/* This is where the abstract matrix operations are defined */ #include "src/mat/matimpl.h" /*I "petscmat.h" I*/ #include "vecimpl.h" /* Logging support */ PetscCookie MAT_COOKIE = 0, MATSNESMFCTX_COOKIE = 0; PetscEvent MAT_Mult = 0, MAT_MultMatrixFree = 0, MAT_Mults = 0, MAT_MultConstrained = 0, MAT_MultAdd = 0, MAT_MultTranspose = 0; PetscEvent MAT_MultTransposeConstrained = 0, MAT_MultTransposeAdd = 0, MAT_Solve = 0, MAT_Solves = 0, MAT_SolveAdd = 0, MAT_SolveTranspose = 0; PetscEvent MAT_SolveTransposeAdd = 0, MAT_Relax = 0, MAT_ForwardSolve = 0, MAT_BackwardSolve = 0, MAT_LUFactor = 0, MAT_LUFactorSymbolic = 0; PetscEvent MAT_LUFactorNumeric = 0, MAT_CholeskyFactor = 0, MAT_CholeskyFactorSymbolic = 0, MAT_CholeskyFactorNumeric = 0, MAT_ILUFactor = 0; PetscEvent MAT_ILUFactorSymbolic = 0, MAT_ICCFactorSymbolic = 0, MAT_Copy = 0, MAT_Convert = 0, MAT_Scale = 0, MAT_AssemblyBegin = 0; PetscEvent MAT_AssemblyEnd = 0, MAT_SetValues = 0, MAT_GetValues = 0, MAT_GetRow = 0, MAT_GetSubMatrices = 0, MAT_GetColoring = 0, MAT_GetOrdering = 0; PetscEvent MAT_IncreaseOverlap = 0, MAT_Partitioning = 0, MAT_ZeroEntries = 0, MAT_Load = 0, MAT_View = 0, MAT_AXPY = 0, MAT_FDColoringCreate = 0; PetscEvent MAT_FDColoringApply = 0,MAT_Transpose = 0,MAT_FDColoringFunction = 0; PetscEvent MAT_MatMult = 0, MAT_MatMultSymbolic = 0, MAT_MatMultNumeric = 0; PetscEvent MAT_PtAP = 0, MAT_PtAPSymbolic = 0, MAT_PtAPNumeric = 0; PetscEvent MAT_MatMultTranspose = 0, MAT_MatMultTransposeSymbolic = 0, MAT_MatMultTransposeNumeric = 0; /* nasty global values for MatSetValue() */ PetscInt MatSetValue_Row = 0, MatSetValue_Column = 0; PetscScalar MatSetValue_Value = 0.0; #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 obtained 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getrow) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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__ "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_COOKIE,1); PetscValidIntPointer(ncols,3); if (!mat->assembled) SETERRQ(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__ "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_ASCII_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_info - Prints info on matrix at conclusion of MatEndAssembly() . -mat_view_info_detailed - Prints more detailed info . -mat_view - Prints matrix in ASCII format . -mat_view_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 users manual) . -viewer_socket_machine . -viewer_socket_port . -mat_view_binary - save matrix to file in binary format - -viewer_binary_filename Level: beginner 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; PetscTruth iascii; char *cstr; PetscViewerFormat format; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (!viewer) viewer = PETSC_VIEWER_STDOUT_(mat->comm); PetscValidHeaderSpecific(viewer,PETSC_VIEWER_COOKIE,2); PetscCheckSameComm(mat,1,viewer,2); if (!mat->assembled) SETERRQ(PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix"); ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); if (iascii) { ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { if (mat->prefix) { ierr = PetscViewerASCIIPrintf(viewer,"Matrix Object:(%s)\n",mat->prefix);CHKERRQ(ierr); } else { ierr = PetscViewerASCIIPrintf(viewer,"Matrix Object:\n");CHKERRQ(ierr); } ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); ierr = MatGetType(mat,&cstr);CHKERRQ(ierr); ierr = MatGetSize(mat,&rows,&cols);CHKERRQ(ierr); ierr = PetscViewerASCIIPrintf(viewer,"type=%s, rows=%D, cols=%D\n",cstr,rows,cols);CHKERRQ(ierr); if (mat->ops->getinfo) { MatInfo info; ierr = MatGetInfo(mat,MAT_GLOBAL_SUM,&info);CHKERRQ(ierr); ierr = PetscViewerASCIIPrintf(viewer,"total: nonzeros=%D, allocated nonzeros=%D\n", (PetscInt)info.nz_used,(PetscInt)info.nz_allocated);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_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); } } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatScaleSystem" /*@C MatScaleSystem - Scale a vector solution and right hand side to match the scaling of a scaled matrix. Collective on Mat Input Parameter: + mat - the matrix . x - solution vector (or PETSC_NULL) - b - right hand side vector (or PETSC_NULL) Notes: For AIJ, BAIJ, and BDiag matrix formats, the matrices are not internally scaled, so this does nothing. For MPIROWBS it permutes and diagonally scales. The KSP methods automatically call this routine when required (via PCPreSolve()) so it is rarely used directly. Level: Developer Concepts: matrices^scaling .seealso: MatUseScaledForm(), MatUnScaleSystem() @*/ PetscErrorCode MatScaleSystem(Mat mat,Vec x,Vec b) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (x) {PetscValidHeaderSpecific(x,VEC_COOKIE,2);PetscCheckSameComm(mat,1,x,2);} if (b) {PetscValidHeaderSpecific(b,VEC_COOKIE,3);PetscCheckSameComm(mat,1,b,3);} if (mat->ops->scalesystem) { ierr = (*mat->ops->scalesystem)(mat,x,b);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatUnScaleSystem" /*@C MatUnScaleSystem - Unscales a vector solution and right hand side to match the original scaling of a scaled matrix. Collective on Mat Input Parameter: + mat - the matrix . x - solution vector (or PETSC_NULL) - b - right hand side vector (or PETSC_NULL) Notes: For AIJ, BAIJ, and BDiag matrix formats, the matrices are not internally scaled, so this does nothing. For MPIROWBS it permutes and diagonally scales. The KSP methods automatically call this routine when required (via PCPreSolve()) so it is rarely used directly. Level: Developer .seealso: MatUseScaledForm(), MatScaleSystem() @*/ PetscErrorCode MatUnScaleSystem(Mat mat,Vec x,Vec b) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (x) {PetscValidHeaderSpecific(x,VEC_COOKIE,2);PetscCheckSameComm(mat,1,x,2);} if (b) {PetscValidHeaderSpecific(b,VEC_COOKIE,3);PetscCheckSameComm(mat,1,b,3);} if (mat->ops->unscalesystem) { ierr = (*mat->ops->unscalesystem)(mat,x,b);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatUseScaledForm" /*@C MatUseScaledForm - For matrix storage formats that scale the matrix (for example MPIRowBS matrices are diagonally scaled on assembly) indicates matrix operations (MatMult() etc) are applied using the scaled matrix. Collective on Mat Input Parameter: + mat - the matrix - scaled - PETSC_TRUE for applying the scaled, PETSC_FALSE for applying the original matrix Notes: For scaled matrix formats, applying the original, unscaled matrix will be slightly more expensive Level: Developer .seealso: MatScaleSystem(), MatUnScaleSystem() @*/ PetscErrorCode MatUseScaledForm(Mat mat,PetscTruth scaled) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (mat->ops->usescaledform) { ierr = (*mat->ops->usescaledform)(mat,scaled);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDestroy" /*@C MatDestroy - Frees space taken by a matrix. Collective on Mat Input Parameter: . A - the matrix Level: beginner @*/ PetscErrorCode MatDestroy(Mat A) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidType(A,1); MatPreallocated(A); if (--A->refct > 0) PetscFunctionReturn(0); /* if memory was published with AMS then destroy it */ ierr = PetscObjectDepublish(A);CHKERRQ(ierr); if (A->mapping) { ierr = ISLocalToGlobalMappingDestroy(A->mapping);CHKERRQ(ierr); } if (A->bmapping) { ierr = ISLocalToGlobalMappingDestroy(A->bmapping);CHKERRQ(ierr); } if (A->rmap) { ierr = PetscMapDestroy(A->rmap);CHKERRQ(ierr); } if (A->cmap) { ierr = PetscMapDestroy(A->cmap);CHKERRQ(ierr); } ierr = (*A->ops->destroy)(A);CHKERRQ(ierr); PetscLogObjectDestroy(A); PetscHeaderDestroy(A); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatValid" /*@ MatValid - Checks whether a matrix object is valid. Collective on Mat Input Parameter: . m - the matrix to check Output Parameter: flg - flag indicating matrix status, either PETSC_TRUE if matrix is valid, or PETSC_FALSE otherwise. Level: developer Concepts: matrices^validity @*/ PetscErrorCode MatValid(Mat m,PetscTruth *flg) { PetscFunctionBegin; PetscValidIntPointer(flg,1); if (!m) *flg = PETSC_FALSE; else if (m->cookie != MAT_COOKIE) *flg = PETSC_FALSE; else *flg = PETSC_TRUE; 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: By default the values, v, are row-oriented and unsorted. 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; PetscFunctionBegin; if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_BOPT_g) else if (mat->insertmode != addv) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); } if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #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->setvalues) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesStencil" /*@C MatSetValuesStencil - Inserts or adds a block of values into a matrix. Using structured grid indexing Not Collective Input Parameters: + mat - the matrix . v - a logically two-dimensional array of values . 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 - 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 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 DAVecGetArray() routine In order to use this routine you must either obtain the matrix with DAGetMatrix() 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 DACreateXXX() or MatSetStencil(). For example, if you create a DA 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 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) 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(), DAGetMatrix(), DAVecGetArray(), MatStencil @*/ PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; PetscInt j,i,jdxm[128],jdxn[256],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_COOKIE,1); PetscValidType(mat,1); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (m > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 128 rows at a time; trying to set %D",m); if (n > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 256 columns at a time; trying to set %D",n); 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); 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 . v - a logically two-dimensional array of values . 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 - 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 DAVecGetArray() routine In order to use this routine you must either obtain the matrix with DAGetMatrix() 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 DACreateXXX() or MatSetStencil(). For example, if you create a DA 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(), DAGetMatrix(), DAVecGetArray(), MatStencil @*/ PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) { PetscErrorCode ierr; PetscInt j,i,jdxm[128],jdxn[256],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_COOKIE,1); PetscValidType(mat,1); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (m > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 128 rows at a time; trying to set %D",m); if (n > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 256 columns at a time; trying to set %D",n); for (i=0; istencil.