1 2 /* 3 This is where the abstract matrix operations are defined 4 */ 5 6 #include <petsc-private/matimpl.h> /*I "petscmat.h" I*/ 7 #include <petsc-private/vecimpl.h> 8 9 /* Logging support */ 10 PetscClassId MAT_CLASSID; 11 PetscClassId MAT_FDCOLORING_CLASSID; 12 PetscClassId MAT_TRANSPOSECOLORING_CLASSID; 13 14 PetscLogEvent MAT_Mult, MAT_Mults, MAT_MultConstrained, MAT_MultAdd, MAT_MultTranspose; 15 PetscLogEvent MAT_MultTransposeConstrained, MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve; 16 PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic; 17 PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor; 18 PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin; 19 PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_GetSubMatrices, MAT_GetColoring, MAT_GetOrdering, MAT_GetRedundantMatrix, MAT_GetSeqNonzeroStructure; 20 PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate; 21 PetscLogEvent MAT_FDColoringApply,MAT_Transpose,MAT_FDColoringFunction; 22 PetscLogEvent MAT_TransposeColoringCreate; 23 PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric; 24 PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric,MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric; 25 PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric; 26 PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric; 27 PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric; 28 PetscLogEvent MAT_MultHermitianTranspose,MAT_MultHermitianTransposeAdd; 29 PetscLogEvent MAT_Getsymtranspose, MAT_Getsymtransreduced, MAT_Transpose_SeqAIJ, MAT_GetBrowsOfAcols; 30 PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym; 31 PetscLogEvent MAT_Applypapt, MAT_Applypapt_numeric, MAT_Applypapt_symbolic, MAT_GetSequentialNonzeroStructure; 32 PetscLogEvent MAT_GetMultiProcBlock; 33 PetscLogEvent MAT_CUSPCopyToGPU, MAT_CUSPARSECopyToGPU, MAT_SetValuesBatch, MAT_SetValuesBatchI, MAT_SetValuesBatchII, MAT_SetValuesBatchIII, MAT_SetValuesBatchIV; 34 PetscLogEvent MAT_ViennaCLCopyToGPU; 35 PetscLogEvent MAT_Merge,MAT_Residual; 36 37 const char *const MatFactorTypes[] = {"NONE","LU","CHOLESKY","ILU","ICC","ILUDT","MatFactorType","MAT_FACTOR_",0}; 38 39 #undef __FUNCT__ 40 #define __FUNCT__ "MatSetRandom" 41 /*@ 42 MatSetRandom - Sets all components of a matrix to random numbers. For sparse matrices that have been preallocated it randomly selects appropriate locations 43 44 Logically Collective on Vec 45 46 Input Parameters: 47 + x - the vector 48 - rctx - the random number context, formed by PetscRandomCreate(), or NULL and 49 it will create one internally. 50 51 Output Parameter: 52 . x - the vector 53 54 Example of Usage: 55 .vb 56 PetscRandomCreate(PETSC_COMM_WORLD,&rctx); 57 VecSetRandom(x,rctx); 58 PetscRandomDestroy(rctx); 59 .ve 60 61 Level: intermediate 62 63 Concepts: vector^setting to random 64 Concepts: random^vector 65 66 .seealso: MatZeroEntries(), MatSetValues(), PetscRandomCreate(), PetscRandomDestroy() 67 @*/ 68 PetscErrorCode MatSetRandom(Mat x,PetscRandom rctx) 69 { 70 PetscErrorCode ierr; 71 PetscRandom randObj = NULL; 72 73 PetscFunctionBegin; 74 PetscValidHeaderSpecific(x,MAT_CLASSID,1); 75 if (rctx) PetscValidHeaderSpecific(rctx,PETSC_RANDOM_CLASSID,2); 76 PetscValidType(x,1); 77 78 if (!rctx) { 79 MPI_Comm comm; 80 ierr = PetscObjectGetComm((PetscObject)x,&comm);CHKERRQ(ierr); 81 ierr = PetscRandomCreate(comm,&randObj);CHKERRQ(ierr); 82 ierr = PetscRandomSetFromOptions(randObj);CHKERRQ(ierr); 83 rctx = randObj; 84 } 85 86 ierr = PetscLogEventBegin(VEC_SetRandom,x,rctx,0,0);CHKERRQ(ierr); 87 ierr = (*x->ops->setrandom)(x,rctx);CHKERRQ(ierr); 88 ierr = PetscLogEventEnd(VEC_SetRandom,x,rctx,0,0);CHKERRQ(ierr); 89 90 x->assembled = PETSC_TRUE; 91 ierr = PetscRandomDestroy(&randObj);CHKERRQ(ierr); 92 PetscFunctionReturn(0); 93 } 94 95 96 #undef __FUNCT__ 97 #define __FUNCT__ "MatFindNonzeroRows" 98 /*@ 99 MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix 100 101 Input Parameter: 102 . A - the matrix 103 104 Output Parameter: 105 . keptrows - the rows that are not completely zero 106 107 Level: intermediate 108 109 @*/ 110 PetscErrorCode MatFindNonzeroRows(Mat mat,IS *keptrows) 111 { 112 PetscErrorCode ierr; 113 114 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 115 PetscValidType(mat,1); 116 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 117 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 118 if (!mat->ops->findnonzerorows) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not coded for this matrix type"); 119 ierr = (*mat->ops->findnonzerorows)(mat,keptrows);CHKERRQ(ierr); 120 PetscFunctionReturn(0); 121 } 122 123 #undef __FUNCT__ 124 #define __FUNCT__ "MatGetDiagonalBlock" 125 /*@ 126 MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling 127 128 Not Collective 129 130 Input Parameters: 131 . A - the matrix 132 133 Output Parameters: 134 . a - the diagonal part (which is a SEQUENTIAL matrix) 135 136 Notes: see the manual page for MatCreateAIJ() for more information on the "diagonal part" of the matrix. 137 138 Level: advanced 139 140 @*/ 141 PetscErrorCode MatGetDiagonalBlock(Mat A,Mat *a) 142 { 143 PetscErrorCode ierr,(*f)(Mat,Mat*); 144 PetscMPIInt size; 145 146 PetscFunctionBegin; 147 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 148 PetscValidType(A,1); 149 PetscValidPointer(a,3); 150 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 151 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 152 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr); 153 ierr = PetscObjectQueryFunction((PetscObject)A,"MatGetDiagonalBlock_C",&f);CHKERRQ(ierr); 154 if (f) { 155 ierr = (*f)(A,a);CHKERRQ(ierr); 156 PetscFunctionReturn(0); 157 } else if (size == 1) { 158 *a = A; 159 } else { 160 MatType mattype; 161 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 162 SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix type %s does not support getting diagonal block",mattype); 163 } 164 PetscFunctionReturn(0); 165 } 166 167 #undef __FUNCT__ 168 #define __FUNCT__ "MatGetTrace" 169 /*@ 170 MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries. 171 172 Collective on Mat 173 174 Input Parameters: 175 . mat - the matrix 176 177 Output Parameter: 178 . trace - the sum of the diagonal entries 179 180 Level: advanced 181 182 @*/ 183 PetscErrorCode MatGetTrace(Mat mat,PetscScalar *trace) 184 { 185 PetscErrorCode ierr; 186 Vec diag; 187 188 PetscFunctionBegin; 189 ierr = MatGetVecs(mat,&diag,NULL);CHKERRQ(ierr); 190 ierr = MatGetDiagonal(mat,diag);CHKERRQ(ierr); 191 ierr = VecSum(diag,trace);CHKERRQ(ierr); 192 ierr = VecDestroy(&diag);CHKERRQ(ierr); 193 PetscFunctionReturn(0); 194 } 195 196 #undef __FUNCT__ 197 #define __FUNCT__ "MatRealPart" 198 /*@ 199 MatRealPart - Zeros out the imaginary part of the matrix 200 201 Logically Collective on Mat 202 203 Input Parameters: 204 . mat - the matrix 205 206 Level: advanced 207 208 209 .seealso: MatImaginaryPart() 210 @*/ 211 PetscErrorCode MatRealPart(Mat mat) 212 { 213 PetscErrorCode ierr; 214 215 PetscFunctionBegin; 216 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 217 PetscValidType(mat,1); 218 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 219 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 220 if (!mat->ops->realpart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 221 MatCheckPreallocated(mat,1); 222 ierr = (*mat->ops->realpart)(mat);CHKERRQ(ierr); 223 #if defined(PETSC_HAVE_CUSP) 224 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 225 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 226 } 227 #endif 228 #if defined(PETSC_HAVE_VIENNACL) 229 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 230 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 231 } 232 #endif 233 PetscFunctionReturn(0); 234 } 235 236 #undef __FUNCT__ 237 #define __FUNCT__ "MatGetGhosts" 238 /*@C 239 MatGetGhosts - Get the global index of all ghost nodes defined by the sparse matrix 240 241 Collective on Mat 242 243 Input Parameter: 244 . mat - the matrix 245 246 Output Parameters: 247 + nghosts - number of ghosts (note for BAIJ matrices there is one ghost for each block) 248 - ghosts - the global indices of the ghost points 249 250 Notes: the nghosts and ghosts are suitable to pass into VecCreateGhost() 251 252 Level: advanced 253 254 @*/ 255 PetscErrorCode MatGetGhosts(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[]) 256 { 257 PetscErrorCode ierr; 258 259 PetscFunctionBegin; 260 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 261 PetscValidType(mat,1); 262 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 263 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 264 if (!mat->ops->getghosts) { 265 if (nghosts) *nghosts = 0; 266 if (ghosts) *ghosts = 0; 267 } else { 268 ierr = (*mat->ops->getghosts)(mat,nghosts,ghosts);CHKERRQ(ierr); 269 } 270 PetscFunctionReturn(0); 271 } 272 273 274 #undef __FUNCT__ 275 #define __FUNCT__ "MatImaginaryPart" 276 /*@ 277 MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part 278 279 Logically Collective on Mat 280 281 Input Parameters: 282 . mat - the matrix 283 284 Level: advanced 285 286 287 .seealso: MatRealPart() 288 @*/ 289 PetscErrorCode MatImaginaryPart(Mat mat) 290 { 291 PetscErrorCode ierr; 292 293 PetscFunctionBegin; 294 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 295 PetscValidType(mat,1); 296 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 297 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 298 if (!mat->ops->imaginarypart) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 299 MatCheckPreallocated(mat,1); 300 ierr = (*mat->ops->imaginarypart)(mat);CHKERRQ(ierr); 301 #if defined(PETSC_HAVE_CUSP) 302 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 303 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 304 } 305 #endif 306 #if defined(PETSC_HAVE_VIENNACL) 307 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 308 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 309 } 310 #endif 311 PetscFunctionReturn(0); 312 } 313 314 #undef __FUNCT__ 315 #define __FUNCT__ "MatMissingDiagonal" 316 /*@ 317 MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for BAIJ matrices) 318 319 Collective on Mat 320 321 Input Parameter: 322 . mat - the matrix 323 324 Output Parameters: 325 + missing - is any diagonal missing 326 - dd - first diagonal entry that is missing (optional) 327 328 Level: advanced 329 330 331 .seealso: MatRealPart() 332 @*/ 333 PetscErrorCode MatMissingDiagonal(Mat mat,PetscBool *missing,PetscInt *dd) 334 { 335 PetscErrorCode ierr; 336 337 PetscFunctionBegin; 338 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 339 PetscValidType(mat,1); 340 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 341 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 342 if (!mat->ops->missingdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 343 ierr = (*mat->ops->missingdiagonal)(mat,missing,dd);CHKERRQ(ierr); 344 PetscFunctionReturn(0); 345 } 346 347 #undef __FUNCT__ 348 #define __FUNCT__ "MatGetRow" 349 /*@C 350 MatGetRow - Gets a row of a matrix. You MUST call MatRestoreRow() 351 for each row that you get to ensure that your application does 352 not bleed memory. 