fact->viewonassembly);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactor" /*@C MatCholeskyFactor - Performs in-place Cholesky factorization of a symmetric matrix. Collective on Mat Input Parameters: + mat - the matrix . perm - row and column permutations - f - expected fill as ratio of original fill Notes: See MatLUFactor() for the nonsymmetric case. See also MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric(). Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^Cholesky factorization .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric() MatGetOrdering() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (perm) PetscValidHeaderSpecific(perm,IS_CLASSID,2); if (info) PetscValidPointer(info,3); if (mat->rmap->N != mat->cmap->N) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONG,"Matrix must be square"); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->choleskyfactor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); ierr = (*mat->ops->choleskyfactor)(mat,perm,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactorSymbolic" /*@C MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization of a symmetric matrix. Collective on Mat Input Parameters: + fact - the factor matrix obtained with MatGetFactor() . mat - the matrix . perm - row and column permutations - info - options for factorization, includes $ fill - expected fill as ratio of original fill. $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) $ Run with the option -info to determine an optimal value to use Notes: See MatLUFactorSymbolic() for the nonsymmetric case. See also MatCholeskyFactor() and MatCholeskyFactorNumeric(). Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^Cholesky symbolic factorization .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric() MatGetOrdering() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatCholeskyFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (perm) PetscValidHeaderSpecific(perm,IS_CLASSID,2); if (info) PetscValidPointer(info,3); PetscValidPointer(fact,4); if (mat->rmap->N != mat->cmap->N) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONG,"Matrix must be square"); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!(fact)->ops->choleskyfactorsymbolic) { const MatSolverPackage spackage; ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s symbolic factor Cholesky using solver package %s",((PetscObject)mat)->type_name,spackage); } MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = (fact->ops->choleskyfactorsymbolic)(fact,mat,perm,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactorNumeric" /*@C MatCholeskyFactorNumeric - Performs numeric Cholesky factorization of a symmetric matrix. Call this routine after first calling MatCholeskyFactorSymbolic(). Collective on Mat Input Parameters: + fact - the factor matrix obtained with MatGetFactor() . mat - the initial matrix . info - options for factorization - fact - the symbolic factor of mat Notes: Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^Cholesky numeric factorization .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatCholeskyFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(fact,2); PetscValidHeaderSpecific(fact,MAT_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!(fact)->ops->choleskyfactornumeric) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s numeric factor Cholesky",((PetscObject)mat)->type_name); if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(((PetscObject)mat)->comm,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); MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); ierr = (fact->ops->choleskyfactornumeric)(fact,mat,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); if (fact->viewonassembly) { ierr = PetscViewerPushFormat(fact->viewonassembly,fact->viewformatonassembly);CHKERRQ(ierr); ierr = MatView(fact,fact->viewonassembly);CHKERRQ(ierr); ierr = PetscViewerPopFormat(fact->viewonassembly);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatSolve" /*@ MatSolve - Solves A x = b, given a factored matrix. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix - b - the right-hand-side vector Output Parameter: . x - the result vector Notes: The vectors b and x cannot be the same. I.e., one cannot call MatSolve(A,x,x). Notes: Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^triangular solves .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd() @*/ PetscErrorCode MatSolve(Mat mat,Vec b,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(b,VEC_CLASSID,2); PetscValidHeaderSpecific(x,VEC_CLASSID,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->cmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); if (mat->rmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 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); if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(0); if (!mat->ops->solve) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); ierr = (*mat->ops->solve)(mat,b,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatSolve_Basic" PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X) { PetscErrorCode ierr; Vec b,x; PetscInt m,N,i; PetscScalar *bb,*xx; PetscBool flg; PetscFunctionBegin; ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr); if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr); if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); ierr = MatDenseGetArray(B,&bb);CHKERRQ(ierr); ierr = MatDenseGetArray(X,&xx);CHKERRQ(ierr); ierr = MatGetLocalSize(B,&m,PETSC_NULL);CHKERRQ(ierr); /* number local rows */ ierr = MatGetSize(B,PETSC_NULL,&N);CHKERRQ(ierr); /* total columns in dense matrix */ ierr = MatGetVecs(A,&x,&b);CHKERRQ(ierr); for (i=0; icomm,PETSC_ERR_ARG_IDN,"X and B must be different matrices"); if (!A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (A->cmap->N != X->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Mat A,Mat X: global dim %D %D",A->cmap->N,X->rmap->N); if (A->rmap->N != B->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D",A->rmap->N,B->rmap->N); 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); if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(0); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_MatSolve,A,B,X,0);CHKERRQ(ierr); if (!A->ops->matsolve) { ierr = PetscInfo1(A,"Mat type %s using basic MatMatSolve",((PetscObject)A)->type_name);CHKERRQ(ierr); ierr = MatMatSolve_Basic(A,B,X);CHKERRQ(ierr); } else { ierr = (*A->ops->matsolve)(A,B,X);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_MatSolve,A,B,X,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)X);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatForwardSolve" /*@ MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U, Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix - b - the right-hand-side vector Output Parameter: . x - the result vector Notes: MatSolve() should be used for most applications, as it performs a forward solve followed by a backward solve. The vectors b and x cannot be the same, i.e., one cannot call MatForwardSolve(A,x,x). For matrix in seqsbaij format with block size larger than 1, the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet. MatForwardSolve() solves U^T*D y = b, and MatBackwardSolve() solves U x = y. Thus they do not provide a symmetric preconditioner. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^forward solves .seealso: MatSolve(), MatBackwardSolve() @*/ PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(b,VEC_CLASSID,2); PetscValidHeaderSpecific(x,VEC_CLASSID,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->ops->forwardsolve) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (mat->cmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); if (mat->rmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 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); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); ierr = (*mat->ops->forwardsolve)(mat,b,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatBackwardSolve" /*@ MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU. D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U, Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix - b - the right-hand-side vector Output Parameter: . x - the result vector Notes: MatSolve() should be used for most applications, as it performs a forward solve followed by a backward solve. The vectors b and x cannot be the same. I.e., one cannot call MatBackwardSolve(A,x,x). For matrix in seqsbaij format with block size larger than 1, the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet. MatForwardSolve() solves U^T*D y = b, and MatBackwardSolve() solves U x = y. Thus they do not provide a symmetric preconditioner. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^backward solves .seealso: MatSolve(), MatForwardSolve() @*/ PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(b,VEC_CLASSID,2); PetscValidHeaderSpecific(x,VEC_CLASSID,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->ops->backwardsolve) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (mat->cmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); if (mat->rmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 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); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); ierr = (*mat->ops->backwardsolve)(mat,b,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveAdd" /*@ MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix . b - the right-hand-side vector - y - the vector to be added to Output Parameter: . x - the result vector Notes: The vectors b and x cannot be the same. I.e., one cannot call MatSolveAdd(A,x,y,x). Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^triangular solves .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd() @*/ PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x) { PetscScalar one = 1.0; Vec tmp; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(y,VEC_CLASSID,2); PetscValidHeaderSpecific(b,VEC_CLASSID,3); PetscValidHeaderSpecific(x,VEC_CLASSID,4); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,y,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->cmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); if (mat->rmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); if (mat->rmap->N != y->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N); 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); 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); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); if (mat->ops->solveadd) { ierr = (*mat->ops->solveadd)(mat,b,y,x);CHKERRQ(ierr); } else { /* do the solve then the add manually */ if (x != y) { ierr = MatSolve(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(x,one,y);CHKERRQ(ierr); } else { ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); ierr = PetscLogObjectParent(mat,tmp);CHKERRQ(ierr); ierr = VecCopy(x,tmp);CHKERRQ(ierr); ierr = MatSolve(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(x,one,tmp);CHKERRQ(ierr); ierr = VecDestroy(&tmp);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveTranspose" /*@ MatSolveTranspose - Solves A' x = b, given a factored matrix. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix - b - the right-hand-side vector Output Parameter: . x - the result vector Notes: The vectors b and x cannot be the same. I.e., one cannot call MatSolveTranspose(A,x,x). Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^triangular solves .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd() @*/ PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(b,VEC_CLASSID,2); PetscValidHeaderSpecific(x,VEC_CLASSID,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->ops->solvetranspose) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name); if (mat->rmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); if (mat->cmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); ierr = (*mat->ops->solvetranspose)(mat,b,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveTransposeAdd" /*@ MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a factored matrix. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the factored matrix . b - the right-hand-side vector - y - the vector to be added to Output Parameter: . x - the result vector Notes: The vectors b and x cannot be the same. I.e., one cannot call MatSolveTransposeAdd(A,x,y,x). Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^triangular solves .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose() @*/ PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x) { PetscScalar one = 1.0; PetscErrorCode ierr; Vec tmp; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(y,VEC_CLASSID,2); PetscValidHeaderSpecific(b,VEC_CLASSID,3); PetscValidHeaderSpecific(x,VEC_CLASSID,4); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,y,3); PetscCheckSameComm(mat,1,x,4); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->rmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); if (mat->cmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N); if (mat->cmap->N != y->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N); 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); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); if (mat->ops->solvetransposeadd) { ierr = (*mat->ops->solvetransposeadd)(mat,b,y,x);CHKERRQ(ierr); } else { /* do the solve then the add manually */ if (x != y) { ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(x,one,y);CHKERRQ(ierr); } else { ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); ierr = PetscLogObjectParent(mat,tmp);CHKERRQ(ierr); ierr = VecCopy(x,tmp);CHKERRQ(ierr); ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(x,one,tmp);CHKERRQ(ierr); ierr = VecDestroy(&tmp);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatSOR" /*@ MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps. Neighbor-wise Collective on Mat and Vec Input Parameters: + mat - the matrix . b - the right hand side . omega - the relaxation factor . flag - flag indicating the type of SOR (see below) . shift - diagonal shift . its - the number of iterations - lits - the number of local iterations Output Parameters: . x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess) SOR Flags: . SOR_FORWARD_SWEEP - forward SOR . SOR_BACKWARD_SWEEP - backward SOR . SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR) . SOR_LOCAL_FORWARD_SWEEP - local forward SOR . SOR_LOCAL_BACKWARD_SWEEP - local forward SOR . SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR . SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies upper/lower triangular part of matrix to vector (with omega) . SOR_ZERO_INITIAL_GUESS - zero initial guess Notes: SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings on each processor. Application programmers will not generally use MatSOR() directly, but instead will employ the KSP/PC interface. Notes: for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing Notes for Advanced Users: The flags are implemented as bitwise inclusive or operations. For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP) to specify a zero initial guess for SSOR. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Vectors x and b CANNOT be the same Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes Level: developer Concepts: matrices^relaxation Concepts: matrices^SOR Concepts: matrices^Gauss-Seidel @*/ PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(b,VEC_CLASSID,2); PetscValidHeaderSpecific(x,VEC_CLASSID,8); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,8); if (!mat->ops->sor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->cmap->N != x->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); if (mat->rmap->N != b->map->N) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 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); if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its); if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits); if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_SOR,mat,b,x,0);CHKERRQ(ierr); ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SOR,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCopy_Basic" /* Default matrix copy routine. */ PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str) { PetscErrorCode ierr; PetscInt i,rstart = 0,rend = 0,nz; const PetscInt *cwork; const PetscScalar *vwork; PetscFunctionBegin; if (B->assembled) { ierr = MatZeroEntries(B);CHKERRQ(ierr); } ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); for (i=rstart; iassembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(((PetscObject)A)->comm,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); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_Copy,A,B,0,0);CHKERRQ(ierr); if (A->ops->copy) { ierr = (*A->ops->copy)(A,B,str);CHKERRQ(ierr); } else { /* generic conversion */ ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); } B->stencil.dim = A->stencil.dim; B->stencil.noc = A->stencil.noc; for (i=0; i<=A->stencil.dim; i++) { B->stencil.dims[i] = A->stencil.dims[i]; B->stencil.starts[i] = A->stencil.starts[i]; } ierr = PetscLogEventEnd(MAT_Copy,A,B,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)B);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatConvert" /*@C MatConvert - Converts a matrix to another matrix, either of the same or different type. Collective on Mat Input Parameters: + mat - the matrix . newtype - new matrix type. Use MATSAME to create a new matrix of the same type as the original matrix. - reuse - denotes if the destination matrix is to be created or reused. Currently MAT_REUSE_MATRIX is only supported for inplace conversion, otherwise use MAT_INITIAL_MATRIX. Output Parameter: . M - pointer to place new matrix Notes: MatConvert() first creates a new matrix and then copies the data from the first matrix. A related routine is MatCopy(), which copies the matrix entries of one matrix to another already existing matrix context. Cannot be used to convert a sequential matrix to parallel or parallel to sequential, the MPI communicator of the generated matrix is always the same as the communicator of the input matrix. Level: intermediate Concepts: matrices^converting between storage formats .seealso: MatCopy(), MatDuplicate() @*/ PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M) { PetscErrorCode ierr; PetscBool sametype,issame,flg; char convname[256],mtype[256]; Mat B; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(M,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);CHKERRQ(ierr); ierr = PetscOptionsGetString(((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);CHKERRQ(ierr); if (flg) { newtype = mtype; } ierr = PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);CHKERRQ(ierr); ierr = PetscStrcmp(newtype,"same",&issame);CHKERRQ(ierr); if ((reuse == MAT_REUSE_MATRIX) && (mat != *M)) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"MAT_REUSE_MATRIX only supported for in-place conversion currently"); if ((reuse == MAT_REUSE_MATRIX) && (issame || sametype)) PetscFunctionReturn(0); if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) { ierr = (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);CHKERRQ(ierr); } else { PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=PETSC_NULL; const char *prefix[3] = {"seq","mpi",""}; PetscInt i; /* Order of precedence: 1) See if a specialized converter is known to the current matrix. 