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 = (PetscTruth)(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: By default the values, v, are row-oriented and unsorted. 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. Restrictions: MatSetValuesBlocked() is currently supported only for the BAIJ and SBAIJ formats Level: intermediate Concepts: matrices^putting entries in blocked .seealso: 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; if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_BOPT_g) else if (mat->insertmode != addv) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); } if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #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) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 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. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(idxm,3); PetscValidIntPointer(idxn,5); PetscValidScalarPointer(v,6); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->getvalues) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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__ "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 - mapping - mapping created with ISLocalToGlobalMappingCreate() or ISLocalToGlobalMappingCreateIS() 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 mapping) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(x,MAT_COOKIE,1); PetscValidType(x,1); MatPreallocated(x); PetscValidHeaderSpecific(mapping,IS_LTOGM_COOKIE,2); if (x->mapping) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Mapping already set for matrix"); } if (x->ops->setlocaltoglobalmapping) { ierr = (*x->ops->setlocaltoglobalmapping)(x,mapping);CHKERRQ(ierr); } else { x->mapping = mapping; ierr = PetscObjectReference((PetscObject)mapping);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 - mapping - mapping created with ISLocalToGlobalMappingCreate() or ISLocalToGlobalMappingCreateIS() 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 mapping) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(x,MAT_COOKIE,1); PetscValidType(x,1); MatPreallocated(x); PetscValidHeaderSpecific(mapping,IS_LTOGM_COOKIE,2); if (x->bmapping) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Mapping already set for matrix"); } x->bmapping = mapping; ierr = PetscObjectReference((PetscObject)mapping);CHKERRQ(ierr); 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: Before calling MatSetValuesLocal(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMapping(). 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; PetscInt irowm[2048],icolm[2048]; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(irow,3); PetscValidIntPointer(icol,5); PetscValidScalarPointer(y,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_BOPT_g) else if (mat->insertmode != addv) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); } if (!mat->ops->setvalueslocal && (nrow > 2048 || ncol > 2048)) { SETERRQ2(PETSC_ERR_SUP,"Number column/row indices must be <= 2048: are %D %D",nrow,ncol); } if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #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 = ISLocalToGlobalMappingApply(mat->mapping,nrow,irow,irowm);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApply(mat->mapping,ncol,icol,icolm);CHKERRQ(ierr); ierr = (*mat->ops->setvalues)(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); } else { ierr = (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 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: Before calling MatSetValuesBlockedLocal(), the user must first set the local-to-global mapping by calling MatSetLocalToGlobalMappingBlock(), where the mapping MUST be set for matrix blocks, not for matrix elements. 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: 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; PetscInt irowm[2048],icolm[2048]; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(irow,3); PetscValidIntPointer(icol,5); PetscValidScalarPointer(y,6); if (mat->insertmode == NOT_SET_VALUES) { mat->insertmode = addv; } #if defined(PETSC_USE_BOPT_g) else if (mat->insertmode != addv) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); } if (!mat->bmapping) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Local to global never set with MatSetLocalToGlobalMappingBlock()"); } if (nrow > 2048 || ncol > 2048) { SETERRQ2(PETSC_ERR_SUP,"Number column/row indices must be <= 2048: are %D %D",nrow,ncol); } if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); #endif if (mat->assembled) { mat->was_assembled = PETSC_TRUE; mat->assembled = PETSC_FALSE; } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApply(mat->bmapping,nrow,irow,irowm);CHKERRQ(ierr); ierr = ISLocalToGlobalMappingApply(mat->bmapping,ncol,icol,icolm);CHKERRQ(ierr); ierr = (*mat->ops->setvaluesblocked)(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } /* --------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatMult" /*@ MatMult - Computes the matrix-vector product, y = Ax. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); #ifndef PETSC_HAVE_CONSTRAINTS if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); if (mat->m != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->m,y->n); #endif if (mat->nullsp) { ierr = MatNullSpaceRemove(mat->nullsp,x,&x);CHKERRQ(ierr); } 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); if (mat->nullsp) { ierr = MatNullSpaceRemove(mat->nullsp,y,PETSC_NULL);CHKERRQ(ierr); } ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultTranspose" /*@ MatMultTranspose - Computes matrix transpose times a vector. 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). Level: beginner Concepts: matrix vector product^transpose .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd() @*/ PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscTruth flg1, flg2; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); #ifndef PETSC_HAVE_CONSTRAINTS if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->N,y->N); #endif if (!mat->ops->multtranspose) SETERRQ(PETSC_ERR_SUP, "Operation not supported"); ierr = PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); if (!mat->ops->multtranspose) SETERRQ(PETSC_ERR_SUP,"This matrix type does not have a multiply tranpose defined"); ierr = PetscTypeCompare((PetscObject)mat,MATSEQSBAIJ,&flg1); ierr = PetscTypeCompare((PetscObject)mat,MATMPISBAIJ,&flg2); if (flg1 || flg2) { /* mat is in sbaij format */ ierr = (*mat->ops->mult)(mat,x,y);CHKERRQ(ierr); } else { ierr = (*mat->ops->multtranspose)(mat,x,y);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultAdd" /*@ MatMultAdd - Computes v3 = v2 + A * v1. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(v1,VEC_COOKIE,2); PetscValidHeaderSpecific(v2,VEC_COOKIE,3); PetscValidHeaderSpecific(v3,VEC_COOKIE,4); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->N != v1->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->N,v1->N); if (mat->M != v2->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->M,v2->N); if (mat->M != v3->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->M,v3->N); if (mat->m != v3->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: local dim %D %D",mat->m,v3->n); if (mat->m != v2->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: local dim %D %D",mat->m,v2->n); if (v1 == v3) SETERRQ(PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 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 = PetscObjectIncreaseState((PetscObject)v3);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultTransposeAdd" /*@ MatMultTransposeAdd - Computes v3 = v2 + A' * v1. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(v1,VEC_COOKIE,2); PetscValidHeaderSpecific(v2,VEC_COOKIE,3); PetscValidHeaderSpecific(v3,VEC_COOKIE,4); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->multtransposeadd) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (v1 == v3) SETERRQ(PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); if (mat->M != v1->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->M,v1->N); if (mat->N != v2->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->N,v2->N); if (mat->N != v3->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->N,v3->N); 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 = PetscObjectIncreaseState((PetscObject)v3);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatMultConstrained" /*@ MatMultConstrained - The inner multiplication routine for a constrained matrix P^T A P. 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(), MatMultTrans(), MatMultAdd(), MatMultTransAdd() @*/ PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); if (mat->m != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->m,y->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 = PetscObjectIncreaseState((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. 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(), MatMultTrans(), MatMultAdd(), MatMultTransAdd() @*/ PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->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 = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); 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 STDOUT when using the runtime options $ -log_info -mat_view_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 @*/ PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(info,3); if (!mat->ops->getinfo) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getinfo)(mat,flag,info);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatILUDTFactor" /*@C MatILUDTFactor - Performs a drop tolerance ILU factorization. Collective on Mat Input Parameters: + mat - the matrix . info - information about the factorization to be done . row - row permutation - col - column permutation Output Parameters: . fact - the factored matrix Level: developer 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 is currently only supported for the SeqAIJ matrix format using code from Yousef Saad's SPARSEKIT2 package (translated to C with f2c) and/or Matlab. SPARSEKIT2 is copyrighted by Yousef Saad with the GNU copyright and thus can be distributed with PETSc. Concepts: matrices^ILUDT factorization .