353 354 Not Collective 355 356 Input Parameters: 357 + mat - the matrix 358 - row - the row to get 359 360 Output Parameters: 361 + ncols - if not NULL, the number of nonzeros in the row 362 . cols - if not NULL, the column numbers 363 - vals - if not NULL, the values 364 365 Notes: 366 This routine is provided for people who need to have direct access 367 to the structure of a matrix. We hope that we provide enough 368 high-level matrix routines that few users will need it. 369 370 MatGetRow() always returns 0-based column indices, regardless of 371 whether the internal representation is 0-based (default) or 1-based. 372 373 For better efficiency, set cols and/or vals to NULL if you do 374 not wish to extract these quantities. 375 376 The user can only examine the values extracted with MatGetRow(); 377 the values cannot be altered. To change the matrix entries, one 378 must use MatSetValues(). 379 380 You can only have one call to MatGetRow() outstanding for a particular 381 matrix at a time, per processor. MatGetRow() can only obtain rows 382 associated with the given processor, it cannot get rows from the 383 other processors; for that we suggest using MatGetSubMatrices(), then 384 MatGetRow() on the submatrix. The row indix passed to MatGetRows() 385 is in the global number of rows. 386 387 Fortran Notes: 388 The calling sequence from Fortran is 389 .vb 390 MatGetRow(matrix,row,ncols,cols,values,ierr) 391 Mat matrix (input) 392 integer row (input) 393 integer ncols (output) 394 integer cols(maxcols) (output) 395 double precision (or double complex) values(maxcols) output 396 .ve 397 where maxcols >= maximum nonzeros in any row of the matrix. 398 399 400 Caution: 401 Do not try to change the contents of the output arrays (cols and vals). 402 In some cases, this may corrupt the matrix. 403 404 Level: advanced 405 406 Concepts: matrices^row access 407 408 .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatGetSubMatrices(), MatGetDiagonal() 409 @*/ 410 PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) 411 { 412 PetscErrorCode ierr; 413 PetscInt incols; 414 415 PetscFunctionBegin; 416 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 417 PetscValidType(mat,1); 418 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 419 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 420 if (!mat->ops->getrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 421 MatCheckPreallocated(mat,1); 422 ierr = PetscLogEventBegin(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); 423 ierr = (*mat->ops->getrow)(mat,row,&incols,(PetscInt**)cols,(PetscScalar**)vals);CHKERRQ(ierr); 424 if (ncols) *ncols = incols; 425 ierr = PetscLogEventEnd(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); 426 PetscFunctionReturn(0); 427 } 428 429 #undef __FUNCT__ 430 #define __FUNCT__ "MatConjugate" 431 /*@ 432 MatConjugate - replaces the matrix values with their complex conjugates 433 434 Logically Collective on Mat 435 436 Input Parameters: 437 . mat - the matrix 438 439 Level: advanced 440 441 .seealso: VecConjugate() 442 @*/ 443 PetscErrorCode MatConjugate(Mat mat) 444 { 445 #if defined(PETSC_USE_COMPLEX) 446 PetscErrorCode ierr; 447 448 PetscFunctionBegin; 449 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 450 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 451 if (!mat->ops->conjugate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not provided for this matrix format, send email to petsc-maint@mcs.anl.gov"); 452 ierr = (*mat->ops->conjugate)(mat);CHKERRQ(ierr); 453 #if defined(PETSC_HAVE_CUSP) 454 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 455 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 456 } 457 #endif 458 #if defined(PETSC_HAVE_VIENNACL) 459 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 460 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 461 } 462 #endif 463 PetscFunctionReturn(0); 464 #else 465 return 0; 466 #endif 467 } 468 469 #undef __FUNCT__ 470 #define __FUNCT__ "MatRestoreRow" 471 /*@C 472 MatRestoreRow - Frees any temporary space allocated by MatGetRow(). 473 474 Not Collective 475 476 Input Parameters: 477 + mat - the matrix 478 . row - the row to get 479 . ncols, cols - the number of nonzeros and their columns 480 - vals - if nonzero the column values 481 482 Notes: 483 This routine should be called after you have finished examining the entries. 484 485 Fortran Notes: 486 The calling sequence from Fortran is 487 .vb 488 MatRestoreRow(matrix,row,ncols,cols,values,ierr) 489 Mat matrix (input) 490 integer row (input) 491 integer ncols (output) 492 integer cols(maxcols) (output) 493 double precision (or double complex) values(maxcols) output 494 .ve 495 Where maxcols >= maximum nonzeros in any row of the matrix. 496 497 In Fortran MatRestoreRow() MUST be called after MatGetRow() 498 before another call to MatGetRow() can be made. 499 500 Level: advanced 501 502 .seealso: MatGetRow() 503 @*/ 504 PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) 505 { 506 PetscErrorCode ierr; 507 508 PetscFunctionBegin; 509 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 510 if (ncols) PetscValidIntPointer(ncols,3); 511 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 512 if (!mat->ops->restorerow) PetscFunctionReturn(0); 513 ierr = (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);CHKERRQ(ierr); 514 if (ncols) *ncols = 0; 515 if (cols) *cols = NULL; 516 if (vals) *vals = NULL; 517 PetscFunctionReturn(0); 518 } 519 520 #undef __FUNCT__ 521 #define __FUNCT__ "MatGetRowUpperTriangular" 522 /*@ 523 MatGetRowUpperTriangular - Sets a flag to enable calls to MatGetRow() for matrix in MATSBAIJ format. 524 You should call MatRestoreRowUpperTriangular() after calling MatGetRow/MatRestoreRow() to disable the flag. 525 526 Not Collective 527 528 Input Parameters: 529 + mat - the matrix 530 531 Notes: 532 The flag is to ensure that users are aware of MatGetRow() only provides the upper trianglular part of the row for the matrices in MATSBAIJ format. 533 534 Level: advanced 535 536 Concepts: matrices^row access 537 538 .seealso: MatRestoreRowRowUpperTriangular() 539 @*/ 540 PetscErrorCode MatGetRowUpperTriangular(Mat mat) 541 { 542 PetscErrorCode ierr; 543 544 PetscFunctionBegin; 545 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 546 PetscValidType(mat,1); 547 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 548 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 549 if (!mat->ops->getrowuppertriangular) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 550 MatCheckPreallocated(mat,1); 551 ierr = (*mat->ops->getrowuppertriangular)(mat);CHKERRQ(ierr); 552 PetscFunctionReturn(0); 553 } 554 555 #undef __FUNCT__ 556 #define __FUNCT__ "MatRestoreRowUpperTriangular" 557 /*@ 558 MatRestoreRowUpperTriangular - Disable calls to MatGetRow() for matrix in MATSBAIJ format. 559 560 Not Collective 561 562 Input Parameters: 563 + mat - the matrix 564 565 Notes: 566 This routine should be called after you have finished MatGetRow/MatRestoreRow(). 567 568 569 Level: advanced 570 571 .seealso: MatGetRowUpperTriangular() 572 @*/ 573 PetscErrorCode MatRestoreRowUpperTriangular(Mat mat) 574 { 575 PetscErrorCode ierr; 576 577 PetscFunctionBegin; 578 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 579 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 580 if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(0); 581 ierr = (*mat->ops->restorerowuppertriangular)(mat);CHKERRQ(ierr); 582 PetscFunctionReturn(0); 583 } 584 585 #undef __FUNCT__ 586 #define __FUNCT__ "MatSetOptionsPrefix" 587 /*@C 588 MatSetOptionsPrefix - Sets the prefix used for searching for all 589 Mat options in the database. 590 591 Logically Collective on Mat 592 593 Input Parameter: 594 + A - the Mat context 595 - prefix - the prefix to prepend to all option names 596 597 Notes: 598 A hyphen (-) must NOT be given at the beginning of the prefix name. 599 The first character of all runtime options is AUTOMATICALLY the hyphen. 600 601 Level: advanced 602 603 .keywords: Mat, set, options, prefix, database 604 605 .seealso: MatSetFromOptions() 606 @*/ 607 PetscErrorCode MatSetOptionsPrefix(Mat A,const char prefix[]) 608 { 609 PetscErrorCode ierr; 610 611 PetscFunctionBegin; 612 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 613 ierr = PetscObjectSetOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); 614 PetscFunctionReturn(0); 615 } 616 617 #undef __FUNCT__ 618 #define __FUNCT__ "MatAppendOptionsPrefix" 619 /*@C 620 MatAppendOptionsPrefix - Appends to the prefix used for searching for all 621 Mat options in the database. 622 623 Logically Collective on Mat 624 625 Input Parameters: 626 + A - the Mat context 627 - prefix - the prefix to prepend to all option names 628 629 Notes: 630 A hyphen (-) must NOT be given at the beginning of the prefix name. 631 The first character of all runtime options is AUTOMATICALLY the hyphen. 632 633 Level: advanced 634 635 .keywords: Mat, append, options, prefix, database 636 637 .seealso: MatGetOptionsPrefix() 638 @*/ 639 PetscErrorCode MatAppendOptionsPrefix(Mat A,const char prefix[]) 640 { 641 PetscErrorCode ierr; 642 643 PetscFunctionBegin; 644 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 645 ierr = PetscObjectAppendOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); 646 PetscFunctionReturn(0); 647 } 648 649 #undef __FUNCT__ 650 #define __FUNCT__ "MatGetOptionsPrefix" 651 /*@C 652 MatGetOptionsPrefix - Sets the prefix used for searching for all 653 Mat options in the database. 654 655 Not Collective 656 657 Input Parameter: 658 . A - the Mat context 659 660 Output Parameter: 661 . prefix - pointer to the prefix string used 662 663 Notes: On the fortran side, the user should pass in a string 'prefix' of 664 sufficient length to hold the prefix. 665 666 Level: advanced 667 668 .keywords: Mat, get, options, prefix, database 669 670 .seealso: MatAppendOptionsPrefix() 671 @*/ 672 PetscErrorCode MatGetOptionsPrefix(Mat A,const char *prefix[]) 673 { 674 PetscErrorCode ierr; 675 676 PetscFunctionBegin; 677 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 678 ierr = PetscObjectGetOptionsPrefix((PetscObject)A,prefix);CHKERRQ(ierr); 679 PetscFunctionReturn(0); 680 } 681 682 #undef __FUNCT__ 683 #define __FUNCT__ "MatSetUp" 684 /*@ 685 MatSetUp - Sets up the internal matrix data structures for the later use. 686 687 Collective on Mat 688 689 Input Parameters: 690 . A - the Mat context 691 692 Notes: 693 If the user has not set preallocation for this matrix then a default preallocation that is likely to be inefficient is used. 694 695 If a suitable preallocation routine is used, this function does not need to be called. 696 697 See the Performance chapter of the PETSc users manual for how to preallocate matrices 698 699 Level: beginner 700 701 .keywords: Mat, setup 702 703 .seealso: MatCreate(), MatDestroy() 704 @*/ 705 PetscErrorCode MatSetUp(Mat A) 706 { 707 PetscMPIInt size; 708 PetscErrorCode ierr; 709 710 PetscFunctionBegin; 711 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 712 if (!((PetscObject)A)->type_name) { 713 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A), &size);CHKERRQ(ierr); 714 if (size == 1) { 715 ierr = MatSetType(A, MATSEQAIJ);CHKERRQ(ierr); 716 } else { 717 ierr = MatSetType(A, MATMPIAIJ);CHKERRQ(ierr); 718 } 719 } 720 if (!A->preallocated && A->ops->setup) { 721 ierr = PetscInfo(A,"Warning not preallocating matrix storage\n");CHKERRQ(ierr); 722 ierr = (*A->ops->setup)(A);CHKERRQ(ierr); 723 } 724 A->preallocated = PETSC_TRUE; 725 PetscFunctionReturn(0); 726 } 727 728 #if defined(PETSC_HAVE_AMS) 729 #include <petscviewerams.h> 730 #endif 731 #undef __FUNCT__ 732 #define __FUNCT__ "MatView" 733 /*@C 734 MatView - Visualizes a matrix object. 