2) See if a specialized converter is known to the desired matrix class. 3) See if a good general converter is registered for the desired class (as of 6/27/03 only MATMPIADJ falls into this category). 4) See if a good general converter is known for the current matrix. 5) Use a really basic converter. */ /* 1) See if a specialized converter is known to the current matrix and the desired class */ for (i=0; i<3; i++) { ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); ierr = PetscStrcat(convname,((PetscObject)mat)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); ierr = PetscStrcat(convname,prefix[i]);CHKERRQ(ierr); ierr = PetscStrcat(convname,issame ? ((PetscObject)mat)->type_name : newtype);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); if (conv) goto foundconv; } /* 2) See if a specialized converter is known to the desired matrix class. */ ierr = MatCreate(((PetscObject)mat)->comm,&B);CHKERRQ(ierr); ierr = MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);CHKERRQ(ierr); ierr = MatSetType(B,newtype);CHKERRQ(ierr); for (i=0; i<3; i++) { ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); ierr = PetscStrcat(convname,((PetscObject)mat)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); ierr = PetscStrcat(convname,prefix[i]);CHKERRQ(ierr); ierr = PetscStrcat(convname,newtype);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,convname,(void (**)(void))&conv);CHKERRQ(ierr); if (conv) { ierr = MatDestroy(&B);CHKERRQ(ierr); goto foundconv; } } /* 3) See if a good general converter is registered for the desired class */ conv = B->ops->convertfrom; ierr = MatDestroy(&B);CHKERRQ(ierr); if (conv) goto foundconv; /* 4) See if a good general converter is known for the current matrix */ if (mat->ops->convert) { conv = mat->ops->convert; } if (conv) goto foundconv; /* 5) Use a really basic converter. */ conv = MatConvert_Basic; foundconv: ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = (*conv)(mat,newtype,reuse,M);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)*M);CHKERRQ(ierr); /* Copy Mat options */ if (mat->symmetric) {ierr = MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr);} if (mat->hermitian) {ierr = MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr);} PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatFactorGetSolverPackage" /*@C MatFactorGetSolverPackage - Returns name of the package providing the factorization routines Not Collective Input Parameter: . mat - the matrix, must be a factored matrix Output Parameter: . type - the string name of the package (do not free this string) Notes: In Fortran you pass in a empty string and the package name will be copied into it. (Make sure the string is long enough) Level: intermediate .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor() @*/ PetscErrorCode MatFactorGetSolverPackage(Mat mat, const MatSolverPackage *type) { PetscErrorCode ierr, (*conv)(Mat,const MatSolverPackage*); PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); ierr = PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverPackage_C",(void (**)(void))&conv);CHKERRQ(ierr); if (!conv) { *type = MATSOLVERPETSC; } else { ierr = (*conv)(mat,type);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetFactor" /*@C MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic() Collective on Mat Input Parameters: + mat - the matrix . type - name of solver type, for example, superlu, petsc (to use PETSc's default) - ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU, Output Parameters: . f - the factor matrix used with MatXXFactorSymbolic() calls Notes: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package Level: intermediate .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable() @*/ PetscErrorCode MatGetFactor(Mat mat, const MatSolverPackage type,MatFactorType ftype,Mat *f) { PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*); char convname[256]; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscStrcpy(convname,"MatGetFactor_");CHKERRQ(ierr); ierr = PetscStrcat(convname,type);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); if (!conv) { PetscBool flag; MPI_Comm comm; ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); ierr = PetscStrcasecmp(MATSOLVERPETSC,type,&flag);CHKERRQ(ierr); if (flag) SETERRQ2(comm,PETSC_ERR_SUP,"Matrix format %s does not have a built-in PETSc %s",((PetscObject)mat)->type_name,MatFactorTypes[ftype]); else SETERRQ4(comm,PETSC_ERR_SUP,"Matrix format %s does not have a solver package %s for %s. Perhaps you must ./configure with --download-%s",((PetscObject)mat)->type_name,type,MatFactorTypes[ftype],type); } ierr = (*conv)(mat,ftype,f);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetFactorAvailable" /*@C MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type Not Collective Input Parameters: + mat - the matrix . type - name of solver type, for example, superlu, petsc (to use PETSc's default) - ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU, Output Parameter: . flg - PETSC_TRUE if the factorization is available Notes: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package Level: intermediate .seealso: MatCopy(), MatDuplicate(), MatGetFactor() @*/ PetscErrorCode MatGetFactorAvailable(Mat mat, const MatSolverPackage type,MatFactorType ftype,PetscBool *flg) { PetscErrorCode ierr, (*conv)(Mat,MatFactorType,PetscBool*); char convname[256]; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscStrcpy(convname,"MatGetFactorAvailable_");CHKERRQ(ierr); ierr = PetscStrcat(convname,type);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); if (!conv) { *flg = PETSC_FALSE; } else { ierr = (*conv)(mat,ftype,flg);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDuplicate" /*@ MatDuplicate - Duplicates a matrix including the non-zero structure. Collective on Mat Input Parameters: + mat - the matrix - op - either MAT_DO_NOT_COPY_VALUES or MAT_COPY_VALUES, cause it to copy the numerical values in the matrix MAT_SHARE_NONZERO_PATTERN to share the nonzero patterns with the previous matrix and not copy them. Output Parameter: . M - pointer to place new matrix Level: intermediate Concepts: matrices^duplicating Notes: You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN. .seealso: MatCopy(), MatConvert() @*/ PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M) { PetscErrorCode ierr; Mat B; PetscInt i; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(M,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); *M = 0; if (!mat->ops->duplicate) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Not written for this matrix type"); ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->duplicate)(mat,op,M);CHKERRQ(ierr); B = *M; B->stencil.dim = mat->stencil.dim; B->stencil.noc = mat->stencil.noc; for (i=0; i<=mat->stencil.dim; i++) { B->stencil.dims[i] = mat->stencil.dims[i]; B->stencil.starts[i] = mat->stencil.starts[i]; } B->nooffproczerorows = mat->nooffproczerorows; B->nooffprocentries = mat->nooffprocentries; ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)B);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetDiagonal" /*@ MatGetDiagonal - Gets the diagonal of a matrix. Logically Collective on Mat and Vec Input Parameters: + mat - the matrix - v - the vector for storing the diagonal Output Parameter: . v - the diagonal of the matrix Level: intermediate Note: Currently only correct in parallel for square matrices. Concepts: matrices^accessing diagonals .seealso: MatGetRow(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs() @*/ PetscErrorCode MatGetDiagonal(Mat mat,Vec v) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getdiagonal) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->getdiagonal)(mat,v);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowMin" /*@ MatGetRowMin - Gets the minimum value (of the real part) of each row of the matrix Logically Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: + v - the vector for storing the maximums - idx - the indices of the column found for each row (optional) Level: intermediate Notes: The result of this call are the same as if one converted the matrix to dense format and found the minimum value in each row (i.e. the implicit zeros are counted as zeros). This code is only implemented for a couple of matrix formats. Concepts: matrices^getting row maximums .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs(), MatGetRowMax() @*/ PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->getrowmin)(mat,v,idx);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowMinAbs" /*@ MatGetRowMinAbs - Gets the minimum value (in absolute value) of each row of the matrix Logically Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: + v - the vector for storing the minimums - idx - the indices of the column found for each row (or PETSC_NULL if not needed) Level: intermediate Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that row is 0 (the first column). This code is only implemented for a couple of matrix formats. Concepts: matrices^getting row maximums .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin() @*/ PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getrowminabs) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); if (idx) {ierr = PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));CHKERRQ(ierr);} ierr = (*mat->ops->getrowminabs)(mat,v,idx);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowMax" /*@ MatGetRowMax - Gets the maximum value (of the real part) of each row of the matrix Logically Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: + v - the vector for storing the maximums - idx - the indices of the column found for each row (optional) Level: intermediate Notes: The result of this call are the same as if one converted the matrix to dense format and found the minimum value in each row (i.e. the implicit zeros are counted as zeros). This code is only implemented for a couple of matrix formats. Concepts: matrices^getting row maximums .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs(), MatGetRowMin() @*/ PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getrowmax) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->getrowmax)(mat,v,idx);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowMaxAbs" /*@ MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each row of the matrix Logically Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: + v - the vector for storing the maximums - idx - the indices of the column found for each row (or PETSC_NULL if not needed) Level: intermediate Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that row is 0 (the first column). This code is only implemented for a couple of matrix formats. Concepts: matrices^getting row maximums .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMin() @*/ PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); if (idx) {ierr = PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));CHKERRQ(ierr);} ierr = (*mat->ops->getrowmaxabs)(mat,v,idx);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowSum" /*@ MatGetRowSum - Gets the sum of each row of the matrix Logically Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: . v - the vector for storing the sum of rows Level: intermediate Notes: This code is slow since it is not currently specialized for different formats Concepts: matrices^getting row sums .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMin() @*/ PetscErrorCode MatGetRowSum(Mat mat, Vec v) { PetscInt start = 0, end = 0, row; PetscScalar *array; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(v,VEC_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); MatCheckPreallocated(mat,1); ierr = MatGetOwnershipRange(mat, &start, &end);CHKERRQ(ierr); ierr = VecGetArray(v, &array);CHKERRQ(ierr); for (row = start; row < end; ++row) { PetscInt ncols, col; const PetscInt *cols; const PetscScalar *vals; array[row - start] = 0.0; ierr = MatGetRow(mat, row, &ncols, &cols, &vals);CHKERRQ(ierr); for (col = 0; col < ncols; col++) { array[row - start] += vals[col]; } ierr = MatRestoreRow(mat, row, &ncols, &cols, &vals);CHKERRQ(ierr); } ierr = VecRestoreArray(v, &array);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject) v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTranspose" /*@ MatTranspose - Computes an in-place or out-of-place transpose of a matrix. Collective on Mat Input Parameter: + mat - the matrix to transpose - reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX Output Parameters: . B - the transpose Notes: If you pass in &mat for B the transpose will be done in place, for example MatTranspose(mat,MAT_REUSE_MATRIX,&mat); Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed. Level: intermediate Concepts: matrices^transposing .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse @*/ PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->transpose) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->transpose)(mat,reuse,B);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); if (B) {ierr = PetscObjectStateIncrease((PetscObject)*B);CHKERRQ(ierr);} PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsTranspose" /*@ MatIsTranspose - Test whether a matrix is another one's transpose, or its own, in which case it tests symmetry. Collective on Mat Input Parameter: + A - the matrix to test - B - the matrix to test against, this can equal the first parameter Output Parameters: . flg - the result Notes: Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm has a running time of the order of the number of nonzeros; the parallel test involves parallel copies of the block-offdiagonal parts of the matrix. Level: intermediate Concepts: matrices^transposing, matrix^symmetry .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian() @*/ PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool *flg) { PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidPointer(flg,3); ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",(void (**)(void))&f);CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",(void (**)(void))&g);CHKERRQ(ierr); *flg = PETSC_FALSE; if (f && g) { if (f == g) { ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); } else SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test"); } else { MatType mattype; if (!f) { ierr = MatGetType(A,&mattype);CHKERRQ(ierr); } else { ierr = MatGetType(B,&mattype);CHKERRQ(ierr); } SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for transpose",mattype); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatHermitianTranspose" /*@ MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate. Collective on Mat Input Parameter: + mat - the matrix to transpose and complex conjugate - reuse - store the transpose matrix in the provided B Output Parameters: . B - the Hermitian Notes: If you pass in &mat for B the Hermitian will be done in place Level: intermediate Concepts: matrices^transposing, complex conjugatex .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse @*/ PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B) { PetscErrorCode ierr; PetscFunctionBegin; ierr = MatTranspose(mat,reuse,B);CHKERRQ(ierr); #if defined(PETSC_USE_COMPLEX) ierr = MatConjugate(*B);CHKERRQ(ierr); #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsHermitianTranspose" /*@ MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose, Collective on Mat Input Parameter: + A - the matrix to test - B - the matrix to test against, this can equal the first parameter Output Parameters: . flg - the result Notes: Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm has a running time of the order of the number of nonzeros; the parallel test involves parallel copies of the block-offdiagonal parts of the matrix. Level: intermediate Concepts: matrices^transposing, matrix^symmetry .