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo @*/ PetscErrorCode MatILUDTFactor(Mat mat,MatFactorInfo *info,IS row,IS col,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(info,2); if (row) PetscValidHeaderSpecific(row,IS_COOKIE,3); if (col) PetscValidHeaderSpecific(col,IS_COOKIE,4); PetscValidPointer(fact,5); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->iludtfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = (*mat->ops->iludtfactor)(mat,info,row,col,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatLUFactor" /*@ 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 -log_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 @*/ PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); PetscValidPointer(info,4); PetscValidType(mat,1); MatPreallocated(mat); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->lufactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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 = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatILUFactor" /*@ 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 @*/ PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); PetscValidPointer(info,4); PetscValidType(mat,1); MatPreallocated(mat); if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"matrix must be square"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->ilufactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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 = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatLUFactorSymbolic" /*@ MatLUFactorSymbolic - Performs symbolic LU factorization of matrix. Call this routine before calling MatLUFactorNumeric(). Collective on Mat Input Parameters: + 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 -log_info to determine an optimal value to use Output Parameter: . 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^LU symbolic factorization .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo @*/ PetscErrorCode MatLUFactorSymbolic(Mat mat,IS row,IS col,MatFactorInfo *info,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); PetscValidPointer(info,4); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(fact,5); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->lufactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic LU",mat->type_name); ierr = PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = (*mat->ops->lufactorsymbolic)(mat,row,col,info,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatLUFactorNumeric" /*@ MatLUFactorNumeric - Performs numeric LU factorization of a matrix. Call this routine after first calling MatLUFactorSymbolic(). Collective on Mat Input Parameters: + mat - the matrix - fact - the matrix generated for the factor, from MatLUFactorSymbolic() 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() @*/ PetscErrorCode MatLUFactorNumeric(Mat mat,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(fact,2); PetscValidHeaderSpecific(*fact,MAT_COOKIE,2); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->M != (*fact)->M || mat->N != (*fact)->N) { SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat mat,Mat *fact: global dimensions are different %D should = %D %D should = %D", mat->M,(*fact)->M,mat->N,(*fact)->N); } if (!(*fact)->ops->lufactornumeric) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = PetscLogEventBegin(MAT_LUFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); ierr = (*(*fact)->ops->lufactornumeric)(mat,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_LUFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); ierr = MatView_Private(*fact);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactor" /*@ 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() @*/ PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(perm,IS_COOKIE,2); PetscValidPointer(info,3); if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"Matrix must be square"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->choleskyfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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 = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactorSymbolic" /*@ MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization of a symmetric matrix. Collective on Mat Input Parameters: + 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 -log_info to determine an optimal value to use Output Parameter: . fact - the factored matrix 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() @*/ PetscErrorCode MatCholeskyFactorSymbolic(Mat mat,IS perm,MatFactorInfo *info,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (perm) PetscValidHeaderSpecific(perm,IS_COOKIE,2); PetscValidPointer(info,3); PetscValidPointer(fact,4); if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"Matrix must be square"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->choleskyfactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = (*mat->ops->choleskyfactorsymbolic)(mat,perm,info,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCholeskyFactorNumeric" /*@ MatCholeskyFactorNumeric - Performs numeric Cholesky factorization of a symmetric matrix. Call this routine after first calling MatCholeskyFactorSymbolic(). Collective on Mat Input Parameter: . mat - the initial matrix Output Parameter: . fact - the factored matrix 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() @*/ PetscErrorCode MatCholeskyFactorNumeric(Mat mat,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(fact,2); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!(*fact)->ops->choleskyfactornumeric) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (mat->M != (*fact)->M || mat->N != (*fact)->N) { SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat mat,Mat *fact: global dim %D should = %D %D should = %D", mat->M,(*fact)->M,mat->N,(*fact)->N); } ierr = PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); ierr = (*(*fact)->ops->choleskyfactornumeric)(mat,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatSolve" /*@ MatSolve - Solves A x = b, given a factored matrix. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); if (!mat->M && !mat->N) PetscFunctionReturn(0); if (!mat->ops->solve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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 = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatForwardSolve" /* @ MatForwardSolve - Solves L x = b, given a factored matrix, A = LU. 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). 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->ops->forwardsolve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 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 = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatBackwardSolve" /* @ MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU. 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). 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->ops->backwardsolve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 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 = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveAdd" /*@ MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(y,VEC_COOKIE,2); PetscValidHeaderSpecific(b,VEC_COOKIE,3); PetscValidHeaderSpecific(x,VEC_COOKIE,4); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,y,2); PetscCheckSameComm(mat,1,x,3); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); if (x->n != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->n,y->n); 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(&one,y,x);CHKERRQ(ierr); } else { ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); PetscLogObjectParent(mat,tmp); ierr = VecCopy(x,tmp);CHKERRQ(ierr); ierr = MatSolve(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(&one,tmp,x);CHKERRQ(ierr); ierr = VecDestroy(tmp);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveTranspose" /*@ MatSolveTranspose - Solves A' x = b, given a factored matrix. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,3); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,3); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->ops->solvetranspose) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s",mat->type_name); if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); if (mat->N != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->N,b->N); 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 = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSolveTransposeAdd" /*@ MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a factored matrix. 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(y,VEC_COOKIE,2); PetscValidHeaderSpecific(b,VEC_COOKIE,3); PetscValidHeaderSpecific(x,VEC_COOKIE,4); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,y,3); PetscCheckSameComm(mat,1,x,4); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); if (mat->N != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->N,b->N); if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->N,y->N); if (x->n != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->n,y->n); 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(&one,y,x);CHKERRQ(ierr); } else { ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); PetscLogObjectParent(mat,tmp); ierr = VecCopy(x,tmp);CHKERRQ(ierr); ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); ierr = VecAXPY(&one,tmp,x);CHKERRQ(ierr); ierr = VecDestroy(tmp);CHKERRQ(ierr); } } ierr = PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatRelax" /*@ MatRelax - Computes relaxation (SOR, Gauss-Seidel) sweeps. 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) 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 seperate independent smoothings on each processor. Application programmers will not generally use MatRelax() directly, but instead will employ the KSP/PC interface. 