735 736 Collective on Mat 737 738 Input Parameters: 739 + mat - the matrix 740 - viewer - visualization context 741 742 Notes: 743 The available visualization contexts include 744 + PETSC_VIEWER_STDOUT_SELF - standard output (default) 745 . PETSC_VIEWER_STDOUT_WORLD - synchronized standard 746 output where only the first processor opens 747 the file. All other processors send their 748 data to the first processor to print. 749 - PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure 750 751 The user can open alternative visualization contexts with 752 + PetscViewerASCIIOpen() - Outputs matrix to a specified file 753 . PetscViewerBinaryOpen() - Outputs matrix in binary to a 754 specified file; corresponding input uses MatLoad() 755 . PetscViewerDrawOpen() - Outputs nonzero matrix structure to 756 an X window display 757 - PetscViewerSocketOpen() - Outputs matrix to Socket viewer. 758 Currently only the sequential dense and AIJ 759 matrix types support the Socket viewer. 760 761 The user can call PetscViewerSetFormat() to specify the output 762 format of ASCII printed objects (when using PETSC_VIEWER_STDOUT_SELF, 763 PETSC_VIEWER_STDOUT_WORLD and PetscViewerASCIIOpen). Available formats include 764 + PETSC_VIEWER_DEFAULT - default, prints matrix contents 765 . PETSC_VIEWER_ASCII_MATLAB - prints matrix contents in Matlab format 766 . PETSC_VIEWER_ASCII_DENSE - prints entire matrix including zeros 767 . PETSC_VIEWER_ASCII_COMMON - prints matrix contents, using a sparse 768 format common among all matrix types 769 . PETSC_VIEWER_ASCII_IMPL - prints matrix contents, using an implementation-specific 770 format (which is in many cases the same as the default) 771 . PETSC_VIEWER_ASCII_INFO - prints basic information about the matrix 772 size and structure (not the matrix entries) 773 . PETSC_VIEWER_ASCII_INFO_DETAIL - prints more detailed information about 774 the matrix structure 775 776 Options Database Keys: 777 + -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly() 778 . -mat_view ::ascii_info_detail - Prints more detailed info 779 . -mat_view - Prints matrix in ASCII format 780 . -mat_view ::ascii_matlab - Prints matrix in Matlab format 781 . -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). 782 . -display <name> - Sets display name (default is host) 783 . -draw_pause <sec> - Sets number of seconds to pause after display 784 . -mat_view socket - Sends matrix to socket, can be accessed from Matlab (see the <a href="../../docs/manual.pdf">users manual</a> for details). 785 . -viewer_socket_machine <machine> 786 . -viewer_socket_port <port> 787 . -mat_view binary - save matrix to file in binary format 788 - -viewer_binary_filename <name> 789 Level: beginner 790 791 Notes: see the manual page for MatLoad() for the exact format of the binary file when the binary 792 viewer is used. 793 794 See bin/matlab/PetscBinaryRead.m for a Matlab code that can read in the binary file when the binary 795 viewer is used. 796 797 One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure. 798 And then use the following mouse functions: 799 left mouse: zoom in 800 middle mouse: zoom out 801 right mouse: continue with the simulation 802 803 Concepts: matrices^viewing 804 Concepts: matrices^plotting 805 Concepts: matrices^printing 806 807 .seealso: PetscViewerSetFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(), 808 PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad() 809 @*/ 810 PetscErrorCode MatView(Mat mat,PetscViewer viewer) 811 { 812 PetscErrorCode ierr; 813 PetscInt rows,cols,bs; 814 PetscBool iascii; 815 PetscViewerFormat format; 816 #if defined(PETSC_HAVE_AMS) 817 PetscBool isams; 818 #endif 819 820 PetscFunctionBegin; 821 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 822 PetscValidType(mat,1); 823 if (!viewer) { 824 ierr = PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat),&viewer);CHKERRQ(ierr); 825 } 826 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 827 PetscCheckSameComm(mat,1,viewer,2); 828 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix"); 829 MatCheckPreallocated(mat,1); 830 831 ierr = PetscLogEventBegin(MAT_View,mat,viewer,0,0);CHKERRQ(ierr); 832 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 833 #if defined(PETSC_HAVE_AMS) 834 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERAMS,&isams);CHKERRQ(ierr); 835 #endif 836 if (iascii) { 837 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 838 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 839 ierr = PetscObjectPrintClassNamePrefixType((PetscObject)mat,viewer);CHKERRQ(ierr); 840 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 841 ierr = MatGetSize(mat,&rows,&cols);CHKERRQ(ierr); 842 ierr = MatGetBlockSize(mat,&bs);CHKERRQ(ierr); 843 if (bs != 1) { 844 ierr = PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D, bs=%D\n",rows,cols,bs);CHKERRQ(ierr); 845 } else { 846 ierr = PetscViewerASCIIPrintf(viewer,"rows=%D, cols=%D\n",rows,cols);CHKERRQ(ierr); 847 } 848 if (mat->factortype) { 849 const MatSolverPackage solver; 850 ierr = MatFactorGetSolverPackage(mat,&solver);CHKERRQ(ierr); 851 ierr = PetscViewerASCIIPrintf(viewer,"package used to perform factorization: %s\n",solver);CHKERRQ(ierr); 852 } 853 if (mat->ops->getinfo) { 854 MatInfo info; 855 ierr = MatGetInfo(mat,MAT_GLOBAL_SUM,&info);CHKERRQ(ierr); 856 ierr = PetscViewerASCIIPrintf(viewer,"total: nonzeros=%lld, allocated nonzeros=%lld\n",(Petsc64bitInt)info.nz_used,(Petsc64bitInt)info.nz_allocated);CHKERRQ(ierr); 857 ierr = PetscViewerASCIIPrintf(viewer,"total number of mallocs used during MatSetValues calls =%D\n",(PetscInt)info.mallocs);CHKERRQ(ierr); 858 } 859 if (mat->nullsp) {ierr = PetscViewerASCIIPrintf(viewer," has attached null space\n");CHKERRQ(ierr);} 860 if (mat->nearnullsp) {ierr = PetscViewerASCIIPrintf(viewer," has attached near null space\n");CHKERRQ(ierr);} 861 } 862 #if defined(PETSC_HAVE_AMS) 863 } else if (isams) { 864 if (((PetscObject)mat)->amsmem == -1) { 865 ierr = PetscObjectViewAMS((PetscObject)mat,viewer);CHKERRQ(ierr); 866 } 867 #endif 868 } 869 if (mat->ops->view) { 870 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 871 ierr = (*mat->ops->view)(mat,viewer);CHKERRQ(ierr); 872 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 873 } else if (!iascii) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Viewer type %s not supported",((PetscObject)viewer)->type_name); 874 if (iascii) { 875 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 876 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 877 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 878 } 879 } 880 ierr = PetscLogEventEnd(MAT_View,mat,viewer,0,0);CHKERRQ(ierr); 881 PetscFunctionReturn(0); 882 } 883 884 #if defined(PETSC_USE_DEBUG) 885 #include <../src/sys/totalview/tv_data_display.h> 886 PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat) 887 { 888 TV_add_row("Local rows", "int", &mat->rmap->n); 889 TV_add_row("Local columns", "int", &mat->cmap->n); 890 TV_add_row("Global rows", "int", &mat->rmap->N); 891 TV_add_row("Global columns", "int", &mat->cmap->N); 892 TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name); 893 return TV_format_OK; 894 } 895 #endif 896 897 #undef __FUNCT__ 898 #define __FUNCT__ "MatLoad" 899 /*@C 900 MatLoad - Loads a matrix that has been stored in binary format 901 with MatView(). The matrix format is determined from the options database. 902 Generates a parallel MPI matrix if the communicator has more than one 903 processor. The default matrix type is AIJ. 904 905 Collective on PetscViewer 906 907 Input Parameters: 908 + newmat - the newly loaded matrix, this needs to have been created with MatCreate() 909 or some related function before a call to MatLoad() 910 - viewer - binary file viewer, created with PetscViewerBinaryOpen() 911 912 Options Database Keys: 913 Used with block matrix formats (MATSEQBAIJ, ...) to specify 914 block size 915 . -matload_block_size <bs> 916 917 Level: beginner 918 919 Notes: 920 If the Mat type has not yet been given then MATAIJ is used, call MatSetFromOptions() on the 921 Mat before calling this routine if you wish to set it from the options database. 922 923 MatLoad() automatically loads into the options database any options 924 given in the file filename.info where filename is the name of the file 925 that was passed to the PetscViewerBinaryOpen(). The options in the info 926 file will be ignored if you use the -viewer_binary_skip_info option. 927 928 If the type or size of newmat is not set before a call to MatLoad, PETSc 929 sets the default matrix type AIJ and sets the local and global sizes. 930 If type and/or size is already set, then the same are used. 931 932 In parallel, each processor can load a subset of rows (or the 933 entire matrix). This routine is especially useful when a large 934 matrix is stored on disk and only part of it is desired on each 935 processor. For example, a parallel solver may access only some of 936 the rows from each processor. The algorithm used here reads 937 relatively small blocks of data rather than reading the entire 938 matrix and then subsetting it. 939 940 Notes for advanced users: 941 Most users should not need to know the details of the binary storage 942 format, since MatLoad() and MatView() completely hide these details. 943 But for anyone who's interested, the standard binary matrix storage 944 format is 945 946 $ int MAT_FILE_CLASSID 947 $ int number of rows 948 $ int number of columns 949 $ int total number of nonzeros 950 $ int *number nonzeros in each row 951 $ int *column indices of all nonzeros (starting index is zero) 952 $ PetscScalar *values of all nonzeros 953 954 PETSc automatically does the byte swapping for 955 machines that store the bytes reversed, e.g. DEC alpha, freebsd, 956 linux, Windows and the paragon; thus if you write your own binary 957 read/write routines you have to swap the bytes; see PetscBinaryRead() 958 and PetscBinaryWrite() to see how this may be done. 959 960 .keywords: matrix, load, binary, input 961 962 .seealso: PetscViewerBinaryOpen(), MatView(), VecLoad() 963 964 @*/ 965 PetscErrorCode MatLoad(Mat newmat,PetscViewer viewer) 966 { 967 PetscErrorCode ierr; 968 PetscBool isbinary,flg; 969 970 PetscFunctionBegin; 971 PetscValidHeaderSpecific(newmat,MAT_CLASSID,1); 972 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 973 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);CHKERRQ(ierr); 974 if (!isbinary) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid viewer; open viewer with PetscViewerBinaryOpen()"); 975 976 if (!((PetscObject)newmat)->type_name) { 977 ierr = MatSetType(newmat,MATAIJ);CHKERRQ(ierr); 978 } 979 980 if (!newmat->ops->load) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatLoad is not supported for type"); 981 ierr = PetscLogEventBegin(MAT_Load,viewer,0,0,0);CHKERRQ(ierr); 982 ierr = (*newmat->ops->load)(newmat,viewer);CHKERRQ(ierr); 983 ierr = PetscLogEventEnd(MAT_Load,viewer,0,0,0);CHKERRQ(ierr); 984 985 flg = PETSC_FALSE; 986 ierr = PetscOptionsGetBool(((PetscObject)newmat)->prefix,"-matload_symmetric",&flg,NULL);CHKERRQ(ierr); 987 if (flg) { 988 ierr = MatSetOption(newmat,MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); 989 ierr = MatSetOption(newmat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE);CHKERRQ(ierr); 990 } 991 flg = PETSC_FALSE; 992 ierr = PetscOptionsGetBool(((PetscObject)newmat)->prefix,"-matload_spd",&flg,NULL);CHKERRQ(ierr); 993 if (flg) { 994 ierr = MatSetOption(newmat,MAT_SPD,PETSC_TRUE);CHKERRQ(ierr); 995 } 996 PetscFunctionReturn(0); 997 } 998 999 #undef __FUNCT__ 1000 #define __FUNCT__ "MatDestroy" 1001 /*@ 1002 MatDestroy - Frees space taken by a matrix. 