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose() @*/ PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool *flg) { PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidPointer(flg,3); ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",(void (**)(void))&f);CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",(void (**)(void))&g);CHKERRQ(ierr); if (f && g) { if (f==g) { ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); } else SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test"); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPermute" /*@ MatPermute - Creates a new matrix with rows and columns permuted from the original. Collective on Mat Input Parameters: + mat - the matrix to permute . row - row permutation, each processor supplies only the permutation for its rows - col - column permutation, each processor supplies only the permutation for its columns Output Parameters: . B - the permuted matrix Level: advanced Note: The index sets map from row/col of permuted matrix to row/col of original matrix. The index sets should be on the same communicator as Mat and have the same local sizes. Concepts: matrices^permuting .seealso: MatGetOrdering(), ISAllGather() @*/ PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(row,IS_CLASSID,2); PetscValidHeaderSpecific(col,IS_CLASSID,3); PetscValidPointer(B,4); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->permute)(mat,row,col,B);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)*B);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatEqual" /*@ MatEqual - Compares two matrices. Collective on Mat Input Parameters: + A - the first matrix - B - the second matrix Output Parameter: . flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise. Level: intermediate Concepts: matrices^equality between @*/ PetscErrorCode MatEqual(Mat A,Mat B,PetscBool *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(A,1); PetscValidType(B,2); PetscValidIntPointer(flg,3); PetscCheckSameComm(A,1,B,2); MatCheckPreallocated(B,2); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!B->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(((PetscObject)A)->comm,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); if (!A->ops->equal) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name); if (!B->ops->equal) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name); if (A->ops->equal != B->ops->equal) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"A is type: %s\nB is type: %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); MatCheckPreallocated(A,1); ierr = (*A->ops->equal)(A,B,flg);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDiagonalScale" /*@ MatDiagonalScale - Scales a matrix on the left and right by diagonal matrices that are stored as vectors. Either of the two scaling matrices can be PETSC_NULL. Collective on Mat Input Parameters: + mat - the matrix to be scaled . l - the left scaling vector (or PETSC_NULL) - r - the right scaling vector (or PETSC_NULL) Notes: MatDiagonalScale() computes A = LAR, where L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector) The L scales the rows of the matrix, the R scales the columns of the matrix. Level: intermediate Concepts: matrices^diagonal scaling Concepts: diagonal scaling of matrices .seealso: MatScale() @*/ PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->ops->diagonalscale) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (l) {PetscValidHeaderSpecific(l,VEC_CLASSID,2);PetscCheckSameComm(mat,1,l,2);} if (r) {PetscValidHeaderSpecific(r,VEC_CLASSID,3);PetscCheckSameComm(mat,1,r,3);} if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->diagonalscale)(mat,l,r);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatScale" /*@ MatScale - Scales all elements of a matrix by a given number. Logically Collective on Mat Input Parameters: + mat - the matrix to be scaled - a - the scaling value Output Parameter: . mat - the scaled matrix Level: intermediate Concepts: matrices^scaling all entries .seealso: MatDiagonalScale() @*/ PetscErrorCode MatScale(Mat mat,PetscScalar a) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidLogicalCollectiveScalar(mat,a,2); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); if (a != (PetscScalar)1.0) { ierr = (*mat->ops->scale)(mat,a);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatNorm" /*@ MatNorm - Calculates various norms of a matrix. Collective on Mat Input Parameters: + mat - the matrix - type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY Output Parameters: . nrm - the resulting norm Level: intermediate Concepts: matrices^norm Concepts: norm^of matrix @*/ PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidScalarPointer(nrm,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->norm) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->norm)(mat,type,nrm);CHKERRQ(ierr); PetscFunctionReturn(0); } /* This variable is used to prevent counting of MatAssemblyBegin() that are called from within a MatAssemblyEnd(). */ static PetscInt MatAssemblyEnd_InUse = 0; #undef __FUNCT__ #define __FUNCT__ "MatAssemblyBegin" /*@ MatAssemblyBegin - Begins assembling the matrix. This routine should be called after completing all calls to MatSetValues(). Collective on Mat Input Parameters: + mat - the matrix - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY Notes: MatSetValues() generally caches the values. The matrix is ready to use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before using the matrix. ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the 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 a global collective operation requring all processes that share the matrix. Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros before MAT_FINAL_ASSEMBLY so the space is not compressed out. Level: beginner Concepts: matrices^assembling .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled() @*/ PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?"); if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } if (!MatAssemblyEnd_InUse) { ierr = PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->assemblybegin) {ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} ierr = PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); } else if (mat->ops->assemblybegin) { ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatAssembled" /*@ MatAssembled - Indicates if a matrix has been assembled and is ready for use; for example, in matrix-vector product. Not Collective Input Parameter: . mat - the matrix Output Parameter: . assembled - PETSC_TRUE or PETSC_FALSE Level: advanced Concepts: matrices^assembled? .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin() @*/ PetscErrorCode MatAssembled(Mat mat,PetscBool *assembled) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(assembled,2); *assembled = mat->assembled; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatViewFromOptions" /* Processes command line options to determine if/how a matrix is to be viewed. Called by MatAssemblyEnd() and MatLoad(). */ PetscErrorCode MatViewFromOptions(Mat mat,const char optionname[]) { PetscErrorCode ierr; PetscViewer viewer; PetscBool flg; static PetscBool incall = PETSC_FALSE; PetscViewerFormat format; PetscFunctionBegin; if (incall) PetscFunctionReturn(0); incall = PETSC_TRUE; ierr = PetscOptionsGetViewer(((PetscObject)mat)->comm,((PetscObject)mat)->prefix,optionname,&viewer,&format,&flg);CHKERRQ(ierr); if (flg) { ierr = PetscViewerPushFormat(viewer,format);CHKERRQ(ierr); ierr = MatView(mat,viewer);CHKERRQ(ierr); ierr = PetscViewerPopFormat(viewer);CHKERRQ(ierr); ierr = PetscViewerDestroy(&viewer);CHKERRQ(ierr); } incall = PETSC_FALSE; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatAssemblyEnd" /*@ MatAssemblyEnd - Completes assembling the matrix. This routine should be called after MatAssemblyBegin(). Collective on Mat Input Parameters: + mat - the matrix - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY Options Database Keys: + -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly() . -mat_view ::ascii_info_detail - Prints more detailed info . -mat_view - Prints matrix in ASCII format . -mat_view ::ascii_matlab - Prints matrix in Matlab format . -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). . -display - Sets display name (default is host) . -draw_pause - Sets number of seconds to pause after display . -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See the users manual) . -viewer_socket_machine . -viewer_socket_port . -mat_view binary - save matrix to file in binary format - -viewer_binary_filename Notes: MatSetValues() generally caches the values. The matrix is ready to use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before using the matrix. Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros before MAT_FINAL_ASSEMBLY so the space is not compressed out. Level: beginner .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), PetscDrawCreate(), MatView(), MatAssembled(), PetscViewerSocketOpen() @*/ PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type) { PetscErrorCode ierr; static PetscInt inassm = 0; PetscBool flg = PETSC_FALSE; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); inassm++; MatAssemblyEnd_InUse++; if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */ ierr = PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); if (mat->ops->assemblyend) { ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); } else if (mat->ops->assemblyend) { ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); } /* Flush assembly is not a true assembly */ if (type != MAT_FLUSH_ASSEMBLY) { mat->assembled = PETSC_TRUE; mat->num_ass++; } mat->insertmode = NOT_SET_VALUES; MatAssemblyEnd_InUse--; ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); if (!mat->symmetric_eternal) { mat->symmetric_set = PETSC_FALSE; mat->hermitian_set = PETSC_FALSE; mat->structurally_symmetric_set = PETSC_FALSE; } #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) { if (mat->viewonassembly) { ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); } if (mat->checksymmetryonassembly) { ierr = MatIsSymmetric(mat,mat->checksymmetrytol,&flg);CHKERRQ(ierr); if (flg) { ierr = PetscPrintf(((PetscObject)mat)->comm,"Matrix is symmetric (tolerance %G)\n",mat->checksymmetrytol);CHKERRQ(ierr); } else { ierr = PetscPrintf(((PetscObject)mat)->comm,"Matrix is not symmetric (tolerance %G)\n",mat->checksymmetrytol);CHKERRQ(ierr); } } if (mat->nullsp && mat->checknullspaceonassembly) { ierr = MatNullSpaceTest(mat->nullsp,mat,PETSC_NULL);CHKERRQ(ierr); } } inassm--; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetOption" /*@ MatSetOption - Sets a parameter option for a matrix. Some options may be specific to certain storage formats. Some options determine how values will be inserted (or added). Sorted, row-oriented input will generally assemble the fastest. The default is row-oriented. Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption Input Parameters: + mat - the matrix . option - the option, one of those listed below (and possibly others), - flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE) Options Describing Matrix Structure: + MAT_SPD - symmetric positive definite - MAT_SYMMETRIC - symmetric in terms of both structure and value . MAT_HERMITIAN - transpose is the complex conjugation . MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure - MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag you set to be kept with all future use of the matrix including after MatAssemblyBegin/End() which could potentially change the symmetry structure, i.e. you KNOW the matrix will ALWAYS have the property you set. Options For Use with MatSetValues(): Insert a logically dense subblock, which can be . MAT_ROW_ORIENTED - row-oriented (default) Note these options reflect the data you pass in with MatSetValues(); it has nothing to do with how the data is stored internally in the matrix data structure. When (re)assembling a matrix, we can restrict the input for efficiency/debugging purposes. These options include + MAT_NEW_NONZERO_LOCATIONS - additional insertions will be allowed if they generate a new nonzero . MAT_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only) . MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries . MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry . MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly + MAT_NO_OFF_PROC_ENTRIES - you know each process will only set values for its own rows, will generate an error if any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves performance for very large process counts. Notes: Some options are relevant only for particular matrix types and are thus ignored by others. Other options are not supported by certain matrix types and will generate an error message if set. If using a Fortran 77 module to compute a matrix, one may need to use the column-oriented option (or convert to the row-oriented format). MAT_NEW_NONZERO_LOCATIONS set to PETSC_FALSE indicates that any add or insertion that would generate a new entry in the nonzero structure is instead ignored. Thus, if memory has not alredy been allocated for this particular data, then the insertion is ignored. For dense matrices, in which the entire array is allocated, no entries are ever ignored. Set after the first MatAssemblyEnd() MAT_NEW_NONZERO_LOCATION_ERR indicates that any add or insertion that would generate a new entry in the nonzero structure instead produces an error. (Currently supported for AIJ and BAIJ formats only.) This is a useful flag when using SAME_NONZERO_PATTERN in calling KSPSetOperators() to ensure that the nonzero pattern truely does remain unchanged. Set after the first MatAssemblyEnd() MAT_NEW_NONZERO_ALLOCATION_ERR indicates that any add or insertion that would generate a new entry that has not been preallocated will instead produce an error. (Currently supported for AIJ and BAIJ formats only.) This is a useful flag when debugging matrix memory preallocation. MAT_IGNORE_OFF_PROC_ENTRIES indicates entries destined for other processors should be dropped, rather than stashed. This is useful if you know that the "owning" processor is also always generating the correct matrix entries, so that PETSc need not transfer duplicate entries generated on another processor. MAT_USE_HASH_TABLE indicates that a hash table be used to improve the searches during matrix assembly. When this flag is set, the hash table is created during the first Matrix Assembly. This hash table is used the next time through, during MatSetVaules()/MatSetVaulesBlocked() to improve the searching of indices. MAT_NEW_NONZERO_LOCATIONS flag should be used with MAT_USE_HASH_TABLE flag. This option is currently supported by MATMPIBAIJ format only. MAT_KEEP_NONZERO_PATTERN indicates when MatZeroRows() is called the zeroed entries are kept in the nonzero structure MAT_IGNORE_ZERO_ENTRIES - for AIJ/IS matrices this will stop zero values from creating a zero location in the matrix MAT_USE_INODES - indicates using inode version of the code - works with AIJ and ROWBS matrix types MAT_NO_OFF_PROC_ZERO_ROWS - you know each process will only zero its own rows. This avoids all reductions in the zero row routines and thus improves performance for very large process counts. MAT_IGNORE_LOWER_TRIANGULAR - For SBAIJ matrices will ignore any insertions you make in the lower triangular part of the matrix (since they should match the upper triangular part). Notes: Can only be called after MatSetSizes() and MatSetType() have been set. Level: intermediate Concepts: matrices^setting options .seealso: MatOption, Mat @*/ PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (op > 0) PetscValidLogicalCollectiveEnum(mat,op,2); PetscValidLogicalCollectiveBool(mat,flg,3); if (((int) op) <= MAT_OPTION_MIN || ((int) op) >= MAT_OPTION_MAX) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Options %d is out of range",(int)op); if (!((PetscObject)mat)->type_name) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_TYPENOTSET,"Cannot set options until type and size have been set, see MatSetType() and MatSetSizes()"); switch (op) { case MAT_NO_OFF_PROC_ENTRIES: mat->nooffprocentries = flg; PetscFunctionReturn(0); break; case MAT_NO_OFF_PROC_ZERO_ROWS: mat->nooffproczerorows = flg; PetscFunctionReturn(0); break; case MAT_SPD: mat->spd_set = PETSC_TRUE; mat->spd = flg; if (flg) { mat->symmetric = PETSC_TRUE; mat->structurally_symmetric = PETSC_TRUE; mat->symmetric_set = PETSC_TRUE; mat->structurally_symmetric_set = PETSC_TRUE; } break; case MAT_SYMMETRIC: mat->symmetric = flg; if (flg) mat->structurally_symmetric = PETSC_TRUE; mat->symmetric_set = PETSC_TRUE; mat->structurally_symmetric_set = flg; break; case MAT_HERMITIAN: mat->hermitian = flg; if (flg) mat->structurally_symmetric = PETSC_TRUE; mat->hermitian_set = PETSC_TRUE; mat->structurally_symmetric_set = flg; break; case MAT_STRUCTURALLY_SYMMETRIC: mat->structurally_symmetric = flg; mat->structurally_symmetric_set = PETSC_TRUE; break; case MAT_SYMMETRY_ETERNAL: mat->symmetric_eternal = flg; break; default: break; } if (mat->ops->setoption) { ierr = (*mat->ops->setoption)(mat,op,flg);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroEntries" /*@ MatZeroEntries - Zeros all entries of a matrix. For sparse matrices this routine retains the old nonzero structure. Logically Collective on Mat Input Parameters: . mat - the matrix Level: intermediate Notes: If the matrix was not preallocated then a default, likely poor preallocation will be set in the matrix, so this should be called after the preallocation phase. See the Performance chapter of the users manual for information on preallocating matrices. Concepts: matrices^zeroing .seealso: MatZeroRows() @*/ PetscErrorCode MatZeroEntries(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 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"); if (!mat->ops->zeroentries) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->zeroentries)(mat);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsColumns" /*@C MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal) of a set of rows and columns of a matrix. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows to remove . rows - the global row indices . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself). The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(), MatZeroRowsColumnsIS() @*/ PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->zerorowscolumns) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); if (mat->viewonassembly) { ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsColumnsIS" /*@C MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal) of a set of rows and columns of a matrix. Collective on Mat Input Parameters: + mat - the matrix . is - the rows to zero . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself). The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(), MatZeroRowsColumns() @*/ PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscInt numRows; const PetscInt *rows; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(is,IS_CLASSID,2); PetscValidType(mat,1); PetscValidType(is,2); ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); ierr = MatZeroRowsColumns(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRows" /*@C MatZeroRows - Zeros all entries (except possibly the main diagonal) of a set of rows of a matrix. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows to remove . rows - the global row indices . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: For the AIJ and BAIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself). You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it owns that are to be zeroed. This saves a global synchronization in the implementation. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() @*/ PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->zerorows) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); if (mat->viewonassembly) { ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsIS" /*@C MatZeroRowsIS - Zeros all entries (except possibly the main diagonal) of a set of rows of a matrix. Collective on Mat Input Parameters: + mat - the matrix . is - index set of rows to remove . diag - value put in all diagonals of eliminated rows . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: For the AIJ and BAIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself). You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it owns that are to be zeroed. This saves a global synchronization in the implementation. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() @*/ PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) { PetscInt numRows; const PetscInt *rows; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(is,IS_CLASSID,2); ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); ierr = MatZeroRows(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsStencil" /*@C MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal) of a set of rows of a matrix. These rows must be local to the process. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows to remove . rows - the grid coordinates (and component number when dof > 1) for matrix rows . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: For the AIJ and BAIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself). The grid coordinates are across the entire grid, not just the local portion In Fortran idxm and idxn should be declared as $ MatStencil idxm(4,m) and the values inserted using $ idxm(MatStencil_i,1) = i $ idxm(MatStencil_j,1) = j $ idxm(MatStencil_k,1) = k $ idxm(MatStencil_c,1) = c etc For periodic boundary conditions use negative indices for values to the left (below 0; that are to be obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the DMDA_BOUNDARY_PERIODIC boundary type. 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 a single value per point) you can skip filling those indices. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRows(), MatZeroRowsIS(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() @*/ PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b) { PetscInt dim = mat->stencil.dim; PetscInt sdim = dim - (1 - (PetscInt) mat->stencil.noc); PetscInt *dims = mat->stencil.dims+1; PetscInt *starts = mat->stencil.starts; PetscInt *dxm = (PetscInt*) rows; PetscInt *jdxm, i, j, tmp, numNewRows = 0; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); ierr = PetscMalloc(numRows*sizeof(PetscInt), &jdxm);CHKERRQ(ierr); for (i = 0; i < numRows; ++i) { /* Skip unused dimensions (they are ordered k, j, i, c) */ for (j = 0; j < 3-sdim; ++j) dxm++; /* Local index in X dir */ tmp = *dxm++ - starts[0]; /* Loop over remaining dimensions */ for (j = 0; j < dim-1; ++j) { /* If nonlocal, set index to be negative */ if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT; /* Update local index */ else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; } /* Skip component slot if necessary */ if (mat->stencil.noc) dxm++; /* Local row number */ if (tmp >= 0) { jdxm[numNewRows++] = tmp; } } ierr = MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);CHKERRQ(ierr); ierr = PetscFree(jdxm);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsColumnsStencil" /*@C MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal) of a set of rows and columns of a matrix. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows/columns to remove . rows - the grid coordinates (and component number when dof > 1) for matrix rows . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: For the AIJ and BAIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). For the parallel case, all processes that share the matrix (i.e., those in the communicator used for matrix creation) MUST call this routine, regardless of whether any rows being zeroed are owned by them. Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to list only rows local to itself, but the row/column numbers are given in local numbering). The grid coordinates are across the entire grid, not just the local portion In Fortran idxm and idxn should be declared as $ MatStencil idxm(4,m) and the values inserted using $ idxm(MatStencil_i,1) = i $ idxm(MatStencil_j,1) = j $ idxm(MatStencil_k,1) = k $ idxm(MatStencil_c,1) = c etc For periodic boundary conditions use negative indices for values to the left (below 0; that are to be obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the DMDA_BOUNDARY_PERIODIC boundary type. 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 a single value per point) you can skip filling those indices. Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroRows(), MatZeroRowsIS(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() @*/ PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b) { PetscInt dim = mat->stencil.dim; PetscInt sdim = dim - (1 - (PetscInt) mat->stencil.noc); PetscInt *dims = mat->stencil.dims+1; PetscInt *starts = mat->stencil.starts; PetscInt *dxm = (PetscInt*) rows; PetscInt *jdxm, i, j, tmp, numNewRows = 0; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); ierr = PetscMalloc(numRows*sizeof(PetscInt), &jdxm);CHKERRQ(ierr); for (i = 0; i < numRows; ++i) { /* Skip unused dimensions (they are ordered k, j, i, c) */ for (j = 0; j < 3-sdim; ++j) dxm++; /* Local index in X dir */ tmp = *dxm++ - starts[0]; /* Loop over remaining dimensions */ for (j = 0; j < dim-1; ++j) { /* If nonlocal, set index to be negative */ if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT; /* Update local index */ else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; } /* Skip component slot if necessary */ if (mat->stencil.noc) dxm++; /* Local row number */ if (tmp >= 0) { jdxm[numNewRows++] = tmp; } } ierr = MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);CHKERRQ(ierr); ierr = PetscFree(jdxm);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsLocal" /*@C MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal) of a set of rows of a matrix; using local numbering of rows. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows to remove . rows - the global row indices . diag - value put in all diagonals of eliminated rows . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: Before calling MatZeroRowsLocal(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMapping(). For the AIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it owns that are to be zeroed. This saves a global synchronization in the implementation. Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroRows(), MatZeroRowsLocalIS(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping @*/ PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscMPIInt size; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = MPI_Comm_size(((PetscObject)mat)->comm,&size);CHKERRQ(ierr); if (mat->ops->zerorowslocal) { ierr = (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); } else if (size == 1) { ierr = (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); } else { IS is, newis; const PetscInt *newRows; if (!mat->rmap->mapping) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); ierr = ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);CHKERRQ(ierr); ierr = ISGetIndices(newis,&newRows);CHKERRQ(ierr); ierr = (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(newis,&newRows);CHKERRQ(ierr); ierr = ISDestroy(&newis);CHKERRQ(ierr); ierr = ISDestroy(&is);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsLocalIS" /*@C MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal) of a set of rows of a matrix; using local numbering of rows. Collective on Mat Input Parameters: + mat - the matrix . is - index set of rows to remove . diag - value put in all diagonals of eliminated rows . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: Before calling MatZeroRowsLocalIS(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMapping(). For the AIJ matrix formats this removes the old nonzero structure, but does not release memory. For the dense and block diagonal formats this does not alter the nonzero structure. If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure of the matrix is not changed (even for AIJ and BAIJ matrices) the values are merely zeroed. The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it owns that are to be zeroed. This saves a global synchronization in the implementation. Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroRows(), MatZeroRowsLocal(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping @*/ PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscInt numRows; const PetscInt *rows; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(is,IS_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); ierr = MatZeroRowsLocal(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsColumnsLocal" /*@C MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal) of a set of rows and columns of a matrix; using local numbering of rows. Collective on Mat Input Parameters: + mat - the matrix . numRows - the number of rows to remove . rows - the global row indices . diag - value put in all diagonals of eliminated rows . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: Before calling MatZeroRowsColumnsLocal(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMapping(). The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroRows(), MatZeroRowsLocalIS(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping @*/ PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscMPIInt size; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (numRows) PetscValidIntPointer(rows,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = MPI_Comm_size(((PetscObject)mat)->comm,&size);CHKERRQ(ierr); if (size == 1) { ierr = (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); } else { IS is, newis; const PetscInt *newRows; if (!mat->cmap->mapping) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); ierr = ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);CHKERRQ(ierr); ierr = ISGetIndices(newis,&newRows);CHKERRQ(ierr); ierr = (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(newis,&newRows);CHKERRQ(ierr); ierr = ISDestroy(&newis);CHKERRQ(ierr); ierr = ISDestroy(&is);CHKERRQ(ierr); } ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); #if defined(PETSC_HAVE_CUSP) if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { mat->valid_GPU_matrix = PETSC_CUSP_CPU; } #endif PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatZeroRowsColumnsLocalIS" /*@C MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal) of a set of rows and columns of a matrix; using local numbering of rows. Collective on Mat Input Parameters: + mat - the matrix . is - index set of rows to remove . diag - value put in all diagonals of eliminated rows . x - optional vector of solutions for zeroed rows (other entries in vector are not used) - b - optional vector of right hand side, that will be adjusted by provided solution Notes: Before calling MatZeroRowsColumnsLocalIS(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMapping(). The user can set a value in the diagonal entry (or for the AIJ and row formats can optionally remove the main diagonal entry from the nonzero structure as well, by passing 0.0 as the final argument). Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroRows(), MatZeroRowsLocal(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping @*/ PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) { PetscErrorCode ierr; PetscInt numRows; const PetscInt *rows; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(is,IS_CLASSID,2); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); ierr = MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetSize" /*@ MatGetSize - Returns the numbers of rows and columns in a matrix. Not Collective Input Parameter: . mat - the matrix Output Parameters: + m - the number of global rows - n - the number of global columns Note: both output parameters can be PETSC_NULL on input. Level: beginner Concepts: matrices^size .seealso: MatGetLocalSize() @*/ PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (m) *m = mat->rmap->N; if (n) *n = mat->cmap->N; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetLocalSize" /*@ MatGetLocalSize - Returns the number of rows and columns in a matrix stored locally. This information may be implementation dependent, so use with care. Not Collective Input Parameters: . mat - the matrix Output Parameters: + m - the number of local rows - n - the number of local columns Note: both output parameters can be PETSC_NULL on input. Level: beginner Concepts: matrices^local size .seealso: MatGetSize() @*/ PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (m) PetscValidIntPointer(m,2); if (n) PetscValidIntPointer(n,3); if (m) *m = mat->rmap->n; if (n) *n = mat->cmap->n; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOwnershipRangeColumn" /*@ MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by this processor. (The columns of the "diagonal block") Not Collective, unless matrix has not been allocated, then collective on Mat Input Parameters: . mat - the matrix Output Parameters: + m - the global index of the first local column - n - one more than the global index of the last local column Notes: both output parameters can be PETSC_NULL on input. Level: developer Concepts: matrices^column ownership .seealso: MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn() @*/ PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (m) PetscValidIntPointer(m,2); if (n) PetscValidIntPointer(n,3); MatCheckPreallocated(mat,1); if (m) *m = mat->cmap->rstart; if (n) *n = mat->cmap->rend; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOwnershipRange" /*@ MatGetOwnershipRange - Returns the range of matrix rows owned by this processor, assuming that the matrix is laid out with the first n1 rows on the first processor, the next n2 rows on the second, etc. For certain parallel layouts this range may not be well defined. Not Collective Input Parameters: . mat - the matrix Output Parameters: + m - the global index of the first local row - n - one more than the global index of the last local row Note: Both output parameters can be PETSC_NULL on input. $ This function requires that the matrix be preallocated. If you have not preallocated, consider using $ PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N) $ and then MPI_Scan() to calculate prefix sums of the local sizes. Level: beginner Concepts: matrices^row ownership .seealso: MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock() @*/ PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (m) PetscValidIntPointer(m,2); if (n) PetscValidIntPointer(n,3); MatCheckPreallocated(mat,1); if (m) *m = mat->rmap->rstart; if (n) *n = mat->rmap->rend; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOwnershipRanges" /*@C MatGetOwnershipRanges - Returns the range of matrix rows owned by each process Not Collective, unless matrix has not been allocated, then collective on Mat Input Parameters: . mat - the matrix Output Parameters: . ranges - start of each processors portion plus one more then the total length at the end Level: beginner Concepts: matrices^row ownership .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn() @*/ PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); ierr = PetscLayoutGetRanges(mat->rmap,ranges);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOwnershipRangesColumn" /*@C MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by this processor. (The columns of the "diagonal blocks" for each process) Not Collective, unless matrix has not been allocated, then collective on Mat Input Parameters: . mat - the matrix Output Parameters: . ranges - start of each processors portion plus one more then the total length at the end Level: beginner Concepts: matrices^column ownership .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges() @*/ PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); ierr = PetscLayoutGetRanges(mat->cmap,ranges);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetOwnershipIS" /*@C MatGetOwnershipIS - Get row and column ownership as index sets Not Collective Input Arguments: . A - matrix of type Elemental Output Arguments: + rows - rows in which this process owns elements . cols - columns in which this process owns elements Level: intermediate .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL, MatSetValues() @*/ PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols) { PetscErrorCode ierr,(*f)(Mat,IS*,IS*); PetscFunctionBegin; MatCheckPreallocated(A,1); ierr = PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",(PetscVoidStarFunction)&f);CHKERRQ(ierr); if (f) { ierr = (*f)(A,rows,cols);CHKERRQ(ierr); } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */ if (rows) {ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);CHKERRQ(ierr);} if (cols) {ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);CHKERRQ(ierr);} } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatILUFactorSymbolic" /*@C MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix. Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric() to complete the factorization. Collective on Mat Input Parameters: + mat - the matrix . row - row permutation . column - column permutation - info - structure containing $ levels - number of levels of fill. $ expected fill - as ratio of original fill. $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices missing diagonal entries) Output Parameters: . fact - new matrix that has been symbolically factored Notes: See the users manual for additional information about choosing the fill factor for better efficiency. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^symbolic LU factorization Concepts: matrices^factorization Concepts: LU^symbolic factorization .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() MatGetOrdering(), MatFactorInfo Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(row,IS_CLASSID,2); PetscValidHeaderSpecific(col,IS_CLASSID,3); PetscValidPointer(info,4); PetscValidPointer(fact,5); if (info->levels < 0) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels); if (info->fill < 1.0) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %G",info->fill); if (!(fact)->ops->ilufactorsymbolic) { const MatSolverPackage spackage; ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage); } if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatICCFactorSymbolic" /*@C MatICCFactorSymbolic - Performs symbolic incomplete Cholesky factorization for a symmetric matrix. Use MatCholeskyFactorNumeric() to complete the factorization. Collective on Mat Input Parameters: + mat - the matrix . perm - row and column permutation - info - structure containing $ levels - number of levels of fill. $ expected fill - as ratio of original fill. Output Parameter: . fact - the factored matrix Notes: Most users should employ the KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^symbolic incomplete Cholesky factorization Concepts: matrices^factorization Concepts: Cholsky^symbolic factorization .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(perm,IS_CLASSID,2); PetscValidPointer(info,3); PetscValidPointer(fact,4); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (info->levels < 0) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels); if (info->fill < 1.0) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %G",info->fill); if (!