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(). See also, MatPBRelax(). This routine will automatically call the point block version if the point version is not available. Level: developer Concepts: matrices^relaxation Concepts: matrices^SOR Concepts: matrices^Gauss-Seidel @*/ PetscErrorCode MatRelax(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,8); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,8); if (!mat->ops->relax && !mat->ops->pbrelax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); ierr = PetscLogEventBegin(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); if (mat->ops->relax) { ierr =(*mat->ops->relax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); } else { ierr =(*mat->ops->pbrelax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPBRelax" /*@ MatPBRelax - Computes relaxation (SOR, Gauss-Seidel) sweeps. Collective on Mat and Vec See MatRelax() for usage For multi-component PDEs where the Jacobian is stored in a point block format (with the PETSc BAIJ matrix formats) the relaxation is done one point block at a time. That is, the small (for example, 4 by 4) blocks along the diagonal are solved simultaneously (that is a 4 by 4 linear solve is done) to update all the values at a point. Level: developer @*/ PetscErrorCode MatPBRelax(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(b,VEC_COOKIE,2); PetscValidHeaderSpecific(x,VEC_COOKIE,8); PetscCheckSameComm(mat,1,b,2); PetscCheckSameComm(mat,1,x,8); if (!mat->ops->pbrelax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); ierr = PetscLogEventBegin(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); ierr =(*mat->ops->pbrelax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((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,rend,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(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (A->M != B->M || A->N != B->N) SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim (%D,%D) (%D,%D)",A->M,B->M, A->N,B->N); 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); } if (A->mapping) { if (B->mapping) {ierr = ISLocalToGlobalMappingDestroy(B->mapping);CHKERRQ(ierr);B->mapping = 0;} ierr = MatSetLocalToGlobalMapping(B,A->mapping);CHKERRQ(ierr); } if (A->bmapping) { if (B->bmapping) {ierr = ISLocalToGlobalMappingDestroy(B->bmapping);CHKERRQ(ierr);B->bmapping = 0;} ierr = MatSetLocalToGlobalMappingBlock(B,A->mapping);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Copy,A,B,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)B);CHKERRQ(ierr); PetscFunctionReturn(0); } #include "petscsys.h" PetscTruth MatConvertRegisterAllCalled = PETSC_FALSE; PetscFList MatConvertList = 0; #undef __FUNCT__ #define __FUNCT__ "MatConvertRegister" /*@C MatConvertRegister - Allows one to register a routine that converts a sparse matrix from one format to another. Not Collective Input Parameters: + type - the type of matrix (defined in include/petscmat.h), for example, MATSEQAIJ. - Converter - the function that reads the matrix from the binary file. Level: developer .seealso: MatConvertRegisterAll(), MatConvert() @*/ PetscErrorCode MatConvertRegister(const char sname[],const char path[],const char name[],PetscErrorCode (*function)(Mat,MatType,Mat*)) { PetscErrorCode ierr; char fullname[PETSC_MAX_PATH_LEN]; PetscFunctionBegin; ierr = PetscFListConcat(path,name,fullname);CHKERRQ(ierr); ierr = PetscFListAdd(&MatConvertList,sname,fullname,(void (*)(void))function);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. 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. Level: intermediate Concepts: matrices^converting between storage formats .seealso: MatCopy(), MatDuplicate() @*/ PetscErrorCode MatConvert(Mat mat,const MatType newtype,Mat *M) { PetscErrorCode ierr; PetscTruth sametype,issame,flg; char convname[256],mtype[256]; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(M,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); ierr = PetscOptionsGetString(PETSC_NULL,"-matconvert_type",mtype,256,&flg);CHKERRQ(ierr); if (flg) { newtype = mtype; } ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = PetscTypeCompare((PetscObject)mat,newtype,&sametype);CHKERRQ(ierr); ierr = PetscStrcmp(newtype,"same",&issame);CHKERRQ(ierr); if ((sametype || issame) && mat->ops->duplicate) { ierr = (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);CHKERRQ(ierr); } else { PetscErrorCode (*conv)(Mat,const MatType,Mat*)=PETSC_NULL; /* 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. */ ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); ierr = PetscStrcat(convname,mat->type_name);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); ierr = PetscStrcat(convname,newtype);CHKERRQ(ierr); ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); if (!conv) { Mat B; ierr = MatCreate(mat->comm,0,0,0,0,&B);CHKERRQ(ierr); ierr = MatSetType(B,newtype);CHKERRQ(ierr); ierr = PetscObjectQueryFunction((PetscObject)B,convname,(void (**)(void))&conv);CHKERRQ(ierr); ierr = MatDestroy(B);CHKERRQ(ierr); if (!conv) { if (!MatConvertRegisterAllCalled) { ierr = MatConvertRegisterAll(PETSC_NULL);CHKERRQ(ierr); } ierr = PetscFListFind(mat->comm,MatConvertList,newtype,(void(**)(void))&conv);CHKERRQ(ierr); if (!conv) { if (mat->ops->convert) { conv = mat->ops->convert; } else { conv = MatConvert_Basic; } } } } ierr = (*conv)(mat,newtype,M);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatDuplicate" /*@C 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 nonzero values as well or not Output Parameter: . M - pointer to place new matrix Level: intermediate Concepts: matrices^duplicating .seealso: MatCopy(), MatConvert() @*/ PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(M,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); *M = 0; ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); if (!mat->ops->duplicate) { SETERRQ(PETSC_ERR_SUP,"Not written for this matrix type"); } ierr = (*mat->ops->duplicate)(mat,op,M);CHKERRQ(ierr); if (mat->mapping) { ierr = MatSetLocalToGlobalMapping(*M,mat->mapping);CHKERRQ(ierr); } if (mat->bmapping) { ierr = MatSetLocalToGlobalMappingBlock(*M,mat->mapping);CHKERRQ(ierr); } ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetDiagonal" /*@ MatGetDiagonal - Gets the diagonal of a matrix. 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 Notes: For the SeqAIJ matrix format, this routine may also be called on a LU factored matrix; in that case it routines the reciprocal of the diagonal entries in U. It returns the entries permuted by the row and column permutation used during the symbolic factorization. Level: intermediate Concepts: matrices^accessing diagonals .seealso: MatGetRow(), MatGetSubmatrices(), MatGetSubmatrix(), MatGetRowMax() @*/ PetscErrorCode MatGetDiagonal(Mat mat,Vec v) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(v,VEC_COOKIE,2); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getdiagonal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getdiagonal)(mat,v);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetRowMax" /*@ MatGetRowMax - Gets the maximum value (in absolute value) of each row of the matrix Collective on Mat and Vec Input Parameters: . mat - the matrix Output Parameter: . v - the vector for storing the maximums Level: intermediate Concepts: matrices^getting row maximums .seealso: MatGetDiagonal(), MatGetSubmatrices(), MatGetSubmatrix() @*/ PetscErrorCode MatGetRowMax(Mat mat,Vec v) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(v,VEC_COOKIE,2); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->getrowmax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getrowmax)(mat,v);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatTranspose" /*@C MatTranspose - Computes an in-place or out-of-place transpose of a matrix. Collective on Mat Input Parameter: . mat - the matrix to transpose Output Parameters: . B - the transpose (or pass in PETSC_NULL for an in-place transpose) Level: intermediate Concepts: matrices^transposing .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose() @*/ PetscErrorCode MatTranspose(Mat mat,Mat *B) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->transpose) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = PetscLogEventBegin(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->transpose)(mat,B);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); if (B) {ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr);} PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsTranspose" /*@C 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,PetscTruth *flg) { PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscTruth*),(*g)(Mat,Mat,PetscReal,PetscTruth*); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidHeaderSpecific(B,MAT_COOKIE,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); if (f && g) { if (f==g) { ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); } else { SETERRQ(PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test"); } } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPermute" /*@C 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 needs the entire column permutation, that is this is the same size as the total number of columns in the matrix Output Parameters: . B - the permuted matrix Level: advanced Concepts: matrices^permuting .seealso: MatGetOrdering() @*/ PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(row,IS_COOKIE,2); PetscValidHeaderSpecific(col,IS_COOKIE,3); PetscValidPointer(B,4); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->permute) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->permute)(mat,row,col,B);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPermuteSparsify" /*@C MatPermuteSparsify - Creates a new matrix with rows and columns permuted from the original and sparsified to the prescribed tolerance. Collective on Mat Input Parameters: + A - The matrix to permute . band - The half-bandwidth of the sparsified matrix, or PETSC_DECIDE . frac - The half-bandwidth as a fraction of the total size, or 0.0 . tol - The drop tolerance . rowp - The row permutation - colp - The column permutation Output Parameter: . B - The permuted, sparsified matrix Level: advanced Note: The default behavior (band = PETSC_DECIDE and frac = 0.0) is to restrict the half-bandwidth of the resulting matrix to 5% of the total matrix size. .keywords: matrix, permute, sparsify .seealso: MatGetOrdering(), MatPermute() @*/ PetscErrorCode MatPermuteSparsify(Mat A, PetscInt band, PetscReal frac, PetscReal tol, IS rowp, IS colp, Mat *B) { IS irowp, icolp; PetscInt *rows, *cols; PetscInt M, N, locRowStart, locRowEnd; PetscInt nz, newNz; const PetscInt *cwork; PetscInt *cnew; const PetscScalar *vwork; PetscScalar *vnew; PetscInt bw, issize; PetscInt row, locRow, newRow, col, newCol; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A, MAT_COOKIE,1); PetscValidHeaderSpecific(rowp, IS_COOKIE,5); PetscValidHeaderSpecific(colp, IS_COOKIE,6); PetscValidPointer(B,7); if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); if (!A->ops->permutesparsify) { ierr = MatGetSize(A, &M, &N);CHKERRQ(ierr); ierr = MatGetOwnershipRange(A, &locRowStart, &locRowEnd);CHKERRQ(ierr); ierr = ISGetSize(rowp, &issize);CHKERRQ(ierr); if (issize != M) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %D for row permutation, should be %D", issize, M); ierr = ISGetSize(colp, &issize);CHKERRQ(ierr); if (issize != N) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %D for column permutation, should be %D", issize, N); ierr = ISInvertPermutation(rowp, 0, &irowp);CHKERRQ(ierr); ierr = ISGetIndices(irowp, &rows);CHKERRQ(ierr); ierr = ISInvertPermutation(colp, 0, &icolp);CHKERRQ(ierr); ierr = ISGetIndices(icolp, &cols);CHKERRQ(ierr); ierr = PetscMalloc(N * sizeof(PetscInt), &cnew);CHKERRQ(ierr); ierr = PetscMalloc(N * sizeof(PetscScalar), &vnew);CHKERRQ(ierr); /* Setup bandwidth to include */ if (band == PETSC_DECIDE) { if (frac <= 0.0) bw = (PetscInt) (M * 0.05); else bw = (PetscInt) (M * frac); } else { if (band <= 