1003 1004 Collective on Mat 1005 1006 Input Parameter: 1007 . A - the matrix 1008 1009 Level: beginner 1010 1011 @*/ 1012 PetscErrorCode MatDestroy(Mat *A) 1013 { 1014 PetscErrorCode ierr; 1015 1016 PetscFunctionBegin; 1017 if (!*A) PetscFunctionReturn(0); 1018 PetscValidHeaderSpecific(*A,MAT_CLASSID,1); 1019 if (--((PetscObject)(*A))->refct > 0) {*A = NULL; PetscFunctionReturn(0);} 1020 1021 ierr = PetscViewerDestroy(&(*A)->viewonassembly);CHKERRQ(ierr); 1022 /* if memory was published with AMS then destroy it */ 1023 ierr = PetscObjectAMSViewOff((PetscObject)*A);CHKERRQ(ierr); 1024 if ((*A)->ops->destroy) { 1025 ierr = (*(*A)->ops->destroy)(*A);CHKERRQ(ierr); 1026 } 1027 ierr = MatNullSpaceDestroy(&(*A)->nullsp);CHKERRQ(ierr); 1028 ierr = MatNullSpaceDestroy(&(*A)->nearnullsp);CHKERRQ(ierr); 1029 ierr = PetscLayoutDestroy(&(*A)->rmap);CHKERRQ(ierr); 1030 ierr = PetscLayoutDestroy(&(*A)->cmap);CHKERRQ(ierr); 1031 ierr = PetscHeaderDestroy(A);CHKERRQ(ierr); 1032 PetscFunctionReturn(0); 1033 } 1034 1035 #undef __FUNCT__ 1036 #define __FUNCT__ "MatSetValues" 1037 /*@ 1038 MatSetValues - Inserts or adds a block of values into a matrix. 1039 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 1040 MUST be called after all calls to MatSetValues() have been completed. 1041 1042 Not Collective 1043 1044 Input Parameters: 1045 + mat - the matrix 1046 . v - a logically two-dimensional array of values 1047 . m, idxm - the number of rows and their global indices 1048 . n, idxn - the number of columns and their global indices 1049 - addv - either ADD_VALUES or INSERT_VALUES, where 1050 ADD_VALUES adds values to any existing entries, and 1051 INSERT_VALUES replaces existing entries with new values 1052 1053 Notes: 1054 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or 1055 MatSetUp() before using this routine 1056 1057 By default the values, v, are row-oriented. See MatSetOption() for other options. 1058 1059 Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES 1060 options cannot be mixed without intervening calls to the assembly 1061 routines. 1062 1063 MatSetValues() uses 0-based row and column numbers in Fortran 1064 as well as in C. 1065 1066 Negative indices may be passed in idxm and idxn, these rows and columns are 1067 simply ignored. This allows easily inserting element stiffness matrices 1068 with homogeneous Dirchlet boundary conditions that you don't want represented 1069 in the matrix. 1070 1071 Efficiency Alert: 1072 The routine MatSetValuesBlocked() may offer much better efficiency 1073 for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). 1074 1075 Level: beginner 1076 1077 Concepts: matrices^putting entries in 1078 1079 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 1080 InsertMode, INSERT_VALUES, ADD_VALUES 1081 @*/ 1082 PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 1083 { 1084 PetscErrorCode ierr; 1085 #if defined(PETSC_USE_DEBUG) 1086 PetscInt i,j; 1087 #endif 1088 1089 PetscFunctionBegin; 1090 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1091 PetscValidType(mat,1); 1092 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 1093 PetscValidIntPointer(idxm,3); 1094 PetscValidIntPointer(idxn,5); 1095 if (v) PetscValidScalarPointer(v,6); 1096 MatCheckPreallocated(mat,1); 1097 if (mat->insertmode == NOT_SET_VALUES) { 1098 mat->insertmode = addv; 1099 } 1100 #if defined(PETSC_USE_DEBUG) 1101 else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 1102 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1103 if (!mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 1104 1105 if (v) { 1106 for (i=0; i<m; i++) { 1107 for (j=0; j<n; j++) { 1108 if (PetscIsInfOrNanScalar(v[i*n+j])) 1109 #if defined(PETSC_USE_COMPLEX) 1110 SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %G+iG at matrix entry (%D,%D)",PetscRealPart(v[i*n+j]),PetscImaginaryPart(v[i*n+j]),idxm[i],idxn[j]); 1111 #else 1112 SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_FP,"Inserting %G at matrix entry (%D,%D)",(PetscReal)v[i*n+j],idxm[i],idxn[j]); 1113 #endif 1114 } 1115 } 1116 } 1117 #endif 1118 1119 if (mat->assembled) { 1120 mat->was_assembled = PETSC_TRUE; 1121 mat->assembled = PETSC_FALSE; 1122 } 1123 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1124 ierr = (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); 1125 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1126 #if defined(PETSC_HAVE_CUSP) 1127 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 1128 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 1129 } 1130 #endif 1131 #if defined(PETSC_HAVE_VIENNACL) 1132 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 1133 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 1134 } 1135 #endif 1136 PetscFunctionReturn(0); 1137 } 1138 1139 1140 #undef __FUNCT__ 1141 #define __FUNCT__ "MatSetValuesRowLocal" 1142 /*@ 1143 MatSetValuesRowLocal - Inserts a row (block row for BAIJ matrices) of nonzero 1144 values into a matrix 1145 1146 Not Collective 1147 1148 Input Parameters: 1149 + mat - the matrix 1150 . row - the (block) row to set 1151 - v - a logically two-dimensional array of values 1152 1153 Notes: 1154 By the values, v, are column-oriented (for the block version) and sorted 1155 1156 All the nonzeros in the row must be provided 1157 1158 The matrix must have previously had its column indices set 1159 1160 The row must belong to this process 1161 1162 Level: intermediate 1163 1164 Concepts: matrices^putting entries in 1165 1166 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 1167 InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues(), MatSetValuesRow(), MatSetLocalToGlobalMapping() 1168 @*/ 1169 PetscErrorCode MatSetValuesRowLocal(Mat mat,PetscInt row,const PetscScalar v[]) 1170 { 1171 PetscErrorCode ierr; 1172 PetscInt globalrow; 1173 1174 PetscFunctionBegin; 1175 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1176 PetscValidType(mat,1); 1177 PetscValidScalarPointer(v,2); 1178 ierr = ISLocalToGlobalMappingApply(mat->rmap->mapping,1,&row,&globalrow);CHKERRQ(ierr); 1179 ierr = MatSetValuesRow(mat,globalrow,v);CHKERRQ(ierr); 1180 #if defined(PETSC_HAVE_CUSP) 1181 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 1182 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 1183 } 1184 #endif 1185 #if defined(PETSC_HAVE_VIENNACL) 1186 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 1187 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 1188 } 1189 #endif 1190 PetscFunctionReturn(0); 1191 } 1192 1193 #undef __FUNCT__ 1194 #define __FUNCT__ "MatSetValuesRow" 1195 /*@ 1196 MatSetValuesRow - Inserts a row (block row for BAIJ matrices) of nonzero 1197 values into a matrix 1198 1199 Not Collective 1200 1201 Input Parameters: 1202 + mat - the matrix 1203 . row - the (block) row to set 1204 - v - a logically two-dimensional array of values 1205 1206 Notes: 1207 The values, v, are column-oriented for the block version. 1208 1209 All the nonzeros in the row must be provided 1210 1211 THE MATRIX MUSAT HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually MatSetValues() is used. 1212 1213 The row must belong to this process 1214 1215 Level: advanced 1216 1217 Concepts: matrices^putting entries in 1218 1219 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 1220 InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues() 1221 @*/ 1222 PetscErrorCode MatSetValuesRow(Mat mat,PetscInt row,const PetscScalar v[]) 1223 { 1224 PetscErrorCode ierr; 1225 1226 PetscFunctionBegin; 1227 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1228 PetscValidType(mat,1); 1229 MatCheckPreallocated(mat,1); 1230 PetscValidScalarPointer(v,2); 1231 #if defined(PETSC_USE_DEBUG) 1232 if (mat->insertmode == ADD_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add and insert values"); 1233 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1234 #endif 1235 mat->insertmode = INSERT_VALUES; 1236 1237 if (mat->assembled) { 1238 mat->was_assembled = PETSC_TRUE; 1239 mat->assembled = PETSC_FALSE; 1240 } 1241 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1242 if (!mat->ops->setvaluesrow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 1243 ierr = (*mat->ops->setvaluesrow)(mat,row,v);CHKERRQ(ierr); 1244 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1245 #if defined(PETSC_HAVE_CUSP) 1246 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 1247 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 1248 } 1249 #endif 1250 #if defined(PETSC_HAVE_VIENNACL) 1251 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 1252 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 1253 } 1254 #endif 1255 PetscFunctionReturn(0); 1256 } 1257 1258 #undef __FUNCT__ 1259 #define __FUNCT__ "MatSetValuesStencil" 1260 /*@ 1261 MatSetValuesStencil - Inserts or adds a block of values into a matrix. 1262 Using structured grid indexing 1263 1264 Not Collective 1265 1266 Input Parameters: 1267 + mat - the matrix 1268 . m - number of rows being entered 1269 . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered 1270 . n - number of columns being entered 1271 . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered 1272 . v - a logically two-dimensional array of values 1273 - addv - either ADD_VALUES or INSERT_VALUES, where 1274 ADD_VALUES adds values to any existing entries, and 1275 INSERT_VALUES replaces existing entries with new values 1276 1277 Notes: 1278 By default the values, v, are row-oriented. See MatSetOption() for other options. 1279 1280 Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES 1281 options cannot be mixed without intervening calls to the assembly 1282 routines. 1283 1284 The grid coordinates are across the entire grid, not just the local portion 1285 1286 MatSetValuesStencil() uses 0-based row and column numbers in Fortran 1287 as well as in C. 1288 1289 For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine 1290 1291 In order to use this routine you must either obtain the matrix with DMCreateMatrix() 1292 or call MatSetLocalToGlobalMapping() and MatSetStencil() first. 1293 1294 The columns and rows in the stencil passed in MUST be contained within the 1295 ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example, 1296 if you create a DMDA with an overlap of one grid level and on a particular process its first 1297 local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the 1298 first i index you can use in your column and row indices in MatSetStencil() is 5. 1299 1300 In Fortran idxm and idxn should be declared as 1301 $ MatStencil idxm(4,m),idxn(4,n) 1302 and the values inserted using 1303 $ idxm(MatStencil_i,1) = i 1304 $ idxm(MatStencil_j,1) = j 1305 $ idxm(MatStencil_k,1) = k 1306 $ idxm(MatStencil_c,1) = c 1307 etc 1308 1309 For periodic boundary conditions use negative indices for values to the left (below 0; that are to be 1310 obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one 1311 etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the 1312 DMDA_BOUNDARY_PERIODIC boundary type. 1313 1314 For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have 1315 a single value per point) you can skip filling those indices. 