(fact)->ops->iccfactorsymbolic) { const MatSolverPackage spackage; ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage); } if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); MatCheckPreallocated(mat,2); ierr = PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetSubMatrices" /*@C MatGetSubMatrices - Extracts several submatrices from a matrix. If submat points to an array of valid matrices, they may be reused to store the new submatrices. Collective on Mat Input Parameters: + mat - the matrix . n - the number of submatrixes to be extracted (on this processor, may be zero) . irow, icol - index sets of rows and columns to extract (must be sorted) - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX Output Parameter: . submat - the array of submatrices Notes: MatGetSubMatrices() can extract ONLY sequential submatrices (from both sequential and parallel matrices). Use MatGetSubMatrix() to extract a parallel submatrix. Currently both row and column indices must be sorted to guarantee correctness with all matrix types. When extracting submatrices from a parallel matrix, each processor can form a different submatrix by setting the rows and columns of its individual index sets according to the local submatrix desired. When finished using the submatrices, the user should destroy them with MatDestroyMatrices(). MAT_REUSE_MATRIX can only be used when the nonzero structure of the original matrix has not changed from that last call to MatGetSubMatrices(). This routine creates the matrices in submat; you should NOT create them before calling it. It also allocates the array of matrix pointers submat. For BAIJ matrices the index sets must respect the block structure, that is if they request one row/column in a block, they must request all rows/columns that are in that block. For example, if the block size is 2 you cannot request just row 0 and column 0. Fortran Note: The Fortran interface is slightly different from that given below; it requires one to pass in as submat a Mat (integer) array of size at least m. Level: advanced Concepts: matrices^accessing submatrices Concepts: submatrices .seealso: MatDestroyMatrices(), MatGetSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse @*/ PetscErrorCode MatGetSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) { PetscErrorCode ierr; PetscInt i; PetscBool eq; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (n) { PetscValidPointer(irow,3); PetscValidHeaderSpecific(*irow,IS_CLASSID,3); PetscValidPointer(icol,4); PetscValidHeaderSpecific(*icol,IS_CLASSID,4); } PetscValidPointer(submat,6); if (n && scall == MAT_REUSE_MATRIX) { PetscValidPointer(*submat,6); PetscValidHeaderSpecific(**submat,MAT_CLASSID,6); } if (!mat->ops->getsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getsubmatrices)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); for (i=0; isymmetric || mat->structurally_symmetric || mat->hermitian) { ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); if (eq) { if (mat->symmetric) { ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); } else if (mat->hermitian) { ierr = MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr); } else if (mat->structurally_symmetric) { ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); } } } } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetSubMatricesParallel" PetscErrorCode MatGetSubMatricesParallel(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) { PetscErrorCode ierr; PetscInt i; PetscBool eq; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (n) { PetscValidPointer(irow,3); PetscValidHeaderSpecific(*irow,IS_CLASSID,3); PetscValidPointer(icol,4); PetscValidHeaderSpecific(*icol,IS_CLASSID,4); } PetscValidPointer(submat,6); if (n && scall == MAT_REUSE_MATRIX) { PetscValidPointer(*submat,6); PetscValidHeaderSpecific(**submat,MAT_CLASSID,6); } if (!mat->ops->getsubmatricesparallel) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getsubmatricesparallel)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); for (i=0; isymmetric || mat->structurally_symmetric || mat->hermitian) { ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); if (eq) { if (mat->symmetric) { ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); } else if (mat->hermitian) { ierr = MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr); } else if (mat->structurally_symmetric) { ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); } } } } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDestroyMatrices" /*@C MatDestroyMatrices - Destroys a set of matrices obtained with MatGetSubMatrices(). Collective on Mat Input Parameters: + n - the number of local matrices - mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling sequence of MatGetSubMatrices()) Level: advanced Notes: Frees not only the matrices, but also the array that contains the matrices In Fortran will not free the array. .seealso: MatGetSubMatrices() @*/ PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[]) { PetscErrorCode ierr; PetscInt i; PetscFunctionBegin; if (!*mat) PetscFunctionReturn(0); if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n); PetscValidPointer(mat,2); for (i=0; ifactortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); if (!mat->ops->getseqnonzerostructure) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name); ierr = PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getseqnonzerostructure)(mat,matstruct);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDestroySeqNonzeroStructure" /*@C MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure(). Collective on Mat Input Parameters: . mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling sequence of MatGetSequentialNonzeroStructure()) Level: advanced Notes: Frees not only the matrices, but also the array that contains the matrices .seealso: MatGetSeqNonzeroStructure() @*/ PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidPointer(mat,1); ierr = MatDestroy(mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIncreaseOverlap" /*@ MatIncreaseOverlap - Given a set of submatrices indicated by index sets, replaces the index sets by larger ones that represent submatrices with additional overlap. Collective on Mat Input Parameters: + mat - the matrix . n - the number of index sets . is - the array of index sets (these index sets will changed during the call) - ov - the additional overlap requested Level: developer Concepts: overlap Concepts: ASM^computing overlap .seealso: MatGetSubMatrices() @*/ PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n); if (n) { PetscValidPointer(is,3); PetscValidHeaderSpecific(*is,IS_CLASSID,3); } if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); if (!ov) PetscFunctionReturn(0); if (!mat->ops->increaseoverlap) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->increaseoverlap)(mat,n,is,ov);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetBlockSize" /*@ MatGetBlockSize - Returns the matrix block size; useful especially for the block row and block diagonal formats. Not Collective Input Parameter: . mat - the matrix Output Parameter: . bs - block size Notes: Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ Level: intermediate Concepts: matrices^block size .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes() @*/ PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(bs,2); MatCheckPreallocated(mat,1); *bs = mat->rmap->bs; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetBlockSizes" /*@ MatGetBlockSizes - Returns the matrix block row and column sizes; useful especially for the block row and block diagonal formats. Not Collective Input Parameter: . mat - the matrix Output Parameter: . rbs - row block size . cbs - coumn block size Notes: Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ Level: intermediate Concepts: matrices^block size .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() @*/ PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (rbs) PetscValidIntPointer(rbs,2); if (cbs) PetscValidIntPointer(cbs,3); MatCheckPreallocated(mat,1); if (rbs) *rbs = mat->rmap->bs; if (cbs) *cbs = mat->cmap->bs; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetBlockSize" /*@ MatSetBlockSize - Sets the matrix block size. Logically Collective on Mat Input Parameters: + mat - the matrix - bs - block size Notes: This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later Level: intermediate Concepts: matrices^block size .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() @*/ PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidLogicalCollectiveInt(mat,bs,2); ierr = PetscLayoutSetBlockSize(mat->rmap,bs);CHKERRQ(ierr); ierr = PetscLayoutSetBlockSize(mat->cmap,bs);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetBlockSizes" /*@ MatSetBlockSizes - Sets the matrix block row and column sizes. Logically Collective on Mat Input Parameters: + mat - the matrix - rbs - row block size - cbs - column block size Notes: This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later Level: intermediate Concepts: matrices^block size .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() @*/ PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidLogicalCollectiveInt(mat,rbs,2); ierr = PetscLayoutSetBlockSize(mat->rmap,rbs);CHKERRQ(ierr); ierr = PetscLayoutSetBlockSize(mat->cmap,cbs);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowIJ" /*@C MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices. Collective on Mat Input Parameters: + mat - the matrix . shift - 0 or 1 indicating we want the indices starting at 0 or 1 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be symmetrized - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is always used. Output Parameters: + n - number of rows in the (possibly compressed) matrix . ia - the row pointers [of length n+1] . ja - the column indices - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set Level: developer Notes: You CANNOT change any of the ia[] or ja[] values. Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values Fortran Node In Fortran use $ PetscInt ia(1), ja(1) $ PetscOffset iia, jja $ call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr) $ $ or $ $ PetscScalar, pointer :: xx_v(:) $ call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr) Acess the ith and jth entries via ia(iia + i) and ja(jja + j) .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray() @*/ PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(n,4); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); MatCheckPreallocated(mat,1); if (!mat->ops->getrowij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetColumnIJ" /*@C MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices. Collective on Mat Input Parameters: + mat - the matrix . shift - 1 or zero indicating we want the indices starting at 0 or 1 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be symmetrized - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is always used. Output Parameters: + n - number of columns in the (possibly compressed) matrix . ia - the column pointers . ja - the row indices - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned Level: developer .seealso: MatGetRowIJ(), MatRestoreColumnIJ() @*/ PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(n,4); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); MatCheckPreallocated(mat,1); if (!mat->ops->getcolumnij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestoreRowIJ" /*@C MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with MatGetRowIJ(). Collective on Mat Input Parameters: + mat - the matrix . shift - 1 or zero indicating we want the indices starting at 0 or 1 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be symmetrized - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is always used. Output Parameters: + n - size of (possibly compressed) matrix . ia - the row pointers . ja - the column indices - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned Level: developer .seealso: MatGetRowIJ(), MatRestoreColumnIJ() @*/ PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); MatCheckPreallocated(mat,1); if (!mat->ops->restorerowij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestoreColumnIJ" /*@C MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with MatGetColumnIJ(). Collective on Mat Input Parameters: + mat - the matrix . shift - 1 or zero indicating we want the indices starting at 0 or 1 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be symmetrized - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is always used. Output Parameters: + n - size of (possibly compressed) matrix . ia - the column pointers . ja - the row indices - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned Level: developer .seealso: MatGetColumnIJ(), MatRestoreRowIJ() @*/ PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); MatCheckPreallocated(mat,1); if (!mat->ops->restorecolumnij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatColoringPatch" /*@C MatColoringPatch -Used inside matrix coloring routines that use MatGetRowIJ() and/or MatGetColumnIJ(). Collective on Mat Input Parameters: + mat - the matrix . ncolors - max color value . n - number of entries in colorarray - colorarray - array indicating color for each column Output Parameters: . iscoloring - coloring generated using colorarray information Level: developer .seealso: MatGetRowIJ(), MatGetColumnIJ() @*/ PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidIntPointer(colorarray,4); PetscValidPointer(iscoloring,5); MatCheckPreallocated(mat,1); if (!mat->ops->coloringpatch) { ierr = ISColoringCreate(((PetscObject)mat)->comm,ncolors,n,colorarray,iscoloring);CHKERRQ(ierr); } else { ierr = (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetUnfactored" /*@ MatSetUnfactored - Resets a factored matrix to be treated as unfactored. Logically Collective on Mat Input Parameter: . mat - the factored matrix to be reset Notes: This routine should be used only with factored matrices formed by in-place factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE format). This option can save memory, for example, when solving nonlinear systems with a matrix-free Newton-Krylov method and a matrix-based, in-place ILU(0) preconditioner. Note that one can specify in-place ILU(0) factorization by calling .vb PCType(pc,PCILU); PCFactorSeUseInPlace(pc); .ve or by using the options -pc_type ilu -pc_factor_in_place In-place factorization ILU(0) can also be used as a local solver for the blocks within the block Jacobi or additive Schwarz methods (runtime option: -sub_pc_factor_in_place). See the discussion of these preconditioners in the PC chapter of the users manual for details on setting local solver options. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer .seealso: PCFactorSetUseInPlace() Concepts: matrices^unfactored @*/ PetscErrorCode MatSetUnfactored(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); mat->factortype = MAT_FACTOR_NONE; if (!mat->ops->setunfactored) PetscFunctionReturn(0); ierr = (*mat->ops->setunfactored)(mat);CHKERRQ(ierr); PetscFunctionReturn(0); } /*MC MatDenseGetArrayF90 - Accesses a matrix array from Fortran90. Synopsis: MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr) Not collective Input Parameter: . x - matrix Output Parameters: + xx_v - the Fortran90 pointer to the array - ierr - error code Example of Usage: .vb PetscScalar, pointer xx_v(:,:) .... call MatDenseGetArrayF90(x,xx_v,ierr) a = xx_v(3) call MatDenseRestoreArrayF90(x,xx_v,ierr) .ve Level: advanced .seealso: MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray() Concepts: matrices^accessing array M*/ /*MC MatDenseRestoreArrayF90 - Restores a matrix array that has been accessed with MatGetArrayF90(). Synopsis: MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) Not collective Input Parameters: + x - matrix - xx_v - the Fortran90 pointer to the array Output Parameter: . ierr - error code Example of Usage: .vb PetscScalar, pointer xx_v(:) .... call MatDenseGetArrayF90(x,xx_v,ierr) a = xx_v(3) call MatDenseRestoreArrayF90(x,xx_v,ierr) .ve Level: advanced .seealso: MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray() M*/ #undef __FUNCT__ #define __FUNCT__ "MatGetSubMatrix" /*@ MatGetSubMatrix - Gets a single submatrix on the same number of processors as the original matrix. Collective on Mat Input Parameters: + mat - the original matrix . isrow - parallel IS containing the rows this processor should obtain . 