0) SETERRQ(PETSC_ERR_ARG_WRONG, "Bandwidth must be a positive integer"); bw = band; } /* Put values into new matrix */ ierr = MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, B);CHKERRQ(ierr); for(row = locRowStart, locRow = 0; row < locRowEnd; row++, locRow++) { ierr = MatGetRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); newRow = rows[locRow]+locRowStart; for(col = 0, newNz = 0; col < nz; col++) { newCol = cols[cwork[col]]; if ((newCol >= newRow - bw) && (newCol < newRow + bw) && (PetscAbsScalar(vwork[col]) >= tol)) { cnew[newNz] = newCol; vnew[newNz] = vwork[col]; newNz++; } } ierr = MatSetValues(*B, 1, &newRow, newNz, cnew, vnew, INSERT_VALUES);CHKERRQ(ierr); ierr = MatRestoreRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); } ierr = PetscFree(cnew);CHKERRQ(ierr); ierr = PetscFree(vnew);CHKERRQ(ierr); ierr = MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); ierr = MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); ierr = ISRestoreIndices(irowp, &rows);CHKERRQ(ierr); ierr = ISRestoreIndices(icolp, &cols);CHKERRQ(ierr); ierr = ISDestroy(irowp);CHKERRQ(ierr); ierr = ISDestroy(icolp);CHKERRQ(ierr); } else { ierr = (*A->ops->permutesparsify)(A, band, frac, tol, rowp, colp, B);CHKERRQ(ierr); } ierr = PetscObjectIncreaseState((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,PetscTruth *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidHeaderSpecific(B,MAT_COOKIE,2); PetscValidType(A,1); MatPreallocated(A); PetscValidType(B,2); MatPreallocated(B); PetscValidIntPointer(flg,3); PetscCheckSameComm(A,1,B,2); if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!B->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->M != B->M || A->N != B->N) SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D %D %D",A->M,B->M,A->N,B->N); if (!A->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",A->type_name); if (!B->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",B->type_name); if (A->ops->equal != B->ops->equal) SETERRQ2(PETSC_ERR_ARG_INCOMP,"A is type: %s\nB is type: %s",A->type_name,B->type_name); 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) 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (!mat->ops->diagonalscale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (l) {PetscValidHeaderSpecific(l,VEC_COOKIE,2);PetscCheckSameComm(mat,1,l,2);} if (r) {PetscValidHeaderSpecific(r,VEC_COOKIE,3);PetscCheckSameComm(mat,1,r,3);} if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 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 = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatScale" /*@ MatScale - Scales all elements of a matrix by a given number. 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(const PetscScalar *a,Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidScalarPointer(a,1); PetscValidHeaderSpecific(mat,MAT_COOKIE,2); PetscValidType(mat,2); MatPreallocated(mat); if (!mat->ops->scale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = (*mat->ops->scale)(a,mat);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidScalarPointer(nrm,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->norm) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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. Level: beginner Concepts: matrices^assembling .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled() @*/ PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (mat->factor) SETERRQ(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__ "MatAssembed" /*@ MatAssembled - Indicates if a matrix has been assembled and is ready for use; for example, in matrix-vector product. Collective on Mat 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,PetscTruth *assembled) { PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(assembled,2); *assembled = mat->assembled; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatView_Private" /* Processes command line options to determine if/how a matrix is to be viewed. Called by MatAssemblyEnd() and MatLoad(). */ PetscErrorCode MatView_Private(Mat mat) { PetscErrorCode ierr; PetscTruth flg; static PetscTruth incall = PETSC_FALSE; PetscFunctionBegin; if (incall) PetscFunctionReturn(0); incall = PETSC_TRUE; ierr = PetscOptionsBegin(mat->comm,mat->prefix,"Matrix Options","Mat");CHKERRQ(ierr); ierr = PetscOptionsName("-mat_view_info","Information on matrix size","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr); ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsName("-mat_view_info_detailed","Nonzeros in the matrix","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO_DETAIL);CHKERRQ(ierr); ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsName("-mat_view","Print matrix to stdout","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsName("-mat_view_matlab","Print matrix to stdout in a format Matlab can read","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_MATLAB);CHKERRQ(ierr); ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsName("-mat_view_socket","Send matrix to socket (can be read from matlab)","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = MatView(mat,PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerFlush(PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsName("-mat_view_binary","Save matrix to file in binary format","MatView",&flg);CHKERRQ(ierr); if (flg) { ierr = MatView(mat,PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerFlush(PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); } ierr = PetscOptionsEnd();CHKERRQ(ierr); /* cannot have inside PetscOptionsBegin() because uses PetscOptionsBegin() */ ierr = PetscOptionsHasName(mat->prefix,"-mat_view_draw",&flg);CHKERRQ(ierr); if (flg) { ierr = PetscOptionsHasName(mat->prefix,"-mat_view_contour",&flg);CHKERRQ(ierr); if (flg) { PetscViewerPushFormat(PETSC_VIEWER_DRAW_(mat->comm),PETSC_VIEWER_DRAW_CONTOUR);CHKERRQ(ierr); } ierr = MatView(mat,PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); ierr = PetscViewerFlush(PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); if (flg) { PetscViewerPopFormat(PETSC_VIEWER_DRAW_(mat->comm));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_info - Prints info on matrix at conclusion of MatEndAssembly() . -mat_view_info_detailed - Prints more detailed info . -mat_view - Prints matrix in ASCII format . -mat_view_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 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. Level: beginner .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), MatView(), MatAssembled(), PetscViewerSocketOpen() @*/ PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type) { PetscErrorCode ierr; static PetscInt inassm = 0; PetscTruth flg; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); 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 = PetscObjectIncreaseState((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 (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) { ierr = MatView_Private(mat);CHKERRQ(ierr); ierr = PetscOptionsHasName(mat->prefix,"-mat_is_symmetric",&flg);CHKERRQ(ierr); if (flg) { PetscReal tol = 0.0; ierr = PetscOptionsGetReal(mat->prefix,"-mat_is_symmetric",&tol,PETSC_NULL);CHKERRQ(ierr); ierr = MatIsSymmetric(mat,tol,&flg);CHKERRQ(ierr); if (flg) { ierr = PetscPrintf(mat->comm,"Matrix is symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); } else { ierr = PetscPrintf(mat->comm,"Matrix is not symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); } } } inassm--; ierr = PetscOptionsHasName(mat->prefix,"-help",&flg);CHKERRQ(ierr); if (flg) { ierr = MatPrintHelp(mat);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatCompress" /*@ MatCompress - Tries to store the matrix in as little space as possible. May fail if memory is already fully used, since it tries to allocate new space. Collective on Mat Input Parameters: . mat - the matrix Level: advanced @*/ PetscErrorCode MatCompress(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (mat->ops->compress) {ierr = (*mat->ops->compress)(mat);CHKERRQ(ierr);} 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, nonsorted input. Collective on Mat Input Parameters: + mat - the matrix - option - the option, one of those listed below (and possibly others), e.g., MAT_ROWS_SORTED, MAT_NEW_NONZERO_LOCATION_ERR Options Describing Matrix Structure: + MAT_SYMMETRIC - symmetric in terms of both structure and value . MAT_HERMITIAN - transpose is the complex conjugation . MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure . MAT_NOT_SYMMETRIC - not symmetric in value . MAT_NOT_HERMITIAN - transpose is not the complex conjugation . MAT_NOT_STRUCTURALLY_SYMMETRIC - not 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. - MAT_NOT_SYMMETRY_ETERNAL - if MatAssemblyBegin/End() is called then the flags you set will be dropped (in case potentially the symmetry etc was lost). Options For Use with MatSetValues(): Insert a logically dense subblock, which can be + MAT_ROW_ORIENTED - row-oriented (default) . MAT_COLUMN_ORIENTED - column-oriented . MAT_ROWS_SORTED - sorted by row . MAT_ROWS_UNSORTED - not sorted by row (default) . MAT_COLUMNS_SORTED - sorted by column - MAT_COLUMNS_UNSORTED - not sorted by column (default) Not 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_NO_NEW_NONZERO_LOCATIONS - additional insertions will not be allowed if they generate a new nonzero . MAT_YES_NEW_NONZERO_LOCATIONS - additional insertions will be allowed . MAT_NO_NEW_DIAGONALS - additional insertions will not be allowed if they generate a nonzero in a new diagonal (for block diagonal format only) . MAT_YES_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 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_NO_NEW_NONZERO_LOCATIONS 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_NO_NEW_NONZERO_LOCATIONS flag should be used with MAT_USE_HASH_TABLE flag. This option is currently supported by MATMPIBAIJ format only. MAT_KEEP_ZEROED_ROWS indicates when MatZeroRows() is called the zeroed entries are kept in the nonzero structure MAT_IGNORE_ZERO_ENTRIES - for AIJ 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_DO_NOT_USE_INODES - indicates not using inode version of the code - works with AIJ and ROWBS matrix types Level: intermediate Concepts: matrices^setting options @*/ PetscErrorCode MatSetOption(Mat mat,MatOption op) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); switch (op) { case MAT_SYMMETRIC: mat->symmetric = PETSC_TRUE; mat->structurally_symmetric = PETSC_TRUE; mat->symmetric_set = PETSC_TRUE; mat->structurally_symmetric_set = PETSC_TRUE; break; case MAT_HERMITIAN: mat->hermitian = PETSC_TRUE; mat->structurally_symmetric = PETSC_TRUE; mat->hermitian_set = PETSC_TRUE; mat->structurally_symmetric_set = PETSC_TRUE; break; case MAT_STRUCTURALLY_SYMMETRIC: mat->structurally_symmetric = PETSC_TRUE; mat->structurally_symmetric_set = PETSC_TRUE; break; case MAT_NOT_SYMMETRIC: mat->symmetric = PETSC_FALSE; mat->symmetric_set = PETSC_TRUE; break; case MAT_NOT_HERMITIAN: mat->hermitian = PETSC_FALSE; mat->hermitian_set = PETSC_TRUE; break; case MAT_NOT_STRUCTURALLY_SYMMETRIC: mat->structurally_symmetric = PETSC_FALSE; mat->structurally_symmetric_set = PETSC_TRUE; break; case MAT_SYMMETRY_ETERNAL: mat->symmetric_eternal = PETSC_TRUE; break; case MAT_NOT_SYMMETRY_ETERNAL: mat->symmetric_eternal = PETSC_FALSE; break; default: break; } if (mat->ops->setoption) { ierr = (*mat->ops->setoption)(mat,op);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. Collective on Mat Input Parameters: . mat - the matrix Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroRows() @*/ PetscErrorCode MatZeroEntries(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (!mat->ops->zeroentries) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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 = PetscObjectIncreaseState((PetscObject)mat);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 . is - index set of rows to remove - diag - pointer to value put in all diagonals of eliminated rows. Note that diag is not a pointer to an array, but merely a pointer to a single value. 