1316 1317 Inspired by the structured grid interface to the HYPRE package 1318 (http://www.llnl.gov/CASC/hypre) 1319 1320 Efficiency Alert: 1321 The routine MatSetValuesBlockedStencil() may offer much better efficiency 1322 for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). 1323 1324 Level: beginner 1325 1326 Concepts: matrices^putting entries in 1327 1328 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 1329 MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil 1330 @*/ 1331 PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) 1332 { 1333 PetscErrorCode ierr; 1334 PetscInt buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn; 1335 PetscInt j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; 1336 PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); 1337 1338 PetscFunctionBegin; 1339 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 1340 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1341 PetscValidType(mat,1); 1342 PetscValidIntPointer(idxm,3); 1343 PetscValidIntPointer(idxn,5); 1344 PetscValidScalarPointer(v,6); 1345 1346 if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 1347 jdxm = buf; jdxn = buf+m; 1348 } else { 1349 ierr = PetscMalloc2(m,PetscInt,&bufm,n,PetscInt,&bufn);CHKERRQ(ierr); 1350 jdxm = bufm; jdxn = bufn; 1351 } 1352 for (i=0; i<m; i++) { 1353 for (j=0; j<3-sdim; j++) dxm++; 1354 tmp = *dxm++ - starts[0]; 1355 for (j=0; j<dim-1; j++) { 1356 if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1; 1357 else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; 1358 } 1359 if (mat->stencil.noc) dxm++; 1360 jdxm[i] = tmp; 1361 } 1362 for (i=0; i<n; i++) { 1363 for (j=0; j<3-sdim; j++) dxn++; 1364 tmp = *dxn++ - starts[0]; 1365 for (j=0; j<dim-1; j++) { 1366 if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1; 1367 else tmp = tmp*dims[j] + *(dxn-1) - starts[j+1]; 1368 } 1369 if (mat->stencil.noc) dxn++; 1370 jdxn[i] = tmp; 1371 } 1372 ierr = MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);CHKERRQ(ierr); 1373 ierr = PetscFree2(bufm,bufn);CHKERRQ(ierr); 1374 PetscFunctionReturn(0); 1375 } 1376 1377 #undef __FUNCT__ 1378 #define __FUNCT__ "MatSetValuesBlockedStencil" 1379 /*@C 1380 MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix. 1381 Using structured grid indexing 1382 1383 Not Collective 1384 1385 Input Parameters: 1386 + mat - the matrix 1387 . m - number of rows being entered 1388 . idxm - grid coordinates for matrix rows being entered 1389 . n - number of columns being entered 1390 . idxn - grid coordinates for matrix columns being entered 1391 . v - a logically two-dimensional array of values 1392 - addv - either ADD_VALUES or INSERT_VALUES, where 1393 ADD_VALUES adds values to any existing entries, and 1394 INSERT_VALUES replaces existing entries with new values 1395 1396 Notes: 1397 By default the values, v, are row-oriented and unsorted. 1398 See MatSetOption() for other options. 1399 1400 Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES 1401 options cannot be mixed without intervening calls to the assembly 1402 routines. 1403 1404 The grid coordinates are across the entire grid, not just the local portion 1405 1406 MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran 1407 as well as in C. 1408 1409 For setting/accessing vector values via array coordinates you can use the DMDAVecGetArray() routine 1410 1411 In order to use this routine you must either obtain the matrix with DMCreateMatrix() 1412 or call MatSetBlockSize(), MatSetLocalToGlobalMapping() and MatSetStencil() first. 1413 1414 The columns and rows in the stencil passed in MUST be contained within the 1415 ghost region of the given process as set with DMDACreateXXX() or MatSetStencil(). For example, 1416 if you create a DMDA with an overlap of one grid level and on a particular process its first 1417 local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the 1418 first i index you can use in your column and row indices in MatSetStencil() is 5. 1419 1420 In Fortran idxm and idxn should be declared as 1421 $ MatStencil idxm(4,m),idxn(4,n) 1422 and the values inserted using 1423 $ idxm(MatStencil_i,1) = i 1424 $ idxm(MatStencil_j,1) = j 1425 $ idxm(MatStencil_k,1) = k 1426 etc 1427 1428 Negative indices may be passed in idxm and idxn, these rows and columns are 1429 simply ignored. This allows easily inserting element stiffness matrices 1430 with homogeneous Dirchlet boundary conditions that you don't want represented 1431 in the matrix. 1432 1433 Inspired by the structured grid interface to the HYPRE package 1434 (http://www.llnl.gov/CASC/hypre) 1435 1436 Level: beginner 1437 1438 Concepts: matrices^putting entries in 1439 1440 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 1441 MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DMCreateMatrix(), DMDAVecGetArray(), MatStencil, 1442 MatSetBlockSize(), MatSetLocalToGlobalMapping() 1443 @*/ 1444 PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) 1445 { 1446 PetscErrorCode ierr; 1447 PetscInt buf[8192],*bufm=0,*bufn=0,*jdxm,*jdxn; 1448 PetscInt j,i,dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; 1449 PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); 1450 1451 PetscFunctionBegin; 1452 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 1453 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1454 PetscValidType(mat,1); 1455 PetscValidIntPointer(idxm,3); 1456 PetscValidIntPointer(idxn,5); 1457 PetscValidScalarPointer(v,6); 1458 1459 if ((m+n) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 1460 jdxm = buf; jdxn = buf+m; 1461 } else { 1462 ierr = PetscMalloc2(m,PetscInt,&bufm,n,PetscInt,&bufn);CHKERRQ(ierr); 1463 jdxm = bufm; jdxn = bufn; 1464 } 1465 for (i=0; i<m; i++) { 1466 for (j=0; j<3-sdim; j++) dxm++; 1467 tmp = *dxm++ - starts[0]; 1468 for (j=0; j<sdim-1; j++) { 1469 if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = -1; 1470 else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; 1471 } 1472 dxm++; 1473 jdxm[i] = tmp; 1474 } 1475 for (i=0; i<n; i++) { 1476 for (j=0; j<3-sdim; j++) dxn++; 1477 tmp = *dxn++ - starts[0]; 1478 for (j=0; j<sdim-1; j++) { 1479 if ((*dxn++ - starts[j+1]) < 0 || tmp < 0) tmp = -1; 1480 else tmp = tmp*dims[j] + *(dxn-1) - starts[j+1]; 1481 } 1482 dxn++; 1483 jdxn[i] = tmp; 1484 } 1485 ierr = MatSetValuesBlockedLocal(mat,m,jdxm,n,jdxn,v,addv);CHKERRQ(ierr); 1486 ierr = PetscFree2(bufm,bufn);CHKERRQ(ierr); 1487 #if defined(PETSC_HAVE_CUSP) 1488 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 1489 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 1490 } 1491 #endif 1492 #if defined(PETSC_HAVE_VIENNACL) 1493 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 1494 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 1495 } 1496 #endif 1497 PetscFunctionReturn(0); 1498 } 1499 1500 #undef __FUNCT__ 1501 #define __FUNCT__ "MatSetStencil" 1502 /*@ 1503 MatSetStencil - Sets the grid information for setting values into a matrix via 1504 MatSetValuesStencil() 1505 1506 Not Collective 1507 1508 Input Parameters: 1509 + mat - the matrix 1510 . dim - dimension of the grid 1, 2, or 3 1511 . dims - number of grid points in x, y, and z direction, including ghost points on your processor 1512 . starts - starting point of ghost nodes on your processor in x, y, and z direction 1513 - dof - number of degrees of freedom per node 1514 1515 1516 Inspired by the structured grid interface to the HYPRE package 1517 (www.llnl.gov/CASC/hyper) 1518 1519 For matrices generated with DMCreateMatrix() this routine is automatically called and so not needed by the 1520 user. 1521 1522 Level: beginner 1523 1524 Concepts: matrices^putting entries in 1525 1526 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 1527 MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil() 1528 @*/ 1529 PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof) 1530 { 1531 PetscInt i; 1532 1533 PetscFunctionBegin; 1534 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1535 PetscValidIntPointer(dims,3); 1536 PetscValidIntPointer(starts,4); 1537 1538 mat->stencil.dim = dim + (dof > 1); 1539 for (i=0; i<dim; i++) { 1540 mat->stencil.dims[i] = dims[dim-i-1]; /* copy the values in backwards */ 1541 mat->stencil.starts[i] = starts[dim-i-1]; 1542 } 1543 mat->stencil.dims[dim] = dof; 1544 mat->stencil.starts[dim] = 0; 1545 mat->stencil.noc = (PetscBool)(dof == 1); 1546 PetscFunctionReturn(0); 1547 } 1548 1549 #undef __FUNCT__ 1550 #define __FUNCT__ "MatSetValuesBlocked" 1551 /*@ 1552 MatSetValuesBlocked - Inserts or adds a block of values into a matrix. 1553 1554 Not Collective 1555 1556 Input Parameters: 1557 + mat - the matrix 1558 . v - a logically two-dimensional array of values 1559 . m, idxm - the number of block rows and their global block indices 1560 . n, idxn - the number of block columns and their global block indices 1561 - addv - either ADD_VALUES or INSERT_VALUES, where 1562 ADD_VALUES adds values to any existing entries, and 1563 INSERT_VALUES replaces existing entries with new values 1564 1565 Notes: 1566 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call 1567 MatXXXXSetPreallocation() or MatSetUp() before using this routine. 1568 1569 The m and n count the NUMBER of blocks in the row direction and column direction, 1570 NOT the total number of rows/columns; for example, if the block size is 2 and 1571 you are passing in values for rows 2,3,4,5 then m would be 2 (not 4). 1572 The values in idxm would be 1 2; that is the first index for each block divided by 1573 the block size. 1574 1575 Note that you must call MatSetBlockSize() when constructing this matrix (after 1576 preallocating it). 1577 1578 By default the values, v, are row-oriented, so the layout of 1579 v is the same as for MatSetValues(). See MatSetOption() for other options. 1580 1581 Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES 1582 options cannot be mixed without intervening calls to the assembly 1583 routines. 1584 1585 MatSetValuesBlocked() uses 0-based row and column numbers in Fortran 1586 as well as in C. 1587 1588 Negative indices may be passed in idxm and idxn, these rows and columns are 1589 simply ignored. This allows easily inserting element stiffness matrices 1590 with homogeneous Dirchlet boundary conditions that you don't want represented 1591 in the matrix. 1592 1593 Each time an entry is set within a sparse matrix via MatSetValues(), 1594 internal searching must be done to determine where to place the the 1595 data in the matrix storage space. By instead inserting blocks of 1596 entries via MatSetValuesBlocked(), the overhead of matrix assembly is 1597 reduced. 