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. - cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX Output Parameter: . newmat - the new submatrix, of the same type as the old Level: advanced Notes: The submatrix will be able to be multiplied with vectors using the same layout as iscol. The rows in isrow will be sorted into the same order as the original matrix on each process. The first time this is called you should use a cll of MAT_INITIAL_MATRIX, the MatGetSubMatrix() routine will create the newmat for you. Any additional calls to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX will reuse the matrix generated the first time. You should call MatDestroy() on newmat when you are finished using it. The communicator of the newly obtained matrix is ALWAYS the same as the communicator of the input matrix. If iscol is PETSC_NULL then all columns are obtained (not supported in Fortran). Example usage: Consider the following 8x8 matrix with 34 non-zero values, that is assembled across 3 processors. Let's assume that proc0 owns 3 rows, proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown as follows: .vb 1 2 0 | 0 3 0 | 0 4 Proc0 0 5 6 | 7 0 0 | 8 0 9 0 10 | 11 0 0 | 12 0 ------------------------------------- 13 0 14 | 15 16 17 | 0 0 Proc1 0 18 0 | 19 20 21 | 0 0 0 0 0 | 22 23 0 | 24 0 ------------------------------------- Proc2 25 26 27 | 0 0 28 | 29 0 30 0 0 | 31 32 33 | 0 34 .ve Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6]. The resulting submatrix is .vb 2 0 | 0 3 0 | 0 Proc0 5 6 | 7 0 0 | 8 ------------------------------- Proc1 18 0 | 19 20 21 | 0 ------------------------------- Proc2 26 27 | 0 0 28 | 29 0 0 | 31 32 33 | 0 .ve Concepts: matrices^submatrices .seealso: MatGetSubMatrices() @*/ PetscErrorCode MatGetSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat) { PetscErrorCode ierr; PetscMPIInt size; Mat *local; IS iscoltmp; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(isrow,IS_CLASSID,2); if (iscol) PetscValidHeaderSpecific(iscol,IS_CLASSID,3); PetscValidPointer(newmat,5); if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat,MAT_CLASSID,5); PetscValidType(mat,1); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = MPI_Comm_size(((PetscObject)mat)->comm,&size);CHKERRQ(ierr); if (!iscol) { ierr = ISCreateStride(((PetscObject)mat)->comm,mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);CHKERRQ(ierr); } else { iscoltmp = iscol; } /* if original matrix is on just one processor then use submatrix generated */ if (mat->ops->getsubmatrices && !mat->ops->getsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) { ierr = MatGetSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);CHKERRQ(ierr); if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} PetscFunctionReturn(0); } else if (mat->ops->getsubmatrices && !mat->ops->getsubmatrix && size == 1) { ierr = MatGetSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); *newmat = *local; ierr = PetscFree(local);CHKERRQ(ierr); if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} PetscFunctionReturn(0); } else if (!mat->ops->getsubmatrix) { /* Create a new matrix type that implements the operation using the full matrix */ switch (cll) { case MAT_INITIAL_MATRIX: ierr = MatCreateSubMatrix(mat,isrow,iscoltmp,newmat);CHKERRQ(ierr); break; case MAT_REUSE_MATRIX: ierr = MatSubMatrixUpdate(*newmat,mat,isrow,iscoltmp);CHKERRQ(ierr); break; default: SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX"); } if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} PetscFunctionReturn(0); } if (!mat->ops->getsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = (*mat->ops->getsubmatrix)(mat,isrow,iscoltmp,cll,newmat);CHKERRQ(ierr); if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} if (*newmat && cll == MAT_INITIAL_MATRIX) {ierr = PetscObjectStateIncrease((PetscObject)*newmat);CHKERRQ(ierr);} PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatStashSetInitialSize" /*@ MatStashSetInitialSize - sets the sizes of the matrix stash, that is used during the assembly process to store values that belong to other processors. Not Collective Input Parameters: + mat - the matrix . size - the initial size of the stash. - bsize - the initial size of the block-stash(if used). Options Database Keys: + -matstash_initial_size or - -matstash_block_initial_size or Level: intermediate Notes: The block-stash is used for values set with MatSetValuesBlocked() while the stash is used for values set with MatSetValues() Run with the option -info and look for output of the form MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs. to determine the appropriate value, MM, to use for size and MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs. to determine the value, BMM to use for bsize Concepts: stash^setting matrix size Concepts: matrices^stash .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo() @*/ PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); ierr = MatStashSetInitialSize_Private(&mat->stash,size);CHKERRQ(ierr); ierr = MatStashSetInitialSize_Private(&mat->bstash,bsize);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatInterpolateAdd" /*@ MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of the matrix Neighbor-wise Collective on Mat Input Parameters: + mat - the matrix . x,y - the vectors - w - where the result is stored Level: intermediate Notes: w may be the same vector as y. This allows one to use either the restriction or interpolation (its transpose) matrix to do the interpolation Concepts: interpolation .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() @*/ PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w) { PetscErrorCode ierr; PetscInt M,N,Ny; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); PetscValidHeaderSpecific(w,VEC_CLASSID,4); PetscValidType(A,1); MatCheckPreallocated(A,1); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); if (M == Ny) { ierr = MatMultAdd(A,x,y,w);CHKERRQ(ierr); } else { ierr = MatMultTransposeAdd(A,x,y,w);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatInterpolate" /*@ MatInterpolate - y = A*x or A'*x depending on the shape of the matrix Neighbor-wise Collective on Mat Input Parameters: + mat - the matrix - x,y - the vectors Level: intermediate Notes: This allows one to use either the restriction or interpolation (its transpose) matrix to do the interpolation Concepts: matrices^interpolation .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() @*/ PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y) { PetscErrorCode ierr; PetscInt M,N,Ny; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); PetscValidType(A,1); MatCheckPreallocated(A,1); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); if (M == Ny) { ierr = MatMult(A,x,y);CHKERRQ(ierr); } else { ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestrict" /*@ MatRestrict - y = A*x or A'*x Neighbor-wise Collective on Mat Input Parameters: + mat - the matrix - x,y - the vectors Level: intermediate Notes: This allows one to use either the restriction or interpolation (its transpose) matrix to do the restriction Concepts: matrices^restriction .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate() @*/ PetscErrorCode MatRestrict(Mat A,Vec x,Vec y) { PetscErrorCode ierr; PetscInt M,N,Ny; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidHeaderSpecific(x,VEC_CLASSID,2); PetscValidHeaderSpecific(y,VEC_CLASSID,3); PetscValidType(A,1); MatCheckPreallocated(A,1); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); if (M == Ny) { ierr = MatMult(A,x,y);CHKERRQ(ierr); } else { ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetNullSpace" /*@ MatGetNullSpace - retrieves the null space to a matrix. Logically Collective on Mat and MatNullSpace Input Parameters: + mat - the matrix - nullsp - the null space object Level: developer Notes: This null space is used by solvers. Overwrites any previous null space that may have been attached Concepts: null space^attaching to matrix .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace() @*/ PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(nullsp,2); *nullsp = mat->nullsp; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetNullSpace" /*@ MatSetNullSpace - attaches a null space to a matrix. This null space will be removed from the resulting vector whenever MatMult() is called Logically Collective on Mat and MatNullSpace Input Parameters: + mat - the matrix - nullsp - the null space object Level: advanced Notes: This null space is used by solvers. Overwrites any previous null space that may have been attached Concepts: null space^attaching to matrix .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace() @*/ PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_CLASSID,2); MatCheckPreallocated(mat,1); ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); ierr = MatNullSpaceDestroy(&mat->nullsp);CHKERRQ(ierr); mat->nullsp = nullsp; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetNearNullSpace" /*@ MatSetNearNullSpace - attaches a null space to a matrix. This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix. Logically Collective on Mat and MatNullSpace Input Parameters: + mat - the matrix - nullsp - the null space object Level: advanced Notes: Overwrites any previous near null space that may have been attached Concepts: null space^attaching to matrix .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace() @*/ PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_CLASSID,2); MatCheckPreallocated(mat,1); ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); ierr = MatNullSpaceDestroy(&mat->nearnullsp);CHKERRQ(ierr); mat->nearnullsp = nullsp; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetNearNullSpace" /*@ MatGetNearNullSpace -Get null space attached with MatSetNearNullSpace() Not Collective Input Parameters: . mat - the matrix Output Parameters: . nullsp - the null space object, PETSC_NULL if not set Level: developer Concepts: null space^attaching to matrix .seealso: MatSetNearNullSpace(), MatGetNullSpace() @*/ PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(nullsp,2); MatCheckPreallocated(mat,1); *nullsp = mat->nearnullsp; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatICCFactor" /*@C MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix. Collective on Mat Input Parameters: + mat - the matrix . row - row/column permutation . fill - expected fill factor >= 1.0 - level - level of fill, for ICC(k) Notes: Probably really in-place only when level of fill is zero, otherwise allocates new space to store factored matrix and deletes previous memory. Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^incomplete Cholesky factorization Concepts: Cholesky factorization .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); PetscValidPointer(info,3); if (mat->rmap->N != mat->cmap->N) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONG,"matrix must be square"); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->iccfactor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = (*mat->ops->iccfactor)(mat,row,info);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesAdifor" /*@ MatSetValuesAdifor - Sets values computed with automatic differentiation into a matrix. Not Collective Input Parameters: + mat - the matrix . nl - leading dimension of v - v - the values compute with ADIFOR Level: developer Notes: Must call MatSetColoring() before using this routine. Also this matrix must already have its nonzero pattern determined. .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), MatSetValues(), MatSetColoring() @*/ PetscErrorCode MatSetValuesAdifor(Mat mat,PetscInt nl,void *v) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); PetscValidPointer(v,3); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (!mat->ops->setvaluesadifor) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = (*mat->ops->setvaluesadifor)(mat,nl,v);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDiagonalScaleLocal" /*@ MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the ghosted ones. Not Collective Input Parameters: + mat - the matrix - diag = the diagonal values, including ghost ones Level: developer Notes: Works only for MPIAIJ and MPIBAIJ matrices .seealso: MatDiagonalScale() @*/ PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag) { PetscErrorCode ierr; PetscMPIInt size; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(diag,VEC_CLASSID,2); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = MPI_Comm_size(((PetscObject)mat)->comm,&size);CHKERRQ(ierr); if (size == 1) { PetscInt n,m; ierr = VecGetSize(diag,&n);CHKERRQ(ierr); ierr = MatGetSize(mat,0,&m);CHKERRQ(ierr); if (m == n) { ierr = MatDiagonalScale(mat,0,diag);CHKERRQ(ierr); } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions"); } else { ierr = PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetInertia" /*@ MatGetInertia - Gets the inertia from a factored matrix Collective on Mat Input Parameter: . mat - the matrix Output Parameters: + nneg - number of negative eigenvalues . nzero - number of zero eigenvalues - npos - number of positive eigenvalues Level: advanced Notes: Matrix must have been factored by MatCholeskyFactor() @*/ PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled"); if (!mat->ops->getinertia) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); ierr = (*mat->ops->getinertia)(mat,nneg,nzero,npos);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatSolves" /*@C MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors Neighbor-wise Collective on Mat and Vecs Input Parameters: + mat - the factored matrix - b - the right-hand-side vectors Output Parameter: . x - the result vectors Notes: The vectors b and x cannot be the same. I.e., one cannot call MatSolves(A,x,x). Notes: Most users should employ the simplified KSP interface for linear solvers instead of working directly with matrix algebra routines such as this. See, e.g., KSPCreate(). Level: developer Concepts: matrices^triangular solves .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve() @*/ PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (x == b) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(0); if (!mat->ops->solves) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->solves)(mat,b,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsSymmetric" /*@ MatIsSymmetric - Test whether a matrix is symmetric Collective on Mat Input Parameter: + A - the matrix to test - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose) Output Parameters: . flg - the result Notes: For real numbers MatIsSymmetric() and MatIsHermitian() return identical results Level: intermediate Concepts: matrix^symmetry .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown() @*/ PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidPointer(flg,2); if (!A->symmetric_set) { if (!A->ops->issymmetric) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); } ierr = (*A->ops->issymmetric)(A,tol,flg);CHKERRQ(ierr); if (!tol) { A->symmetric_set = PETSC_TRUE; A->symmetric = *flg; if (A->symmetric) { A->structurally_symmetric_set = PETSC_TRUE; A->structurally_symmetric = PETSC_TRUE; } } } else if (A->symmetric) { *flg = PETSC_TRUE; } else if (!tol) { *flg = PETSC_FALSE; } else { if (!A->ops->issymmetric) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); } ierr = (*A->ops->issymmetric)(A,tol,flg);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsHermitian" /*@ MatIsHermitian - Test whether a matrix is Hermitian Collective on Mat Input Parameter: + A - the matrix to test - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian) Output Parameters: . flg - the result Level: intermediate Concepts: matrix^symmetry .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown(), MatIsSymmetric() @*/ PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidPointer(flg,2); if (!A->hermitian_set) { if (!A->ops->ishermitian) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype); } ierr = (*A->ops->ishermitian)(A,tol,flg);CHKERRQ(ierr); if (!tol) { A->hermitian_set = PETSC_TRUE; A->hermitian = *flg; if (A->hermitian) { A->structurally_symmetric_set = PETSC_TRUE; A->structurally_symmetric = PETSC_TRUE; } } } else if (A->hermitian) { *flg = PETSC_TRUE; } else if (!tol) { *flg = PETSC_FALSE; } else { if (!A->ops->ishermitian) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype); } ierr = (*A->ops->ishermitian)(A,tol,flg);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsSymmetricKnown" /*@ MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric. Not Collective Input Parameter: . A - the matrix to check Output Parameters: + set - if the symmetric flag is set (this tells you if the next flag is valid) - flg - the result Level: advanced Concepts: matrix^symmetry Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric() if you want it explicitly checked .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() @*/ PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool *set,PetscBool *flg) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidPointer(set,2); PetscValidPointer(flg,3); if (A->symmetric_set) { *set = PETSC_TRUE; *flg = A->symmetric; } else { *set = PETSC_FALSE; } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsHermitianKnown" /*@ MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian. Not Collective Input Parameter: . A - the matrix to check Output Parameters: + set - if the hermitian flag is set (this tells you if the next flag is valid) - flg - the result Level: advanced Concepts: matrix^symmetry Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian() if you want it explicitly checked .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() @*/ PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool *set,PetscBool *flg) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidPointer(set,2); PetscValidPointer(flg,3); if (A->hermitian_set) { *set = PETSC_TRUE; *flg = A->hermitian; } else { *set = PETSC_FALSE; } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsStructurallySymmetric" /*@ MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric Collective on Mat Input Parameter: . A - the matrix to test Output Parameters: . flg - the result Level: intermediate Concepts: matrix^symmetry .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption() @*/ PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidPointer(flg,2); if (!A->structurally_symmetric_set) { if (!A->ops->isstructurallysymmetric) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric"); ierr = (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);CHKERRQ(ierr); A->structurally_symmetric_set = PETSC_TRUE; } *flg = A->structurally_symmetric; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatStashGetInfo" extern PetscErrorCode MatStashGetInfo_Private(MatStash*,PetscInt*,PetscInt*); /*@ MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need to be communicated to other processors during the MatAssemblyBegin/End() process Not collective Input Parameter: . vec - the vector Output Parameters: + nstash - the size of the stash . reallocs - the number of additional mallocs incurred. . bnstash - the size of the block stash - breallocs - the number of additional mallocs incurred.in the block stash Level: advanced .