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_ZEROED_ROWS) 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 a null pointer (PETSC_NULL in C or PETSC_NULL_SCALAR in Fortran) 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 should list the rows that IT wants zeroed Level: intermediate Concepts: matrices^zeroing rows .seealso: MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() @*/ PetscErrorCode MatZeroRows(Mat mat,IS is,const PetscScalar *diag) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(is,IS_COOKIE,2); if (diag) PetscValidScalarPointer(diag,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->zerorows) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->zerorows)(mat,is,diag);CHKERRQ(ierr); ierr = MatView_Private(mat);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);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 . is - index set of rows to remove - diag - pointer to value put in all diagonals of eliminated rows. Note that diag is not a pointer to an array, but merely a pointer to a single value. 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_ZEROED_ROWS) 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 a null pointer (PETSC_NULL in C or PETSC_NULL_SCALAR in Fortran) as the final argument). Level: intermediate Concepts: matrices^zeroing .seealso: MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping @*/ PetscErrorCode MatZeroRowsLocal(Mat mat,IS is,const PetscScalar *diag) { PetscErrorCode ierr; IS newis; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(is,IS_COOKIE,2); if (diag) PetscValidScalarPointer(diag,3); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (mat->ops->zerorowslocal) { ierr = (*mat->ops->zerorowslocal)(mat,is,diag);CHKERRQ(ierr); } else { if (!mat->mapping) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); ierr = ISLocalToGlobalMappingApplyIS(mat->mapping,is,&newis);CHKERRQ(ierr); ierr = (*mat->ops->zerorows)(mat,newis,diag);CHKERRQ(ierr); ierr = ISDestroy(newis);CHKERRQ(ierr); } ierr = PetscObjectIncreaseState((PetscObject)mat);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_COOKIE,1); if (m) *m = mat->M; if (n) *n = mat->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_COOKIE,1); if (m) PetscValidIntPointer(m,2); if (n) PetscValidIntPointer(n,3); if (m) *m = mat->m; if (n) *n = mat->n; 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. Level: beginner Concepts: matrices^row ownership @*/ PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt* n) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (m) PetscValidIntPointer(m,2); if (n) PetscValidIntPointer(n,3); ierr = PetscMapGetLocalRange(mat->rmap,m,n);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatILUFactorSymbolic" /*@ 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 @*/ PetscErrorCode MatILUFactorSymbolic(Mat mat,IS row,IS col,MatFactorInfo *info,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(row,IS_COOKIE,2); PetscValidHeaderSpecific(col,IS_COOKIE,3); PetscValidPointer(info,4); PetscValidPointer(fact,5); if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels); if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); if (!mat->ops->ilufactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ILU",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); ierr = PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); ierr = (*mat->ops->ilufactorsymbolic)(mat,row,col,info,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatICCFactorSymbolic" /*@ 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: Currently only no-fill factorization is supported. 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 incomplete Cholesky factorization Concepts: matrices^factorization Concepts: Cholsky^symbolic factorization .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo @*/ PetscErrorCode MatICCFactorSymbolic(Mat mat,IS perm,MatFactorInfo *info,Mat *fact) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(perm,IS_COOKIE,2); PetscValidPointer(info,3); PetscValidPointer(fact,4); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels); if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); if (!mat->ops->iccfactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ICC",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); ierr = PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); ierr = (*mat->ops->iccfactorsymbolic)(mat,perm,info,fact);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetArray" /*@C MatGetArray - Returns a pointer to the element values in the matrix. The result of this routine is dependent on the underlying matrix data structure, and may not even work for certain matrix types. You MUST call MatRestoreArray() when you no longer need to access the array. Not Collective Input Parameter: . mat - the matrix Output Parameter: . v - the location of the values Fortran Note: This routine is used differently from Fortran, e.g., .vb Mat mat PetscScalar mat_array(1) PetscOffset i_mat PetscErrorCode ierr call MatGetArray(mat,mat_array,i_mat,ierr) C Access first local entry in matrix; note that array is C treated as one dimensional value = mat_array(i_mat + 1) [... other code ...] call MatRestoreArray(mat,mat_array,i_mat,ierr) .ve See the Fortran chapter of the users manual and petsc/src/mat/examples/tests for details. Level: advanced Concepts: matrices^access array .seealso: MatRestoreArray(), MatGetArrayF90() @*/ PetscErrorCode MatGetArray(Mat mat,PetscScalar *v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(v,2); if (!mat->ops->getarray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getarray)(mat,v);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestoreArray" /*@C MatRestoreArray - Restores the matrix after MatGetArray() has been called. Not Collective Input Parameter: + mat - the matrix - v - the location of the values Fortran Note: This routine is used differently from Fortran, e.g., .vb Mat mat PetscScalar mat_array(1) PetscOffset i_mat PetscErrorCode ierr call MatGetArray(mat,mat_array,i_mat,ierr) C Access first local entry in matrix; note that array is C treated as one dimensional value = mat_array(i_mat + 1) [... other code ...] call MatRestoreArray(mat,mat_array,i_mat,ierr) .ve See the Fortran chapter of the users manual and petsc/src/mat/examples/tests for details Level: advanced .seealso: MatGetArray(), MatRestoreArrayF90() @*/ PetscErrorCode MatRestoreArray(Mat mat,PetscScalar *v[]) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidPointer(v,2); #if defined(PETSC_USE_BOPT_g) CHKMEMQ; #endif if (!mat->ops->restorearray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->restorearray)(mat,v);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);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 - 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. 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. 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() @*/ PetscErrorCode MatGetSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) { PetscErrorCode ierr; PetscInt i; PetscTruth eq; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (n) { PetscValidPointer(irow,3); PetscValidHeaderSpecific(*irow,IS_COOKIE,3); PetscValidPointer(icol,4); PetscValidHeaderSpecific(*icol,IS_COOKIE,4); } PetscValidPointer(submat,6); if (n && scall == MAT_REUSE_MATRIX) { PetscValidPointer(*submat,6); PetscValidHeaderSpecific(**submat,MAT_COOKIE,6); } if (!mat->ops->getsubmatrices) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 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);CHKERRQ(ierr); } else if (mat->hermitian) { ierr = MatSetOption((*submat)[i],MAT_HERMITIAN);CHKERRQ(ierr); } else if (mat->structurally_symmetric) { ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC);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 .seealso: MatGetSubMatrices() @*/ PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[]) { PetscErrorCode ierr; PetscInt i; PetscFunctionBegin; if (n < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n); PetscValidPointer(mat,2); for (i=0; iassembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!ov) PetscFunctionReturn(0); if (!mat->ops->increaseoverlap) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",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__ "MatPrintHelp" /*@ MatPrintHelp - Prints all the options for the matrix. Collective on Mat Input Parameter: . mat - the matrix Options Database Keys: + -help - Prints matrix options - -h - Prints matrix options Level: developer .seealso: MatCreate(), MatCreateXXX() @*/ PetscErrorCode MatPrintHelp(Mat mat) { static PetscTruth called = PETSC_FALSE; PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (!called) { if (mat->ops->printhelp) { ierr = (*mat->ops->printhelp)(mat);CHKERRQ(ierr); } called = PETSC_TRUE; } 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 diagonal formats are MATSEQBDIAG, MATMPIBDIAG. Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ Level: intermediate Concepts: matrices^block size .seealso: MatCreateSeqBAIJ(), MatCreateMPIBAIJ(), MatCreateSeqBDiag(), MatCreateMPIBDiag() @*/ PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(bs,2); if (!mat->ops->getblocksize) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getblocksize)(mat,bs);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 Output Parameters: + n - number of rows in the (possibly compressed) matrix . ia - the row pointers . ja - the column indices - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned Level: developer .seealso: MatGetColumnIJ(), MatRestoreRowIJ() @*/ PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(n,4); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); if (!mat->ops->getrowij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->getrowij)(mat,shift,symmetric,n,ia,ja,done);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 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,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(n,4); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); if (!mat->ops->getcolumnij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->getcolumnij)(mat,shift,symmetric,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 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,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); if (!mat->ops->restorerowij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->restorerowij)(mat,shift,symmetric,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 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,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (ia) PetscValidIntPointer(ia,5); if (ja) PetscValidIntPointer(ja,6); PetscValidIntPointer(done,7); if (!mat->ops->restorecolumnij) *done = PETSC_FALSE; else { *done = PETSC_TRUE; ierr = (*mat->ops->restorecolumnij)(mat,shift,symmetric,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 . n - number of colors - 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 n,PetscInt ncolors,ISColoringValue colorarray[],ISColoring *iscoloring) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidIntPointer(colorarray,4); PetscValidPointer(iscoloring,5); if (!mat->ops->coloringpatch){ ierr = ISColoringCreate(mat->comm,n,colorarray,iscoloring);CHKERRQ(ierr); } else { ierr = (*mat->ops->coloringpatch)(mat,n,ncolors,colorarray,iscoloring);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetUnfactored" /*@ MatSetUnfactored - Resets a factored matrix to be treated as unfactored. 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); PCILUSeUseInPlace(pc); .ve or by using the options -pc_type ilu -pc_ilu_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_ilu_in_place). See the discussion of these preconditioners in 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: PCILUSetUseInPlace(), PCLUSetUseInPlace() Concepts: matrices^unfactored @*/ PetscErrorCode MatSetUnfactored(Mat mat) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); mat->factor = 0; if (!mat->ops->setunfactored) PetscFunctionReturn(0); ierr = (*mat->ops->setunfactored)(mat);CHKERRQ(ierr); PetscFunctionReturn(0); } /*MC MatGetArrayF90 - Accesses a matrix array from Fortran90. Synopsis: MatGetArrayF90(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 MatGetArrayF90(x,xx_v,ierr) a = xx_v(3) call MatRestoreArrayF90(x,xx_v,ierr) .ve Notes: Not yet supported for all F90 compilers Level: advanced .seealso: MatRestoreArrayF90(), MatGetArray(), MatRestoreArray() Concepts: matrices^accessing array M*/ /*MC MatRestoreArrayF90 - Restores a matrix array that has been accessed with MatGetArrayF90(). Synopsis: MatRestoreArrayF90(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 MatGetArrayF90(x,xx_v,ierr) a = xx_v(3) call MatRestoreArrayF90(x,xx_v,ierr) .ve Notes: Not yet supported for all F90 compilers Level: advanced .seealso: MatGetArrayF90(), MatGetArray(), MatRestoreArray() 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 - rows this processor should obtain . iscol - columns for all processors you wish to keep . csize - number of columns "local" to this processor (does nothing for sequential matrices). This should match the result from VecGetLocalSize(x,...) if you plan to use the matrix in a A*x; alternatively, you can use PETSC_DECIDE - 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 iscol argument MUST be the same on each processor. You might be able to create the iscol argument with ISAllGather(). 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 cll of MAT_REUSE_MATRIX will reuse the matrix generated the first time. Concepts: matrices^submatrices .seealso: MatGetSubMatrices(), ISAllGather() @*/ PetscErrorCode MatGetSubMatrix(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse cll,Mat *newmat) { PetscErrorCode ierr; PetscMPIInt size; Mat *local; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidHeaderSpecific(isrow,IS_COOKIE,2); PetscValidHeaderSpecific(iscol,IS_COOKIE,3); PetscValidPointer(newmat,6); if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat,MAT_COOKIE,6); PetscValidType(mat,1); MatPreallocated(mat); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); /* if original matrix is on just one processor then use submatrix generated */ if (!mat->ops->getsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) { ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&newmat);CHKERRQ(ierr); PetscFunctionReturn(0); } else if (!mat->ops->getsubmatrix && size == 1) { ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); *newmat = *local; ierr = PetscFree(local);CHKERRQ(ierr); PetscFunctionReturn(0); } if (!mat->ops->getsubmatrix) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getsubmatrix)(mat,isrow,iscol,csize,cll,newmat);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)*newmat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetPetscMaps" /*@C MatGetPetscMaps - Returns the maps associated with the matrix. Not Collective Input Parameter: . mat - the matrix Output Parameters: + rmap - the row (right) map - cmap - the column (left) map Level: developer Concepts: maps^getting from matrix @*/ PetscErrorCode MatGetPetscMaps(Mat mat,PetscMap *rmap,PetscMap *cmap) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); ierr = (*mat->ops->getmaps)(mat,rmap,cmap);CHKERRQ(ierr); PetscFunctionReturn(0); } /* Version that works for all PETSc matrices */ #undef __FUNCT__ #define __FUNCT__ "MatGetPetscMaps_Petsc" PetscErrorCode MatGetPetscMaps_Petsc(Mat mat,PetscMap *rmap,PetscMap *cmap) { PetscFunctionBegin; if (rmap) *rmap = mat->rmap; if (cmap) *cmap = mat->cmap; 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 VecSetValuesBlocked() while the stash is used for values set with VecSetValues() Run with the option -log_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 @*/ PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); 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 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; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); PetscValidHeaderSpecific(w,VEC_COOKIE,4); PetscValidType(A,1); MatPreallocated(A); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); if (N > M) { ierr = MatMultTransposeAdd(A,x,y,w);CHKERRQ(ierr); } else { ierr = MatMultAdd(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 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; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); PetscValidType(A,1); MatPreallocated(A); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); if (N > M) { ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); } else { ierr = MatMult(A,x,y);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatRestrict" /*@ MatRestrict - y = A*x or A'*x 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; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidHeaderSpecific(x,VEC_COOKIE,2); PetscValidHeaderSpecific(y,VEC_COOKIE,3); PetscValidType(A,1); MatPreallocated(A); ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); if (N > M) { ierr = MatMult(A,x,y);CHKERRQ(ierr); } else { ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); } PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatNullSpaceAttach" /*@C MatNullSpaceAttach - attaches a null space to a matrix. This null space will be removed from the resulting vector whenever MatMult() is called Collective on Mat Input Parameters: + mat - the matrix - nullsp - the null space object Level: developer Notes: Overwrites any previous null space that may have been attached Concepts: null space^attaching to matrix .seealso: MatCreate(), MatNullSpaceCreate() @*/ PetscErrorCode MatNullSpaceAttach(Mat mat,MatNullSpace nullsp) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_COOKIE,2); if (mat->nullsp) { ierr = MatNullSpaceDestroy(mat->nullsp);CHKERRQ(ierr); } mat->nullsp = nullsp; ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatICCFactor" /*@ 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() @*/ PetscErrorCode MatICCFactor(Mat mat,IS row,MatFactorInfo* info) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); PetscValidPointer(info,3); if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"matrix must be square"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (!mat->ops->iccfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->iccfactor)(mat,row,info);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetValuesAdic" /*@ MatSetValuesAdic - Sets values computed with ADIC automatic differentiation into a matrix. Not Collective Input Parameters: + mat - the matrix - v - the values compute with ADIC 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(), MatSetValuesAdifor() @*/ PetscErrorCode MatSetValuesAdic(Mat mat,void *v) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); PetscValidPointer(mat,2); if (!mat->assembled) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (!mat->ops->setvaluesadic) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->setvaluesadic)(mat,v);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); ierr = MatView_Private(mat);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatSetColoring" /*@ MatSetColoring - Sets a coloring used by calls to MatSetValuesAdic() Not Collective Input Parameters: + mat - the matrix - coloring - the coloring Level: developer .