1598 1599 Example: 1600 $ Suppose m=n=2 and block size(bs) = 2 The array is 1601 $ 1602 $ 1 2 | 3 4 1603 $ 5 6 | 7 8 1604 $ - - - | - - - 1605 $ 9 10 | 11 12 1606 $ 13 14 | 15 16 1607 $ 1608 $ v[] should be passed in like 1609 $ v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] 1610 $ 1611 $ If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then 1612 $ v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16] 1613 1614 Level: intermediate 1615 1616 Concepts: matrices^putting entries in blocked 1617 1618 .seealso: MatSetBlockSize(), MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal() 1619 @*/ 1620 PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 1621 { 1622 PetscErrorCode ierr; 1623 1624 PetscFunctionBegin; 1625 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1626 PetscValidType(mat,1); 1627 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 1628 PetscValidIntPointer(idxm,3); 1629 PetscValidIntPointer(idxn,5); 1630 PetscValidScalarPointer(v,6); 1631 MatCheckPreallocated(mat,1); 1632 if (mat->insertmode == NOT_SET_VALUES) { 1633 mat->insertmode = addv; 1634 } 1635 #if defined(PETSC_USE_DEBUG) 1636 else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 1637 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1638 if (!mat->ops->setvaluesblocked && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 1639 #endif 1640 1641 if (mat->assembled) { 1642 mat->was_assembled = PETSC_TRUE; 1643 mat->assembled = PETSC_FALSE; 1644 } 1645 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1646 if (mat->ops->setvaluesblocked) { 1647 ierr = (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); 1648 } else { 1649 PetscInt buf[8192],*bufr=0,*bufc=0,*iidxm,*iidxn; 1650 PetscInt i,j,bs=mat->rmap->bs; 1651 if ((m+n)*bs <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 1652 iidxm = buf; iidxn = buf + m*bs; 1653 } else { 1654 ierr = PetscMalloc2(m*bs,PetscInt,&bufr,n*bs,PetscInt,&bufc);CHKERRQ(ierr); 1655 iidxm = bufr; iidxn = bufc; 1656 } 1657 for (i=0; i<m; i++) { 1658 for (j=0; j<bs; j++) { 1659 iidxm[i*bs+j] = bs*idxm[i] + j; 1660 } 1661 } 1662 for (i=0; i<n; i++) { 1663 for (j=0; j<bs; j++) { 1664 iidxn[i*bs+j] = bs*idxn[i] + j; 1665 } 1666 } 1667 ierr = MatSetValues(mat,m*bs,iidxm,n*bs,iidxn,v,addv);CHKERRQ(ierr); 1668 ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); 1669 } 1670 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1671 #if defined(PETSC_HAVE_CUSP) 1672 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 1673 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 1674 } 1675 #endif 1676 #if defined(PETSC_HAVE_VIENNACL) 1677 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 1678 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 1679 } 1680 #endif 1681 PetscFunctionReturn(0); 1682 } 1683 1684 #undef __FUNCT__ 1685 #define __FUNCT__ "MatGetValues" 1686 /*@ 1687 MatGetValues - Gets a block of values from a matrix. 1688 1689 Not Collective; currently only returns a local block 1690 1691 Input Parameters: 1692 + mat - the matrix 1693 . v - a logically two-dimensional array for storing the values 1694 . m, idxm - the number of rows and their global indices 1695 - n, idxn - the number of columns and their global indices 1696 1697 Notes: 1698 The user must allocate space (m*n PetscScalars) for the values, v. 1699 The values, v, are then returned in a row-oriented format, 1700 analogous to that used by default in MatSetValues(). 1701 1702 MatGetValues() uses 0-based row and column numbers in 1703 Fortran as well as in C. 1704 1705 MatGetValues() requires that the matrix has been assembled 1706 with MatAssemblyBegin()/MatAssemblyEnd(). Thus, calls to 1707 MatSetValues() and MatGetValues() CANNOT be made in succession 1708 without intermediate matrix assembly. 1709 1710 Negative row or column indices will be ignored and those locations in v[] will be 1711 left unchanged. 1712 1713 Level: advanced 1714 1715 Concepts: matrices^accessing values 1716 1717 .seealso: MatGetRow(), MatGetSubMatrices(), MatSetValues() 1718 @*/ 1719 PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[]) 1720 { 1721 PetscErrorCode ierr; 1722 1723 PetscFunctionBegin; 1724 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1725 PetscValidType(mat,1); 1726 if (!m || !n) PetscFunctionReturn(0); 1727 PetscValidIntPointer(idxm,3); 1728 PetscValidIntPointer(idxn,5); 1729 PetscValidScalarPointer(v,6); 1730 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1731 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1732 if (!mat->ops->getvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 1733 MatCheckPreallocated(mat,1); 1734 1735 ierr = PetscLogEventBegin(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); 1736 ierr = (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);CHKERRQ(ierr); 1737 ierr = PetscLogEventEnd(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); 1738 PetscFunctionReturn(0); 1739 } 1740 1741 #undef __FUNCT__ 1742 #define __FUNCT__ "MatSetValuesBatch" 1743 /*@ 1744 MatSetValuesBatch - Adds (ADD_VALUES) many blocks of values into a matrix at once. The blocks must all be square and 1745 the same size. Currently, this can only be called once and creates the given matrix. 1746 1747 Not Collective 1748 1749 Input Parameters: 1750 + mat - the matrix 1751 . nb - the number of blocks 1752 . bs - the number of rows (and columns) in each block 1753 . rows - a concatenation of the rows for each block 1754 - v - a concatenation of logically two-dimensional arrays of values 1755 1756 Notes: 1757 In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix. 1758 1759 Level: advanced 1760 1761 Concepts: matrices^putting entries in 1762 1763 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 1764 InsertMode, INSERT_VALUES, ADD_VALUES, MatSetValues() 1765 @*/ 1766 PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[]) 1767 { 1768 PetscErrorCode ierr; 1769 1770 PetscFunctionBegin; 1771 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1772 PetscValidType(mat,1); 1773 PetscValidScalarPointer(rows,4); 1774 PetscValidScalarPointer(v,5); 1775 #if defined(PETSC_USE_DEBUG) 1776 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1777 #endif 1778 1779 ierr = PetscLogEventBegin(MAT_SetValuesBatch,mat,0,0,0);CHKERRQ(ierr); 1780 if (mat->ops->setvaluesbatch) { 1781 ierr = (*mat->ops->setvaluesbatch)(mat,nb,bs,rows,v);CHKERRQ(ierr); 1782 } else { 1783 PetscInt b; 1784 for (b = 0; b < nb; ++b) { 1785 ierr = MatSetValues(mat, bs, &rows[b*bs], bs, &rows[b*bs], &v[b*bs*bs], ADD_VALUES);CHKERRQ(ierr); 1786 } 1787 } 1788 ierr = PetscLogEventEnd(MAT_SetValuesBatch,mat,0,0,0);CHKERRQ(ierr); 1789 PetscFunctionReturn(0); 1790 } 1791 1792 #undef __FUNCT__ 1793 #define __FUNCT__ "MatSetLocalToGlobalMapping" 1794 /*@ 1795 MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by 1796 the routine MatSetValuesLocal() to allow users to insert matrix entries 1797 using a local (per-processor) numbering. 1798 1799 Not Collective 1800 1801 Input Parameters: 1802 + x - the matrix 1803 . rmapping - row mapping created with ISLocalToGlobalMappingCreate() 1804 or ISLocalToGlobalMappingCreateIS() 1805 - cmapping - column mapping 1806 1807 Level: intermediate 1808 1809 Concepts: matrices^local to global mapping 1810 Concepts: local to global mapping^for matrices 1811 1812 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal() 1813 @*/ 1814 PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping) 1815 { 1816 PetscErrorCode ierr; 1817 1818 PetscFunctionBegin; 1819 PetscValidHeaderSpecific(x,MAT_CLASSID,1); 1820 PetscValidType(x,1); 1821 PetscValidHeaderSpecific(rmapping,IS_LTOGM_CLASSID,2); 1822 PetscValidHeaderSpecific(cmapping,IS_LTOGM_CLASSID,3); 1823 1824 if (x->ops->setlocaltoglobalmapping) { 1825 ierr = (*x->ops->setlocaltoglobalmapping)(x,rmapping,cmapping);CHKERRQ(ierr); 1826 } else { 1827 ierr = PetscLayoutSetISLocalToGlobalMapping(x->rmap,rmapping);CHKERRQ(ierr); 1828 ierr = PetscLayoutSetISLocalToGlobalMapping(x->cmap,cmapping);CHKERRQ(ierr); 1829 } 1830 PetscFunctionReturn(0); 1831 } 1832 1833 #undef __FUNCT__ 1834 #define __FUNCT__ "MatSetLocalToGlobalMappingBlock" 1835 /*@ 1836 MatSetLocalToGlobalMappingBlock - Sets a local-to-global numbering for use 1837 by the routine MatSetValuesBlockedLocal() to allow users to insert matrix 1838 entries using a local (per-processor) numbering. 1839 1840 Not Collective 1841 1842 Input Parameters: 1843 + x - the matrix 1844 . rmapping - row mapping created with ISLocalToGlobalMappingCreate() or 1845 ISLocalToGlobalMappingCreateIS() 1846 - cmapping - column mapping 1847 1848 Level: intermediate 1849 1850 Concepts: matrices^local to global mapping blocked 1851 Concepts: local to global mapping^for matrices, blocked 1852 1853 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal(), 1854 MatSetValuesBlocked(), MatSetValuesLocal() 1855 @*/ 1856 PetscErrorCode MatSetLocalToGlobalMappingBlock(Mat x,ISLocalToGlobalMapping rmapping,ISLocalToGlobalMapping cmapping) 1857 { 1858 PetscErrorCode ierr; 1859 1860 PetscFunctionBegin; 1861 PetscValidHeaderSpecific(x,MAT_CLASSID,1); 1862 PetscValidType(x,1); 1863 PetscValidHeaderSpecific(rmapping,IS_LTOGM_CLASSID,2); 1864 PetscValidHeaderSpecific(cmapping,IS_LTOGM_CLASSID,3); 1865 1866 ierr = PetscLayoutSetISLocalToGlobalMappingBlock(x->rmap,rmapping);CHKERRQ(ierr); 1867 ierr = PetscLayoutSetISLocalToGlobalMappingBlock(x->cmap,cmapping);CHKERRQ(ierr); 1868 PetscFunctionReturn(0); 1869 } 1870 1871 #undef __FUNCT__ 1872 #define __FUNCT__ "MatGetLocalToGlobalMapping" 1873 /*@ 1874 MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by MatSetLocalToGlobalMapping() 1875 1876 Not Collective 1877 1878 Input Parameters: 1879 . A - the matrix 1880 1881 Output Parameters: 1882 + rmapping - row mapping 1883 - cmapping - column mapping 1884 1885 Level: advanced 1886 1887 Concepts: matrices^local to global mapping 1888 Concepts: local to global mapping^for matrices 1889 1890 .seealso: MatSetValuesLocal(), MatGetLocalToGlobalMappingBlock() 1891 @*/ 1892 PetscErrorCode MatGetLocalToGlobalMapping(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping) 1893 { 1894 PetscFunctionBegin; 1895 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 1896 PetscValidType(A,1); 1897 if (rmapping) PetscValidPointer(rmapping,2); 1898 if (cmapping) PetscValidPointer(cmapping,3); 1899 if (rmapping) *rmapping = A->rmap->mapping; 1900 if (cmapping) *cmapping = A->cmap->mapping; 1901 PetscFunctionReturn(0); 1902 } 1903 1904 #undef __FUNCT__ 1905 #define __FUNCT__ "MatGetLocalToGlobalMappingBlock" 1906 /*@ 1907 MatGetLocalToGlobalMappingBlock - Gets the local-to-global numbering set by MatSetLocalToGlobalMappingBlock() 1908 1909 Not Collective 1910 1911 Input Parameters: 1912 . A - the matrix 1913 1914 Output Parameters: 1915 + rmapping - row mapping 1916 - cmapping - column mapping 1917 1918 Level: advanced 1919 1920 Concepts: matrices^local to global mapping blocked 1921 Concepts: local to global mapping^for matrices, blocked 1922 1923 .seealso: MatSetValuesBlockedLocal(), MatGetLocalToGlobalMapping() 1924 @*/ 1925 PetscErrorCode MatGetLocalToGlobalMappingBlock(Mat A,ISLocalToGlobalMapping *rmapping,ISLocalToGlobalMapping *cmapping) 1926 { 1927 PetscFunctionBegin; 1928 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 1929 PetscValidType(A,1); 1930 if (rmapping) PetscValidPointer(rmapping,2); 1931 if (cmapping) PetscValidPointer(cmapping,3); 1932 if (rmapping) *rmapping = A->rmap->bmapping; 1933 if (cmapping) *cmapping = A->cmap->bmapping; 1934 PetscFunctionReturn(0); 1935 } 1936 1937 #undef __FUNCT__ 1938 #define __FUNCT__ "MatSetValuesLocal" 1939 /*@ 1940 MatSetValuesLocal - Inserts or adds values into certain locations of a matrix, 1941 using a local ordering of the nodes. 