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize() @*/ PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs) { PetscErrorCode ierr; PetscFunctionBegin; ierr = MatStashGetInfo_Private(&mat->stash,nstash,reallocs);CHKERRQ(ierr); ierr = MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetVecs" /*@C MatGetVecs - Get vector(s) compatible with the matrix, i.e. with the same parallel layout Collective on Mat Input Parameter: . mat - the matrix Output Parameter: + right - (optional) vector that the matrix can be multiplied against - left - (optional) vector that the matrix vector product can be stored in Level: advanced .seealso: MatCreate() @*/ PetscErrorCode MatGetVecs(Mat mat,Vec *right,Vec *left) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); MatCheckPreallocated(mat,1); if (mat->ops->getvecs) { ierr = (*mat->ops->getvecs)(mat,right,left);CHKERRQ(ierr); } else { PetscMPIInt size; ierr = MPI_Comm_size(((PetscObject)mat)->comm, &size);CHKERRQ(ierr); if (right) { ierr = VecCreate(((PetscObject)mat)->comm,right);CHKERRQ(ierr); ierr = VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);CHKERRQ(ierr); ierr = VecSetBlockSize(*right,mat->rmap->bs);CHKERRQ(ierr); ierr = VecSetType(*right,VECSTANDARD);CHKERRQ(ierr); ierr = PetscLayoutReference(mat->cmap,&(*right)->map);CHKERRQ(ierr); } if (left) { ierr = VecCreate(((PetscObject)mat)->comm,left);CHKERRQ(ierr); ierr = VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);CHKERRQ(ierr); ierr = VecSetBlockSize(*left,mat->rmap->bs);CHKERRQ(ierr); ierr = VecSetType(*left,VECSTANDARD);CHKERRQ(ierr); ierr = PetscLayoutReference(mat->rmap,&(*left)->map);CHKERRQ(ierr); } } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatFactorInfoInitialize" /*@C MatFactorInfoInitialize - Initializes a MatFactorInfo data structure with default values. Not Collective Input Parameters: . info - the MatFactorInfo data structure Notes: The solvers are generally used through the KSP and PC objects, for example PCLU, PCILU, PCCHOLESKY, PCICC Level: developer .seealso: MatFactorInfo Developer Note: fortran interface is not autogenerated as the f90 interface defintion cannot be generated correctly [due to MatFactorInfo] @*/ PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; ierr = PetscMemzero(info,sizeof(MatFactorInfo));CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPtAP" /*@ MatPtAP - Creates the matrix product C = P^T * A * P Neighbor-wise Collective on Mat Input Parameters: + A - the matrix . P - the projection matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)) Output Parameters: . C - the product matrix Notes: C will be created and must be destroyed by the user with MatDestroy(). This routine is currently only implemented for pairs of AIJ matrices and classes which inherit from AIJ. Level: intermediate .seealso: MatPtAPSymbolic(), MatPtAPNumeric(), MatMatMult(), MatRARt() @*/ PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fP)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*ptap)(Mat,Mat,MatReuse,PetscReal,Mat*)=PETSC_NULL; PetscBool viatranspose=PETSC_FALSE,viamatmatmatmult=PETSC_FALSE; PetscFunctionBegin; ierr = PetscOptionsGetBool(((PetscObject)A)->prefix,"-matptap_viatranspose",&viatranspose,PETSC_NULL);CHKERRQ(ierr); ierr = PetscOptionsGetBool(((PetscObject)A)->prefix,"-matptap_viamatmatmatmult",&viamatmatmatmult,PETSC_NULL);CHKERRQ(ierr); PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); MatCheckPreallocated(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(P,MAT_CLASSID,2); PetscValidType(P,2); MatCheckPreallocated(P,2); if (!P->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (P->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); if (scall == MAT_REUSE_MATRIX) { PetscValidPointer(*C,5); PetscValidHeaderSpecific(*C,MAT_CLASSID,5); if (viatranspose || viamatmatmatmult) { Mat Pt; ierr = MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);CHKERRQ(ierr); if (viamatmatmatmult) { ierr = MatMatMatMult(Pt,A,P,scall,fill,C);CHKERRQ(ierr); } else { Mat AP; ierr = MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);CHKERRQ(ierr); ierr = MatMatMult(Pt,AP,scall,fill,C);CHKERRQ(ierr); ierr = MatDestroy(&AP);CHKERRQ(ierr); } ierr = MatDestroy(&Pt);CHKERRQ(ierr); } else { ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); ierr = (*(*C)->ops->ptapnumeric)(A,P,*C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); } PetscFunctionReturn(0); } if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); fA = A->ops->ptap; fP = P->ops->ptap; if (fP == fA) { if (!fA) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",((PetscObject)A)->type_name); ptap = fA; } else { /* dispatch based on the type of A and P from their PetscObject's PetscFunctionLists. */ char ptapname[256]; ierr = PetscStrcpy(ptapname,"MatPtAP_");CHKERRQ(ierr); ierr = PetscStrcat(ptapname,((PetscObject)A)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(ptapname,"_");CHKERRQ(ierr); ierr = PetscStrcat(ptapname,((PetscObject)P)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(ptapname,"_C");CHKERRQ(ierr); /* e.g., ptapname = "MatPtAP_seqdense_seqaij_C" */ ierr = PetscObjectQueryFunction((PetscObject)P,ptapname,(void (**)(void))&ptap);CHKERRQ(ierr); if (!ptap) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatPtAP requires A, %s, to be compatible with P, %s",((PetscObject)A)->type_name,((PetscObject)P)->type_name); } if (viatranspose || viamatmatmatmult) { Mat Pt; ierr = MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);CHKERRQ(ierr); if (viamatmatmatmult) { ierr = MatMatMatMult(Pt,A,P,scall,fill,C);CHKERRQ(ierr); ierr = PetscInfo(*C,"MatPtAP via MatMatMatMult\n");CHKERRQ(ierr); } else { Mat AP; ierr = MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);CHKERRQ(ierr); ierr = MatMatMult(Pt,AP,scall,fill,C);CHKERRQ(ierr); ierr = MatDestroy(&AP);CHKERRQ(ierr); ierr = PetscInfo(*C,"MatPtAP via MatTranspose and MatMatMult\n");CHKERRQ(ierr); } ierr = MatDestroy(&Pt);CHKERRQ(ierr); } else { ierr = PetscLogEventBegin(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); ierr = (*ptap)(A,P,scall,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPtAPNumeric" /*@ MatPtAPNumeric - Computes the matrix product C = P^T * A * P Neighbor-wise Collective on Mat Input Parameters: + A - the matrix - P - the projection matrix Output Parameters: . C - the product matrix Notes: C must have been created by calling MatPtAPSymbolic and must be destroyed by the user using MatDeatroy(). This routine is currently only implemented for pairs of AIJ matrices and classes which inherit from AIJ. C will be of type MATAIJ. Level: intermediate .seealso: MatPtAP(), MatPtAPSymbolic(), MatMatMultNumeric() @*/ PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(P,MAT_CLASSID,2); PetscValidType(P,2); MatCheckPreallocated(P,2); if (!P->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(C,MAT_CLASSID,3); PetscValidType(C,3); MatCheckPreallocated(C,3); if (C->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (P->cmap->N!=C->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->rmap->N); if (P->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); if (A->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); if (P->cmap->N!=C->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->cmap->N); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); ierr = (*C->ops->ptapnumeric)(A,P,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPtAPSymbolic" /*@ MatPtAPSymbolic - Creates the (i,j) structure of the matrix product C = P^T * A * P Neighbor-wise Collective on Mat Input Parameters: + A - the matrix - P - the projection matrix Output Parameters: . C - the (i,j) structure of the product matrix Notes: C will be created and must be destroyed by the user with MatDestroy(). This routine is currently only implemented for pairs of SeqAIJ matrices and classes which inherit from SeqAIJ. C will be of type MATSEQAIJ. The product is computed using this (i,j) structure by calling MatPtAPNumeric(). Level: intermediate .seealso: MatPtAP(), MatPtAPNumeric(), MatMatMultSymbolic() @*/ PetscErrorCode MatPtAPSymbolic(Mat A,Mat P,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (fill <1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); PetscValidHeaderSpecific(P,MAT_CLASSID,2); PetscValidType(P,2); MatCheckPreallocated(P,2); if (!P->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (P->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); if (A->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); ierr = (*A->ops->ptapsymbolic)(A,P,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); /* ierr = MatSetBlockSize(*C,A->rmap->bs);CHKERRQ(ierr); NO! this is not always true -ma */ PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRARt" /*@ MatRARt - Creates the matrix product C = R * A * R^T Neighbor-wise Collective on Mat Input Parameters: + A - the matrix . R - the projection matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - fill - expected fill as ratio of nnz(C)/nnz(A) Output Parameters: . C - the product matrix Notes: C will be created and must be destroyed by the user with MatDestroy(). This routine is currently only implemented for pairs of AIJ matrices and classes which inherit from AIJ. Level: intermediate .seealso: MatRARtSymbolic(), MatRARtNumeric(), MatMatMult(), MatPtAP() @*/ PetscErrorCode MatRARt(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(R,MAT_CLASSID,2); PetscValidType(R,2); MatCheckPreallocated(R,2); if (!R->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (R->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (R->cmap->N!=A->rmap->N) SETERRQ2(((PetscObject)R)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); MatCheckPreallocated(A,1); if (!A->ops->rart) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"Matrix of type <%s> does not support RARt",mattype); } ierr = PetscLogEventBegin(MAT_RARt,A,R,0,0);CHKERRQ(ierr); ierr = (*A->ops->rart)(A,R,scall,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_RARt,A,R,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRARtNumeric" /*@ MatRARtNumeric - Computes the matrix product C = R * A * R^T Neighbor-wise Collective on Mat Input Parameters: + A - the matrix - R - the projection matrix Output Parameters: . C - the product matrix Notes: C must have been created by calling MatRARtSymbolic and must be destroyed by the user using MatDeatroy(). This routine is currently only implemented for pairs of AIJ matrices and classes which inherit from AIJ. C will be of type MATAIJ. Level: intermediate .seealso: MatRARt(), MatRARtSymbolic(), MatMatMultNumeric() @*/ PetscErrorCode MatRARtNumeric(Mat A,Mat R,Mat C) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(R,MAT_CLASSID,2); PetscValidType(R,2); MatCheckPreallocated(R,2); if (!R->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (R->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(C,MAT_CLASSID,3); PetscValidType(C,3); MatCheckPreallocated(C,3); if (C->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (R->rmap->N!=C->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->rmap->N); if (R->cmap->N!=A->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); if (A->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); if (R->rmap->N!=C->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->cmap->N); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); ierr = (*A->ops->rartnumeric)(A,R,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRARtSymbolic" /*@ MatRARtSymbolic - Creates the (i,j) structure of the matrix product C = R * A * R^T Neighbor-wise Collective on Mat Input Parameters: + A - the matrix - R - the projection matrix Output Parameters: . C - the (i,j) structure of the product matrix Notes: C will be created and must be destroyed by the user with MatDestroy(). This routine is currently only implemented for pairs of SeqAIJ matrices and classes which inherit from SeqAIJ. C will be of type MATSEQAIJ. The product is computed using this (i,j) structure by calling MatRARtNumeric(). Level: intermediate .seealso: MatRARt(), MatRARtNumeric(), MatMatMultSymbolic() @*/ PetscErrorCode MatRARtSymbolic(Mat A,Mat R,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (fill <1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); PetscValidHeaderSpecific(R,MAT_CLASSID,2); PetscValidType(R,2); MatCheckPreallocated(R,2); if (!R->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (R->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (R->cmap->N!=A->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); if (A->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); MatCheckPreallocated(A,1); ierr = PetscLogEventBegin(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); ierr = (*A->ops->rartsymbolic)(A,R,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); ierr = MatSetBlockSize(*C,A->rmap->bs);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatMult" /*@ MatMatMult - Performs Matrix-Matrix Multiplication C=A*B. Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix . B - the right matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate if the result is a dense matrix this is irrelevent Output Parameters: . C - the product matrix Notes: Unless scall is MAT_REUSE_MATRIX C will be created. MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed. If you have many matrices with the same non-zero structure to multiply, you should either $ 1) use MAT_REUSE_MATRIX in all calls but the first or $ 2) call MatMatMultSymbolic() once and then MatMatMultNumeric() for each product needed Level: intermediate .seealso: MatMatMultSymbolic(), MatMatMultNumeric(), MatTransposeMatMult(), MatMatTransposeMult(), MatPtAP() @*/ PetscErrorCode MatMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*mult)(Mat,Mat,MatReuse,PetscReal,Mat*)=PETSC_NULL; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); MatCheckPreallocated(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(B,2); MatCheckPreallocated(B,2); if (!B->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (B->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (B->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); if (scall == MAT_REUSE_MATRIX) { PetscValidPointer(*C,5); PetscValidHeaderSpecific(*C,MAT_CLASSID,5); ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); ierr = (*(*C)->ops->matmultnumeric)(A,B,*C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); fA = A->ops->matmult; fB = B->ops->matmult; if (fB == fA) { if (!fB) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatMatMult not supported for B of type %s",((PetscObject)B)->type_name); mult = fB; } else { /* dispatch based on the type of A and B from their PetscObject's PetscFunctionLists. */ char multname[256]; ierr = PetscStrcpy(multname,"MatMatMult_");CHKERRQ(ierr); ierr = PetscStrcat(multname,((PetscObject)A)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); ierr = PetscStrcat(multname,((PetscObject)B)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(multname,"_C");CHKERRQ(ierr); /* e.g., multname = "MatMatMult_seqdense_seqaij_C" */ ierr = PetscObjectQueryFunction((PetscObject)B,multname,(void (**)(void))&mult);CHKERRQ(ierr); if (!mult) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatMatMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); } ierr = PetscLogEventBegin(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); ierr = (*mult)(A,B,scall,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatMultSymbolic" /*@ MatMatMultSymbolic - Performs construction, preallocation, and computes the ij structure of the matrix-matrix product C=A*B. Call this routine before calling MatMatMultNumeric(). Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix . B - the right matrix - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate, if C is a dense matrix this is irrelevent Output Parameters: . C - the product matrix Notes: Unless scall is MAT_REUSE_MATRIX C will be created. To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed. This routine is currently implemented for - pairs of AIJ matrices and classes which inherit from AIJ, C will be of type AIJ - pairs of AIJ (A) and Dense (B) matrix, C will be of type Dense. - pairs of Dense (A) and AIJ (B) matrix, C will be of type Dense. Level: intermediate Developers Note: There are ways to estimate the number of nonzeros in the resulting product, see for example, http://arxiv.org/abs/1006.4173 We should incorporate them into PETSc. .seealso: MatMatMult(), MatMatMultNumeric() @*/ PetscErrorCode MatMatMultSymbolic(Mat A,Mat B,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscErrorCode (*Asymbolic)(Mat,Mat,PetscReal,Mat*); PetscErrorCode (*Bsymbolic)(Mat,Mat,PetscReal,Mat*); PetscErrorCode (*symbolic)(Mat,Mat,PetscReal,Mat*)=PETSC_NULL; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(B,2); MatCheckPreallocated(B,2); if (!B->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (B->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (B->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); if (fill == PETSC_DEFAULT) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); MatCheckPreallocated(A,1); Asymbolic = A->ops->matmultsymbolic; Bsymbolic = B->ops->matmultsymbolic; if (Asymbolic == Bsymbolic) { if (!Bsymbolic) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"C=A*B not implemented for B of type %s",((PetscObject)B)->type_name); symbolic = Bsymbolic; } else { /* dispatch based on the type of A and B */ char symbolicname[256]; ierr = PetscStrcpy(symbolicname,"MatMatMultSymbolic_");CHKERRQ(ierr); ierr = PetscStrcat(symbolicname,((PetscObject)A)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(symbolicname,"_");CHKERRQ(ierr); ierr = PetscStrcat(symbolicname,((PetscObject)B)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(symbolicname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,symbolicname,(void (**)(void))&symbolic);CHKERRQ(ierr); if (!symbolic) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatMatMultSymbolic requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); } ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); ierr = (*symbolic)(A,B,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatMultNumeric" /*@ MatMatMultNumeric - Performs the numeric matrix-matrix product. Call this routine after first calling MatMatMultSymbolic(). Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix - B - the right matrix Output Parameters: . C - the product matrix, which was created by from MatMatMultSymbolic() or a call to MatMatMult(). Notes: C must have been created with MatMatMultSymbolic(). This routine is currently implemented for - pairs of AIJ matrices and classes which inherit from AIJ, C will be of type MATAIJ. - pairs of AIJ (A) and Dense (B) matrix, C will be of type Dense. - pairs of Dense (A) and AIJ (B) matrix, C will be of type Dense. Level: intermediate .seealso: MatMatMult(), MatMatMultSymbolic() @*/ PetscErrorCode MatMatMultNumeric(Mat A,Mat B,Mat C) { PetscErrorCode ierr; PetscFunctionBegin; ierr = MatMatMult(A,B,MAT_REUSE_MATRIX,0.0,&C);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatTransposeMult" /*@ MatMatTransposeMult - Performs Matrix-Matrix Multiplication C=A*B^T. Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix . B - the right matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known Output Parameters: . C - the product matrix Notes: C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy(). MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed. This routine is currently only implemented for pairs of SeqAIJ matrices. C will be of type MATSEQAIJ. Level: intermediate .seealso: MatMatTransposeMultSymbolic(), MatMatTransposeMultNumeric(), MatMatMult(), MatTransposeMatMult() MatPtAP() @*/ PetscErrorCode MatMatTransposeMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(B,2); MatCheckPreallocated(B,2); if (!B->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (B->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (B->cmap->N!=A->cmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, AN %D != BN %D",A->cmap->N,B->cmap->N); if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); MatCheckPreallocated(A,1); fA = A->ops->mattransposemult; if (!fA) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatMatTransposeMult not supported for A of type %s",((PetscObject)A)->type_name); fB = B->ops->mattransposemult; if (!fB) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatMatTransposeMult not supported for B of type %s",((PetscObject)B)->type_name); if (fB!