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), MatSetValues(), MatSetValuesAdic() @*/ PetscErrorCode MatSetColoring(Mat mat,ISColoring coloring) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(mat,MAT_COOKIE,1); PetscValidType(mat,1); PetscValidPointer(coloring,2); if (!mat->assembled) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); } if (!mat->ops->setcoloring) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->setcoloring)(mat,coloring);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_COOKIE,1); PetscValidType(mat,1); PetscValidPointer(v,3); if (!mat->assembled) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); } ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); if (!mat->ops->setvaluesadifor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->setvaluesadifor)(mat,nl,v);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); PetscFunctionReturn(0); } EXTERN PetscErrorCode MatMPIAIJDiagonalScaleLocal(Mat,Vec); EXTERN PetscErrorCode MatMPIBAIJDiagonalScaleLocal(Mat,Vec); #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_COOKIE,1); PetscValidHeaderSpecific(diag,VEC_COOKIE,2); PetscValidType(mat,1); if (!mat->assembled) { SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); } ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = MPI_Comm_size(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_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions"); } } else { PetscErrorCode (*f)(Mat,Vec); ierr = PetscObjectQueryFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",(void (**)(void))&f);CHKERRQ(ierr); if (f) { ierr = (*f)(mat,diag);CHKERRQ(ierr); } else { SETERRQ(PETSC_ERR_SUP,"Only supported for MPIAIJ and MPIBAIJ parallel matrices"); } } ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); ierr = PetscObjectIncreaseState((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_COOKIE,1); PetscValidType(mat,1); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled"); if (!mat->ops->getinertia) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); ierr = (*mat->ops->getinertia)(mat,nneg,nzero,npos);CHKERRQ(ierr); PetscFunctionReturn(0); } /* ----------------------------------------------------------------*/ #undef __FUNCT__ #define __FUNCT__ "MatSolves" /*@ MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); if (!mat->M && !mat->N) PetscFunctionReturn(0); if (!mat->ops->solves) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 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" /*@C 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 Level: intermediate Concepts: matrix^symmetry .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown() @*/ PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscTruth *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidPointer(flg,2); if (!A->symmetric_set) { if (!A->ops->issymmetric) { MatType mattype; ierr = MatGetType(A,&mattype);CHKERRQ(ierr); SETERRQ1(PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); } ierr = (*A->ops->issymmetric)(A,tol,&A->symmetric);CHKERRQ(ierr); A->symmetric_set = PETSC_TRUE; if (A->symmetric) { A->structurally_symmetric_set = PETSC_TRUE; A->structurally_symmetric = PETSC_TRUE; } } *flg = A->symmetric; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatIsSymmetricKnown" /*@C MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric. Collective on Mat 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,PetscTruth *set,PetscTruth *flg) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,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" /*@C MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian. Collective on Mat 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,PetscTruth *set,PetscTruth *flg) { PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,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" /*@C 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,PetscTruth *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidPointer(flg,2); if (!A->structurally_symmetric_set) { if (!A->ops->isstructurallysymmetric) SETERRQ(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__ "MatIsHermitian" /*@C MatIsHermitian - Test whether a matrix is Hermitian, i.e. it is the complex conjugate of its transpose. Collective on Mat Input Parameter: . A - the matrix to test Output Parameters: . flg - the result Level: intermediate Concepts: matrix^symmetry .seealso: MatTranspose(), MatIsTranspose(), MatIsSymmetric(), MatIsStructurallySymmetric(), MatSetOption() @*/ PetscErrorCode MatIsHermitian(Mat A,PetscTruth *flg) { PetscErrorCode ierr; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidPointer(flg,2); if (!A->hermitian_set) { if (!A->ops->ishermitian) SETERRQ(PETSC_ERR_SUP,"Matrix does not support checking for being Hermitian"); ierr = (*A->ops->ishermitian)(A,&A->hermitian);CHKERRQ(ierr); A->hermitian_set = PETSC_TRUE; if (A->hermitian) { A->structurally_symmetric_set = PETSC_TRUE; A->structurally_symmetric = PETSC_TRUE; } } *flg = A->hermitian; PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatStashGetInfo" extern PetscErrorCode MatStashGetInfo_Private(MatStash*,PetscInt*,PetscInt*); /*@ MatStashGetInfo - Gets how many values are currently in the vector 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 *brealloc) { PetscErrorCode ierr; PetscFunctionBegin; ierr = MatStashGetInfo_Private(&mat->stash,nstash,reallocs);CHKERRQ(ierr); ierr = MatStashGetInfo_Private(&mat->bstash,nstash,reallocs);CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatGetVecs" /*@ 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_COOKIE,1); PetscValidType(mat,1); MatPreallocated(mat); if (mat->ops->getvecs) { ierr = (*mat->ops->getvecs)(mat,right,left);CHKERRQ(ierr); } else { PetscMPIInt size; ierr = MPI_Comm_size(mat->comm, &size);CHKERRQ(ierr); if (right) { ierr = VecCreate(mat->comm,right);CHKERRQ(ierr); ierr = VecSetSizes(*right,mat->n,PETSC_DETERMINE);CHKERRQ(ierr); if (size > 1) {ierr = VecSetType(*right,VECMPI);CHKERRQ(ierr);} else {ierr = VecSetType(*right,VECSEQ);CHKERRQ(ierr);} } if (left) { ierr = VecCreate(mat->comm,left);CHKERRQ(ierr); ierr = VecSetSizes(*left,mat->m,PETSC_DETERMINE);CHKERRQ(ierr); if (size > 1) {ierr = VecSetType(*left,VECMPI);CHKERRQ(ierr);} else {ierr = VecSetType(*left,VECSEQ);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 @*/ PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info) { PetscErrorCode ierr; PetscFunctionBegin; ierr = PetscMemzero(info,sizeof(MatFactorInfo));CHKERRQ(ierr); PetscFunctionReturn(0); } #undef __FUNCT__ #define __FUNCT__ "MatPtAP" /*@C MatPtAP - Creates the matrix projection C = P^T * A * P 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 SeqAIJ matrices and classes which inherit from SeqAIJ. C will be of type MATSEQAIJ. Level: intermediate .seealso: MatPtAPSymbolic(),MatPtAPNumeric(),MatMatMult() @*/ PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C) { PetscErrorCode ierr, (*fA)(Mat,Mat,MatReuse,PetscReal,Mat *), (*fP)(Mat,Mat,MatReuse,PetscReal,Mat *); Mat Ptmp; PetscTruth flg; PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidType(A,1); MatPreallocated(A); if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(P,MAT_COOKIE,2); PetscValidType(P,2); MatPreallocated(P); if (!P->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (P->M!=A->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->M,A->N); if (fill <=0.0) SETERRQ1(PETSC_ERR_ARG_SIZ,"fill=%g must be > 0.0",fill); /* This is a crappy hack */ ierr = PetscTypeCompare((PetscObject)P,MATSEQMAIJ,&flg);CHKERRQ(ierr); if (flg) { ierr = MatConvert(P,MATSEQAIJ,&Ptmp);CHKERRQ(ierr); P = Ptmp; } /* For now, we do not dispatch based on the type of A and P */ /* When implementations like _SeqAIJ_MAIJ exist, attack the multiple dispatch problem. */ fA = A->ops->ptap; if (!fA) SETERRQ1(PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",A->type_name); fP = P->ops->ptap; if (!fP) SETERRQ1(PETSC_ERR_SUP,"MatPtAP not supported for P of type %s",P->type_name); if (fP!=fA) SETERRQ2(PETSC_ERR_ARG_INCOMP,"MatPtAP requires A, %s, to be compatible with P, %s",A->type_name,P->type_name); ierr = PetscLogEventBegin(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); ierr = (*fA)(A,P,scall,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); if (flg) { ierr = MatDestroy(P);CHKERRQ(ierr); } PetscFunctionReturn(0); } /*@C MatPtAPNumeric - Computes the matrix projection C = P^T * A * P 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() @*/ #undef __FUNCT__ #define __FUNCT__ "MatPtAPNumeric" PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C) { PetscErrorCode ierr,(*fA)(Mat,Mat,Mat), (*fP)(Mat,Mat,Mat); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidType(A,1); MatPreallocated(A); if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(P,MAT_COOKIE,2); PetscValidType(P,2); MatPreallocated(P); if (!P->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(C,MAT_COOKIE,3); PetscValidType(C,3); MatPreallocated(C); if (!C->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (C->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); if (P->N!=C->M) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->N,C->M); if (P->M!=A->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->M,A->N); if (A->M!=A->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->M,A->N); if (P->N!=C->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->N,C->N); /* For now, we do not dispatch based on the type of A and P */ /* When implementations like _SeqAIJ_MAIJ exist, attack the multiple dispatch problem. */ fA = A->ops->ptapnumeric; if (!fA) SETERRQ1(PETSC_ERR_SUP,"MatPtAPNumeric not supported for A of type %s",A->type_name); fP = P->ops->ptapnumeric; if (!fP) SETERRQ1(PETSC_ERR_SUP,"MatPtAPNumeric not supported for P of type %s",P->type_name); if (fP!=fA) SETERRQ2(PETSC_ERR_ARG_INCOMP,"MatPtAPNumeric requires A, %s, to be compatible with P, %s",A->type_name,P->type_name); ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); ierr = (*fA)(A,P,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); } /*@C MatPtAPSymbolic - Creates the (i,j) structure of the matrix projection C = P^T * A * P 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() @*/ #undef __FUNCT__ #define __FUNCT__ "MatPtAPSymbolic" PetscErrorCode MatPtAPSymbolic(Mat A,Mat P,PetscReal fill,Mat *C) { PetscErrorCode ierr, (*fA)(Mat,Mat,PetscReal,Mat*), (*fP)(Mat,Mat,PetscReal,Mat*); PetscFunctionBegin; PetscValidHeaderSpecific(A,MAT_COOKIE,1); PetscValidType(A,1); MatPreallocated(A); if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidHeaderSpecific(P,MAT_COOKIE,2); PetscValidType(P,2); MatPreallocated(P); if (!P->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); if (P->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); PetscValidPointer(C,3); if (P->M!=A->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->M,A->N); if (A->M!=A->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->M,A->N); /* For now, we do not dispatch based on the type of A and P */ /* When implementations like _SeqAIJ_MAIJ exist, attack the multiple dispatch problem. */ fA = A->ops->ptapsymbolic; if (!fA) SETERRQ1(PETSC_ERR_SUP,"MatPtAPSymbolic not supported for A of type %s",A->type_name); fP = P->ops->ptapsymbolic; if (!fP) SETERRQ1(PETSC_ERR_SUP,"MatPtAPSymbolic not supported for P of type %s",P->type_name); if (fP!=fA) SETERRQ2(PETSC_ERR_ARG_INCOMP,"MatPtAPSymbolic requires A, %s, to be compatible with P, %s",A->type_name,P->type_name); ierr = PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); ierr = (*fA)(A,P,fill,C);CHKERRQ(ierr); ierr = PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); PetscFunctionReturn(0); }