1942 1943 Not Collective 1944 1945 Input Parameters: 1946 + x - the matrix 1947 . nrow, irow - number of rows and their local indices 1948 . ncol, icol - number of columns and their local indices 1949 . y - a logically two-dimensional array of values 1950 - addv - either INSERT_VALUES or ADD_VALUES, where 1951 ADD_VALUES adds values to any existing entries, and 1952 INSERT_VALUES replaces existing entries with new values 1953 1954 Notes: 1955 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or 1956 MatSetUp() before using this routine 1957 1958 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetLocalToGlobalMapping() before using this routine 1959 1960 Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES 1961 options cannot be mixed without intervening calls to the assembly 1962 routines. 1963 1964 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 1965 MUST be called after all calls to MatSetValuesLocal() have been completed. 1966 1967 Level: intermediate 1968 1969 Concepts: matrices^putting entries in with local numbering 1970 1971 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(), 1972 MatSetValueLocal() 1973 @*/ 1974 PetscErrorCode MatSetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) 1975 { 1976 PetscErrorCode ierr; 1977 1978 PetscFunctionBegin; 1979 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 1980 PetscValidType(mat,1); 1981 MatCheckPreallocated(mat,1); 1982 if (!nrow || !ncol) PetscFunctionReturn(0); /* no values to insert */ 1983 PetscValidIntPointer(irow,3); 1984 PetscValidIntPointer(icol,5); 1985 PetscValidScalarPointer(y,6); 1986 if (mat->insertmode == NOT_SET_VALUES) { 1987 mat->insertmode = addv; 1988 } 1989 #if defined(PETSC_USE_DEBUG) 1990 else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 1991 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1992 if (!mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 1993 #endif 1994 1995 if (mat->assembled) { 1996 mat->was_assembled = PETSC_TRUE; 1997 mat->assembled = PETSC_FALSE; 1998 } 1999 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 2000 if (mat->ops->setvalueslocal) { 2001 ierr = (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); 2002 } else { 2003 PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm; 2004 if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 2005 irowm = buf; icolm = buf+nrow; 2006 } else { 2007 ierr = PetscMalloc2(nrow,PetscInt,&bufr,ncol,PetscInt,&bufc);CHKERRQ(ierr); 2008 irowm = bufr; icolm = bufc; 2009 } 2010 ierr = ISLocalToGlobalMappingApply(mat->rmap->mapping,nrow,irow,irowm);CHKERRQ(ierr); 2011 ierr = ISLocalToGlobalMappingApply(mat->cmap->mapping,ncol,icol,icolm);CHKERRQ(ierr); 2012 ierr = MatSetValues(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); 2013 ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); 2014 } 2015 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 2016 #if defined(PETSC_HAVE_CUSP) 2017 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 2018 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 2019 } 2020 #endif 2021 #if defined(PETSC_HAVE_VIENNACL) 2022 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 2023 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 2024 } 2025 #endif 2026 PetscFunctionReturn(0); 2027 } 2028 2029 #undef __FUNCT__ 2030 #define __FUNCT__ "MatSetValuesBlockedLocal" 2031 /*@ 2032 MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix, 2033 using a local ordering of the nodes a block at a time. 2034 2035 Not Collective 2036 2037 Input Parameters: 2038 + x - the matrix 2039 . nrow, irow - number of rows and their local indices 2040 . ncol, icol - number of columns and their local indices 2041 . y - a logically two-dimensional array of values 2042 - addv - either INSERT_VALUES or ADD_VALUES, where 2043 ADD_VALUES adds values to any existing entries, and 2044 INSERT_VALUES replaces existing entries with new values 2045 2046 Notes: 2047 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatXXXXSetPreallocation() or 2048 MatSetUp() before using this routine 2049 2050 If you create the matrix yourself (that is not with a call to DMCreateMatrix()) then you MUST call MatSetBlockSize() and MatSetLocalToGlobalMappingBlock() 2051 before using this routineBefore calling MatSetValuesLocal(), the user must first set the 2052 2053 Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES 2054 options cannot be mixed without intervening calls to the assembly 2055 routines. 2056 2057 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 2058 MUST be called after all calls to MatSetValuesBlockedLocal() have been completed. 2059 2060 Level: intermediate 2061 2062 Concepts: matrices^putting blocked values in with local numbering 2063 2064 .seealso: MatSetBlockSize(), MatSetLocalToGlobalMappingBlock(), MatAssemblyBegin(), MatAssemblyEnd(), 2065 MatSetValuesLocal(), MatSetLocalToGlobalMappingBlock(), MatSetValuesBlocked() 2066 @*/ 2067 PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) 2068 { 2069 PetscErrorCode ierr; 2070 2071 PetscFunctionBegin; 2072 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2073 PetscValidType(mat,1); 2074 MatCheckPreallocated(mat,1); 2075 if (!nrow || !ncol) PetscFunctionReturn(0); /* no values to insert */ 2076 PetscValidIntPointer(irow,3); 2077 PetscValidIntPointer(icol,5); 2078 PetscValidScalarPointer(y,6); 2079 if (mat->insertmode == NOT_SET_VALUES) { 2080 mat->insertmode = addv; 2081 } 2082 #if defined(PETSC_USE_DEBUG) 2083 else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 2084 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2085 if (!mat->ops->setvaluesblockedlocal && !mat->ops->setvaluesblocked && !mat->ops->setvalueslocal && !mat->ops->setvalues) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2086 #endif 2087 2088 if (mat->assembled) { 2089 mat->was_assembled = PETSC_TRUE; 2090 mat->assembled = PETSC_FALSE; 2091 } 2092 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 2093 if (mat->ops->setvaluesblockedlocal) { 2094 ierr = (*mat->ops->setvaluesblockedlocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); 2095 } else { 2096 PetscInt buf[8192],*bufr=0,*bufc=0,*irowm,*icolm; 2097 if (mat->rmap->bmapping && mat->cmap->bmapping) { 2098 if ((nrow+ncol) <= (PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 2099 irowm = buf; icolm = buf + nrow; 2100 } else { 2101 ierr = PetscMalloc2(nrow,PetscInt,&bufr,ncol,PetscInt,&bufc);CHKERRQ(ierr); 2102 irowm = bufr; icolm = bufc; 2103 } 2104 ierr = ISLocalToGlobalMappingApply(mat->rmap->bmapping,nrow,irow,irowm);CHKERRQ(ierr); 2105 ierr = ISLocalToGlobalMappingApply(mat->cmap->bmapping,ncol,icol,icolm);CHKERRQ(ierr); 2106 ierr = MatSetValuesBlocked(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); 2107 ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); 2108 } else { 2109 PetscInt i,j,bs=mat->rmap->bs; 2110 if ((nrow+ncol)*bs <=(PetscInt)(sizeof(buf)/sizeof(PetscInt))) { 2111 irowm = buf; icolm = buf + nrow; 2112 } else { 2113 ierr = PetscMalloc2(nrow*bs,PetscInt,&bufr,ncol*bs,PetscInt,&bufc);CHKERRQ(ierr); 2114 irowm = bufr; icolm = bufc; 2115 } 2116 for (i=0; i<nrow; i++) { 2117 for (j=0; j<bs; j++) irowm[i*bs+j] = irow[i]*bs+j; 2118 } 2119 for (i=0; i<ncol; i++) { 2120 for (j=0; j<bs; j++) icolm[i*bs+j] = icol[i]*bs+j; 2121 } 2122 ierr = MatSetValuesLocal(mat,nrow*bs,irowm,ncol*bs,icolm,y,addv);CHKERRQ(ierr); 2123 ierr = PetscFree2(bufr,bufc);CHKERRQ(ierr); 2124 } 2125 } 2126 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 2127 #if defined(PETSC_HAVE_CUSP) 2128 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 2129 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 2130 } 2131 #endif 2132 #if defined(PETSC_HAVE_VIENNACL) 2133 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 2134 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 2135 } 2136 #endif 2137 PetscFunctionReturn(0); 2138 } 2139 2140 #undef __FUNCT__ 2141 #define __FUNCT__ "MatMultDiagonalBlock" 2142 /*@ 2143 MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal 2144 2145 Collective on Mat and Vec 2146 2147 Input Parameters: 2148 + mat - the matrix 2149 - x - the vector to be multiplied 2150 2151 Output Parameters: 2152 . y - the result 2153 2154 Notes: 2155 The vectors x and y cannot be the same. I.e., one cannot 2156 call MatMult(A,y,y). 2157 2158 Level: developer 2159 2160 Concepts: matrix-vector product 2161 2162 .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 2163 @*/ 2164 PetscErrorCode MatMultDiagonalBlock(Mat mat,Vec x,Vec y) 2165 { 2166 PetscErrorCode ierr; 2167 2168 PetscFunctionBegin; 2169 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2170 PetscValidType(mat,1); 2171 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2172 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2173 2174 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2175 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2176 if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2177 MatCheckPreallocated(mat,1); 2178 2179 if (!mat->ops->multdiagonalblock) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined"); 2180 ierr = (*mat->ops->multdiagonalblock)(mat,x,y);CHKERRQ(ierr); 2181 ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); 2182 PetscFunctionReturn(0); 2183 } 2184 2185 /* --------------------------------------------------------*/ 2186 #undef __FUNCT__ 2187 #define __FUNCT__ "MatMult" 2188 /*@ 2189 MatMult - Computes the matrix-vector product, y = Ax. 2190 2191 Neighbor-wise Collective on Mat and Vec 2192 2193 Input Parameters: 2194 + mat - the matrix 2195 - x - the vector to be multiplied 2196 2197 Output Parameters: 2198 . y - the result 2199 2200 Notes: 2201 The vectors x and y cannot be the same. I.e., one cannot 2202 call MatMult(A,y,y). 2203 2204 Level: beginner 2205 2206 Concepts: matrix-vector product 2207 2208 .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 2209 @*/ 2210 PetscErrorCode MatMult(Mat mat,Vec x,Vec y) 2211 { 2212 PetscErrorCode ierr; 2213 2214 PetscFunctionBegin; 2215 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2216 PetscValidType(mat,1); 2217 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2218 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2219 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2220 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2221 if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2222 #if !defined(PETSC_HAVE_CONSTRAINTS) 2223 if (mat->cmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 2224 if (mat->rmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N); 2225 if (mat->rmap->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->rmap->n,y->map->n); 2226 #endif 2227 ierr = VecValidValues(x,2,PETSC_TRUE);CHKERRQ(ierr); 2228 MatCheckPreallocated(mat,1); 2229 2230 if (!mat->ops->mult) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply defined"); 2231 ierr = PetscLogEventBegin(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); 2232 ierr = (*mat->ops->mult)(mat,x,y);CHKERRQ(ierr); 2233 ierr = PetscLogEventEnd(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); 2234 ierr = VecValidValues(y,3,PETSC_FALSE);CHKERRQ(ierr); 2235 PetscFunctionReturn(0); 2236 } 2237 2238 #undef __FUNCT__ 2239 #define __FUNCT__ "MatMultTranspose" 2240 /*@ 2241 MatMultTranspose - Computes matrix transpose times a vector. 2242 2243 Neighbor-wise Collective on Mat and Vec 2244 2245 Input Parameters: 2246 + mat - the matrix 2247 - x - the vector to be multilplied 2248 2249 Output Parameters: 2250 . y - the result 2251 2252 Notes: 2253 The vectors x and y cannot be the same. I.e., one cannot 2254 call MatMultTranspose(A,y,y). 2255 2256 For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple, 2257 use MatMultHermitianTranspose() 2258 2259 Level: beginner 2260 2261 Concepts: matrix vector product^transpose 2262 2263 .