=fA) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatMatTransposeMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); if (scall == MAT_INITIAL_MATRIX) { ierr = PetscLogEventBegin(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); ierr = (*A->ops->mattransposemultsymbolic)(A,B,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); } ierr = PetscLogEventBegin(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); ierr = (*A->ops->mattransposemultnumeric)(A,B,*C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTransposeMatMult" /*@ MatTransposeMatMult - Performs Matrix-Matrix Multiplication C=A^T*B. Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix . B - the right matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known Output Parameters: . C - the product matrix Notes: C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy(). MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed. This routine is currently implemented for pairs of AIJ matrices and pairs of SeqDense matrices and classes which inherit from SeqAIJ. C will be of same type as the input matrices. Level: intermediate .seealso: MatTransposeMatMultSymbolic(), MatTransposeMatMultNumeric(), MatMatMult(), MatMatTransposeMult(), MatPtAP() @*/ PetscErrorCode MatTransposeMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr; PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*transposematmult)(Mat,Mat,MatReuse,PetscReal,Mat*) = PETSC_NULL; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(B,2); MatCheckPreallocated(B,2); if (!B->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (B->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (B->rmap->N!=A->rmap->N) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->rmap->N); if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); MatCheckPreallocated(A,1); fA = A->ops->transposematmult; if (!fA) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatTransposeMatMult not supported for A of type %s",((PetscObject)A)->type_name); fB = B->ops->transposematmult; if (!fB) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatTransposeMatMult not supported for B of type %s",((PetscObject)B)->type_name); if (fB==fA) { transposematmult = fA; } if (!transposematmult) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatTransposeMatMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); ierr = PetscLogEventBegin(MAT_TransposeMatMult,A,B,0,0);CHKERRQ(ierr); ierr = (*transposematmult)(A,B,scall,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_TransposeMatMult,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMatMatMult" /*@ MatMatMatMult - Performs Matrix-Matrix-Matrix Multiplication D=A*B*C. Neighbor-wise Collective on Mat Input Parameters: + A - the left matrix . B - the middle matrix . C - the right matrix . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX - 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 if the result is a dense matrix this is irrelevent Output Parameters: . D - the product matrix Notes: Unless scall is MAT_REUSE_MATRIX D will be created. MAT_REUSE_MATRIX can only be used if the matrices A, B and C have the same nonzero pattern as in the previous call To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed. If you have many matrices with the same non-zero structure to multiply, you should either $ 1) use MAT_REUSE_MATRIX in all calls but the first or $ 2) call MatMatMatMultSymbolic() once and then MatMatMatMultNumeric() for each product needed Level: intermediate .seealso: MatMatMult, MatPtAP() @*/ PetscErrorCode MatMatMatMult(Mat A,Mat B,Mat C,MatReuse scall,PetscReal fill,Mat *D) { PetscErrorCode ierr; PetscErrorCode (*fA)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fB)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*fC)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); PetscErrorCode (*mult)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*)=PETSC_NULL; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_CLASSID,1); PetscValidType(A,1); MatCheckPreallocated(A,1); if (!A->assembled) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factortype) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(B,MAT_CLASSID,2); PetscValidType(B,2); MatCheckPreallocated(B,2); if (!B->assembled) SETERRQ(((PetscObject)B)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (B->factortype) SETERRQ(((PetscObject)B)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(C,MAT_CLASSID,3); PetscValidPointer(C,3); MatCheckPreallocated(C,3); if (!C->assembled) SETERRQ(((PetscObject)C)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (C->factortype) SETERRQ(((PetscObject)C)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (B->rmap->N!=A->cmap->N) SETERRQ2(((PetscObject)B)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); if (C->rmap->N!=B->cmap->N) SETERRQ2(((PetscObject)C)->comm,PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",C->rmap->N,B->cmap->N); if (scall == MAT_REUSE_MATRIX) { PetscValidPointer(*D,6); PetscValidHeaderSpecific(*D,MAT_CLASSID,6); ierr = PetscLogEventBegin(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); ierr = (*(*D)->ops->matmatmult)(A,B,C,scall,fill,D);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; if (fill < 1.0) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); fA = A->ops->matmatmult; fB = B->ops->matmatmult; fC = C->ops->matmatmult; if (fA == fB && fA == fC) { if (!fA) SETERRQ1(((PetscObject)A)->comm,PETSC_ERR_SUP,"MatMatMatMult not supported for A of type %s",((PetscObject)A)->type_name); mult = fA; } else { /* dispatch based on the type of A, B and C from their PetscObject's PetscFunctionLists. */ char multname[256]; ierr = PetscStrcpy(multname,"MatMatMatMult_");CHKERRQ(ierr); ierr = PetscStrcat(multname,((PetscObject)A)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); ierr = PetscStrcat(multname,((PetscObject)B)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); ierr = PetscStrcat(multname,((PetscObject)C)->type_name);CHKERRQ(ierr); ierr = PetscStrcat(multname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,multname,(void (**)(void))&mult);CHKERRQ(ierr); if (!mult) SETERRQ3(((PetscObject)A)->comm,PETSC_ERR_ARG_INCOMP,"MatMatMatMult requires A, %s, to be compatible with B, %s, C, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name,((PetscObject)C)->type_name); } ierr = PetscLogEventBegin(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); ierr = (*mult)(A,B,C,scall,fill,D);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRedundantMatrix" /*@C MatGetRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators. Collective on Mat Input Parameters: + mat - the matrix . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices) . subcomm - MPI communicator split from the communicator where mat resides in . mlocal_red - number of local rows of the redundant matrix - reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX Output Parameter: . matredundant - redundant matrix Notes: MAT_REUSE_MATRIX can only be used when the nonzero structure of the original matrix has not changed from that last call to MatGetRedundantMatrix(). This routine creates the duplicated matrices in subcommunicators; you should NOT create them before calling it. Only MPIAIJ matrix is supported. Level: advanced Concepts: subcommunicator Concepts: duplicate matrix .seealso: MatDestroy() @*/ PetscErrorCode MatGetRedundantMatrix(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,PetscInt mlocal_red,MatReuse reuse,Mat *matredundant) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (nsubcomm && reuse == MAT_REUSE_MATRIX) { PetscValidPointer(*matredundant,6); PetscValidHeaderSpecific(*matredundant,MAT_CLASSID,6); } if (!mat->ops->getredundantmatrix) SETERRQ1(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); MatCheckPreallocated(mat,1); ierr = PetscLogEventBegin(MAT_GetRedundantMatrix,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getredundantmatrix)(mat,nsubcomm,subcomm,mlocal_red,reuse,matredundant);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetRedundantMatrix,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetMultiProcBlock" /*@C MatGetMultiProcBlock - Create multiple [bjacobi] 'parallel submatrices' from a given 'mat' object. Each submatrix can span multiple procs. Collective on Mat Input Parameters: + mat - the matrix . subcomm - the subcommunicator obtained by com_split(comm) - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX Output Parameter: . subMat - 'parallel submatrices each spans a given subcomm Notes: The submatrix partition across processors is dicated by 'subComm' a communicator obtained by com_split(comm). The comm_split is not restriced to be grouped with consequitive original ranks. Due the comm_split() usage, the parallel layout of the submatrices map directly to the layout of the original matrix [wrt the local row,col partitioning]. So the original 'DiagonalMat' naturally maps into the 'DiagonalMat' of the subMat, hence it is used directly from the subMat. However the offDiagMat looses some columns - and this is reconstructed with MatSetValues() Level: advanced Concepts: subcommunicator Concepts: submatrices .seealso: MatGetSubMatrices() @*/ PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall,Mat *subMat) { PetscErrorCode ierr; PetscMPIInt commsize,subCommSize; PetscFunctionBegin; ierr = MPI_Comm_size(((PetscObject)mat)->comm,&commsize);CHKERRQ(ierr); ierr = MPI_Comm_size(subComm,&subCommSize);CHKERRQ(ierr); if (subCommSize > commsize) SETERRQ2(((PetscObject)mat)->comm,PETSC_ERR_ARG_OUTOFRANGE,"CommSize %D < SubCommZize %D",commsize,subCommSize); ierr = PetscLogEventBegin(MAT_GetMultiProcBlock,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->getmultiprocblock)(mat,subComm,scall,subMat);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_GetMultiProcBlock,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetLocalSubMatrix" /*@ MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering Not Collective Input Arguments: mat - matrix to extract local submatrix from isrow - local row indices for submatrix iscol - local column indices for submatrix Output Arguments: submat - the submatrix Level: intermediate Notes: The submat should be returned with MatRestoreLocalSubMatrix(). Depending on the format of mat, the returned submat may not implement MatMult(). Its communicator may be the same as mat, it may be PETSC_COMM_SELF, or some other subcomm of mat's. The submat always implements MatSetValuesLocal(). If isrow and iscol have the same block size, then MatSetValuesBlockedLocal() will also be implemented. .seealso: MatRestoreLocalSubMatrix(), MatCreateLocalRef() @*/ PetscErrorCode MatGetLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(isrow,IS_CLASSID,2); PetscValidHeaderSpecific(iscol,IS_CLASSID,3); PetscCheckSameComm(isrow,2,iscol,3); PetscValidPointer(submat,4); if (mat->ops->getlocalsubmatrix) { ierr = (*mat->ops->getlocalsubmatrix)(mat,isrow,iscol,submat);CHKERRQ(ierr); } else { ierr = MatCreateLocalRef(mat,isrow,iscol,submat);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestoreLocalSubMatrix" /*@ MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering Not Collective Input Arguments: mat - matrix to extract local submatrix from isrow - local row indices for submatrix iscol - local column indices for submatrix submat - the submatrix Level: intermediate .seealso: MatGetLocalSubMatrix() @*/ PetscErrorCode MatRestoreLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidHeaderSpecific(isrow,IS_CLASSID,2); PetscValidHeaderSpecific(iscol,IS_CLASSID,3); PetscCheckSameComm(isrow,2,iscol,3); PetscValidPointer(submat,4); if (*submat) { PetscValidHeaderSpecific(*submat,MAT_CLASSID,4); } if (mat->ops->restorelocalsubmatrix) { ierr = (*mat->ops->restorelocalsubmatrix)(mat,isrow,iscol,submat);CHKERRQ(ierr); } else { ierr = MatDestroy(submat);CHKERRQ(ierr); } *submat = PETSC_NULL; PetscFunctionReturn(0); } /* --------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatFindZeroDiagonals" /*@ MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no entry in the matrix Collective on Mat Input Parameter: . mat - the matrix Output Parameter: . is - if any rows have zero diagonals this contains the list of them Level: developer Concepts: matrix-vector product .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatFindZeroDiagonals(Mat mat,IS *is) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); PetscValidType(mat,1); if (!mat->assembled) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->findzerodiagonals) SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"This matrix type does not have a find zero diagonals defined"); ierr = (*mat->ops->findzerodiagonals)(mat,is);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatInvertBlockDiagonal" /*@C MatInvertBlockDiagonal - Inverts the block diagonal entries. Collective on Mat Input Parameters: . mat - the matrix Output Parameters: . values - the block inverses in column major order (FORTRAN-like) Note: This routine is not available from Fortran. Level: advanced @*/ PetscErrorCode MatInvertBlockDiagonal(Mat mat,const PetscScalar **values) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_CLASSID,1); if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->invertblockdiagonal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported"); ierr = (*mat->ops->invertblockdiagonal)(mat,values);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTransposeColoringDestroy" /*@C MatTransposeColoringDestroy - Destroys a coloring context for matrix product C=A*B^T that was created via MatTransposeColoringCreate(). Collective on MatTransposeColoring Input Parameter: . c - coloring context Level: intermediate .seealso: MatTransposeColoringCreate() @*/ PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c) { PetscErrorCode ierr; MatTransposeColoring matcolor=*c; PetscFunctionBegin; if (!matcolor) PetscFunctionReturn(0); if (--((PetscObject)matcolor)->refct > 0) {matcolor = 0; PetscFunctionReturn(0);} ierr = PetscFree(matcolor->ncolumns);CHKERRQ(ierr); ierr = PetscFree(matcolor->nrows);CHKERRQ(ierr); ierr = PetscFree(matcolor->colorforrow);CHKERRQ(ierr); ierr = PetscFree2(matcolor->rows,matcolor->columnsforspidx);CHKERRQ(ierr); ierr = PetscFree(matcolor->colorforcol);CHKERRQ(ierr); ierr = PetscFree(matcolor->columns);CHKERRQ(ierr); ierr = PetscHeaderDestroy(c);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTransColoringApplySpToDen" /*@C MatTransColoringApplySpToDen - Given a symbolic matrix product C=A*B^T for which a MatTransposeColoring context has been created, computes a dense B^T by Apply MatTransposeColoring to sparse B. Collective on MatTransposeColoring Input Parameters: + B - sparse matrix B . Btdense - symbolic dense matrix B^T - coloring - coloring context created with MatTransposeColoringCreate() Output Parameter: . Btdense - dense matrix B^T Options Database Keys: + -mat_transpose_coloring_view - Activates basic viewing or coloring . -mat_transpose_coloring_view_draw - Activates drawing of coloring - -mat_transpose_coloring_view_info - Activates viewing of coloring info Level: intermediate .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy() .keywords: coloring @*/ PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring,Mat B,Mat Btdense) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(B,MAT_CLASSID,1); PetscValidHeaderSpecific(Btdense,MAT_CLASSID,2); PetscValidHeaderSpecific(coloring,MAT_TRANSPOSECOLORING_CLASSID,3); if (!B->ops->transcoloringapplysptoden) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)B)->type_name); ierr = (B->ops->transcoloringapplysptoden)(coloring,B,Btdense);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTransColoringApplyDenToSp" /*@C MatTransColoringApplyDenToSp - Given a symbolic matrix product Csp=A*B^T for which a MatTransposeColoring context has been created and a dense matrix Cden=A*Btdense in which Btdens is obtained from MatTransColoringApplySpToDen(), recover sparse matrix Csp from Cden. Collective on MatTransposeColoring Input Parameters: + coloring - coloring context created with MatTransposeColoringCreate() - Cden - matrix product of a sparse matrix and a dense matrix Btdense Output Parameter: . Csp - sparse matrix Options Database Keys: + -mat_multtranspose_coloring_view - Activates basic viewing or coloring . -mat_multtranspose_coloring_view_draw - Activates drawing of coloring - -mat_multtranspose_coloring_view_info - Activates viewing of coloring info Level: intermediate .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy(), MatTransColoringApplySpToDen() .keywords: coloring @*/ PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring,Mat Cden,Mat Csp) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(matcoloring,MAT_TRANSPOSECOLORING_CLASSID,1); PetscValidHeaderSpecific(Cden,MAT_CLASSID,2); PetscValidHeaderSpecific(Csp,MAT_CLASSID,3); if (!Csp->ops->transcoloringapplydentosp) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)Csp)->type_name); ierr = (Csp->ops->transcoloringapplydentosp)(matcoloring,Cden,Csp);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTransposeColoringCreate" /*@C MatTransposeColoringCreate - Creates a matrix coloring context for matrix product C=A*B^T. Collective on Mat Input Parameters: + mat - the matrix product C - iscoloring - the coloring of the matrix; usually obtained with MatGetColoring() or DMCreateColoring() Output Parameter: . color - the new coloring context Level: intermediate .seealso: MatTransposeColoringDestroy(), MatTransposeColoringSetFromOptions(), MatTransColoringApplySpToDen(), MatTransColoringApplyDenToSp(), MatTransposeColoringView(), @*/ PetscErrorCode MatTransposeColoringCreate(Mat mat,ISColoring iscoloring,MatTransposeColoring *color) { MatTransposeColoring c; MPI_Comm comm; PetscErrorCode ierr; PetscFunctionBegin; ierr = PetscLogEventBegin(MAT_TransposeColoringCreate,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); ierr = PetscHeaderCreate(c,_p_MatTransposeColoring,int,MAT_TRANSPOSECOLORING_CLASSID,0,"MatTransposeColoring","Matrix product C=A*B^T via coloring","Mat",comm,MatTransposeColoringDestroy,0);CHKERRQ(ierr); c->ctype = iscoloring->ctype; if (mat->ops->transposecoloringcreate) { ierr = (*mat->ops->transposecoloringcreate)(mat,iscoloring,c);CHKERRQ(ierr); } else SETERRQ(((PetscObject)mat)->comm,PETSC_ERR_SUP,"Code not yet written for this matrix type"); *color = c; ierr = PetscLogEventEnd(MAT_TransposeColoringCreate,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); }