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd(), MatMultHermitianTranspose(), MatTranspose() 2264 @*/ 2265 PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y) 2266 { 2267 PetscErrorCode ierr; 2268 2269 PetscFunctionBegin; 2270 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2271 PetscValidType(mat,1); 2272 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2273 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2274 2275 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2276 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2277 if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2278 #if !defined(PETSC_HAVE_CONSTRAINTS) 2279 if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); 2280 if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N); 2281 #endif 2282 ierr = VecValidValues(x,2,PETSC_TRUE);CHKERRQ(ierr); 2283 MatCheckPreallocated(mat,1); 2284 2285 if (!mat->ops->multtranspose) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a multiply tranpose defined"); 2286 ierr = PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); 2287 ierr = (*mat->ops->multtranspose)(mat,x,y);CHKERRQ(ierr); 2288 ierr = PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); 2289 ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); 2290 ierr = VecValidValues(y,3,PETSC_FALSE);CHKERRQ(ierr); 2291 PetscFunctionReturn(0); 2292 } 2293 2294 #undef __FUNCT__ 2295 #define __FUNCT__ "MatMultHermitianTranspose" 2296 /*@ 2297 MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector. 2298 2299 Neighbor-wise Collective on Mat and Vec 2300 2301 Input Parameters: 2302 + mat - the matrix 2303 - x - the vector to be multilplied 2304 2305 Output Parameters: 2306 . y - the result 2307 2308 Notes: 2309 The vectors x and y cannot be the same. I.e., one cannot 2310 call MatMultHermitianTranspose(A,y,y). 2311 2312 Also called the conjugate transpose, complex conjugate transpose, or adjoint. 2313 2314 For real numbers MatMultTranspose() and MatMultHermitianTranspose() are identical. 2315 2316 Level: beginner 2317 2318 Concepts: matrix vector product^transpose 2319 2320 .seealso: MatMult(), MatMultAdd(), MatMultHermitianTransposeAdd(), MatMultTranspose() 2321 @*/ 2322 PetscErrorCode MatMultHermitianTranspose(Mat mat,Vec x,Vec y) 2323 { 2324 PetscErrorCode ierr; 2325 Vec w; 2326 2327 PetscFunctionBegin; 2328 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2329 PetscValidType(mat,1); 2330 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2331 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2332 2333 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2334 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2335 if (x == y) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2336 #if !defined(PETSC_HAVE_CONSTRAINTS) 2337 if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); 2338 if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N); 2339 #endif 2340 MatCheckPreallocated(mat,1); 2341 2342 ierr = PetscLogEventBegin(MAT_MultHermitianTranspose,mat,x,y,0);CHKERRQ(ierr); 2343 if (mat->ops->multhermitiantranspose) { 2344 ierr = (*mat->ops->multhermitiantranspose)(mat,x,y);CHKERRQ(ierr); 2345 } else { 2346 ierr = VecDuplicate(x,&w);CHKERRQ(ierr); 2347 ierr = VecCopy(x,w);CHKERRQ(ierr); 2348 ierr = VecConjugate(w);CHKERRQ(ierr); 2349 ierr = MatMultTranspose(mat,w,y);CHKERRQ(ierr); 2350 ierr = VecDestroy(&w);CHKERRQ(ierr); 2351 ierr = VecConjugate(y);CHKERRQ(ierr); 2352 } 2353 ierr = PetscLogEventEnd(MAT_MultHermitianTranspose,mat,x,y,0);CHKERRQ(ierr); 2354 ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); 2355 PetscFunctionReturn(0); 2356 } 2357 2358 #undef __FUNCT__ 2359 #define __FUNCT__ "MatMultAdd" 2360 /*@ 2361 MatMultAdd - Computes v3 = v2 + A * v1. 2362 2363 Neighbor-wise Collective on Mat and Vec 2364 2365 Input Parameters: 2366 + mat - the matrix 2367 - v1, v2 - the vectors 2368 2369 Output Parameters: 2370 . v3 - the result 2371 2372 Notes: 2373 The vectors v1 and v3 cannot be the same. I.e., one cannot 2374 call MatMultAdd(A,v1,v2,v1). 2375 2376 Level: beginner 2377 2378 Concepts: matrix vector product^addition 2379 2380 .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd() 2381 @*/ 2382 PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3) 2383 { 2384 PetscErrorCode ierr; 2385 2386 PetscFunctionBegin; 2387 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2388 PetscValidType(mat,1); 2389 PetscValidHeaderSpecific(v1,VEC_CLASSID,2); 2390 PetscValidHeaderSpecific(v2,VEC_CLASSID,3); 2391 PetscValidHeaderSpecific(v3,VEC_CLASSID,4); 2392 2393 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2394 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2395 if (mat->cmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->cmap->N,v1->map->N); 2396 /* if (mat->rmap->N != v2->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->rmap->N,v2->map->N); 2397 if (mat->rmap->N != v3->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->rmap->N,v3->map->N); */ 2398 if (mat->rmap->n != v3->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: local dim %D %D",mat->rmap->n,v3->map->n); 2399 if (mat->rmap->n != v2->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: local dim %D %D",mat->rmap->n,v2->map->n); 2400 if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 2401 MatCheckPreallocated(mat,1); 2402 2403 if (!mat->ops->multadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No MatMultAdd() for matrix type '%s'",((PetscObject)mat)->type_name); 2404 ierr = PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2405 ierr = (*mat->ops->multadd)(mat,v1,v2,v3);CHKERRQ(ierr); 2406 ierr = PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2407 ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); 2408 PetscFunctionReturn(0); 2409 } 2410 2411 #undef __FUNCT__ 2412 #define __FUNCT__ "MatMultTransposeAdd" 2413 /*@ 2414 MatMultTransposeAdd - Computes v3 = v2 + A' * v1. 2415 2416 Neighbor-wise Collective on Mat and Vec 2417 2418 Input Parameters: 2419 + mat - the matrix 2420 - v1, v2 - the vectors 2421 2422 Output Parameters: 2423 . v3 - the result 2424 2425 Notes: 2426 The vectors v1 and v3 cannot be the same. I.e., one cannot 2427 call MatMultTransposeAdd(A,v1,v2,v1). 2428 2429 Level: beginner 2430 2431 Concepts: matrix vector product^transpose and addition 2432 2433 .seealso: MatMultTranspose(), MatMultAdd(), MatMult() 2434 @*/ 2435 PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3) 2436 { 2437 PetscErrorCode ierr; 2438 2439 PetscFunctionBegin; 2440 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2441 PetscValidType(mat,1); 2442 PetscValidHeaderSpecific(v1,VEC_CLASSID,2); 2443 PetscValidHeaderSpecific(v2,VEC_CLASSID,3); 2444 PetscValidHeaderSpecific(v3,VEC_CLASSID,4); 2445 2446 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2447 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2448 if (!mat->ops->multtransposeadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2449 if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 2450 if (mat->rmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N); 2451 if (mat->cmap->N != v2->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N); 2452 if (mat->cmap->N != v3->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N); 2453 MatCheckPreallocated(mat,1); 2454 2455 ierr = PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2456 ierr = (*mat->ops->multtransposeadd)(mat,v1,v2,v3);CHKERRQ(ierr); 2457 ierr = PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2458 ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); 2459 PetscFunctionReturn(0); 2460 } 2461 2462 #undef __FUNCT__ 2463 #define __FUNCT__ "MatMultHermitianTransposeAdd" 2464 /*@ 2465 MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1. 2466 2467 Neighbor-wise Collective on Mat and Vec 2468 2469 Input Parameters: 2470 + mat - the matrix 2471 - v1, v2 - the vectors 2472 2473 Output Parameters: 2474 . v3 - the result 2475 2476 Notes: 2477 The vectors v1 and v3 cannot be the same. I.e., one cannot 2478 call MatMultHermitianTransposeAdd(A,v1,v2,v1). 2479 2480 Level: beginner 2481 2482 Concepts: matrix vector product^transpose and addition 2483 2484 .seealso: MatMultHermitianTranspose(), MatMultTranspose(), MatMultAdd(), MatMult() 2485 @*/ 2486 PetscErrorCode MatMultHermitianTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3) 2487 { 2488 PetscErrorCode ierr; 2489 2490 PetscFunctionBegin; 2491 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2492 PetscValidType(mat,1); 2493 PetscValidHeaderSpecific(v1,VEC_CLASSID,2); 2494 PetscValidHeaderSpecific(v2,VEC_CLASSID,3); 2495 PetscValidHeaderSpecific(v3,VEC_CLASSID,4); 2496 2497 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2498 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2499 if (!mat->ops->multhermitiantransposeadd) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2500 if (v1 == v3) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 2501 if (mat->rmap->N != v1->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->rmap->N,v1->map->N); 2502 if (mat->cmap->N != v2->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->cmap->N,v2->map->N); 2503 if (mat->cmap->N != v3->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->cmap->N,v3->map->N); 2504 MatCheckPreallocated(mat,1); 2505 2506 ierr = PetscLogEventBegin(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2507 ierr = (*mat->ops->multhermitiantransposeadd)(mat,v1,v2,v3);CHKERRQ(ierr); 2508 ierr = PetscLogEventEnd(MAT_MultHermitianTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 2509 ierr = PetscObjectStateIncrease((PetscObject)v3);CHKERRQ(ierr); 2510 PetscFunctionReturn(0); 2511 } 2512 2513 #undef __FUNCT__ 2514 #define __FUNCT__ "MatMultConstrained" 2515 /*@ 2516 MatMultConstrained - The inner multiplication routine for a 2517 constrained matrix P^T A P. 2518 2519 Neighbor-wise Collective on Mat and Vec 2520 2521 Input Parameters: 2522 + mat - the matrix 2523 - x - the vector to be multilplied 2524 2525 Output Parameters: 2526 . y - the result 2527 2528 Notes: 2529 The vectors x and y cannot be the same. I.e., one cannot 2530 call MatMult(A,y,y). 2531 2532 Level: beginner 2533 2534 .keywords: matrix, multiply, matrix-vector product, constraint 2535 .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 2536 @*/ 2537 PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y) 2538 { 2539 PetscErrorCode ierr; 2540 2541 PetscFunctionBegin; 2542 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2543 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2544 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2545 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2546 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2547 if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2548 if (mat->cmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 2549 if (mat->rmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N); 2550 if (mat->rmap->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->rmap->n,y->map->n); 2551 2552 ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 2553 ierr = (*mat->ops->multconstrained)(mat,x,y);CHKERRQ(ierr); 2554 ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 2555 ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); 2556 PetscFunctionReturn(0); 2557 } 2558 2559 #undef __FUNCT__ 2560 #define __FUNCT__ "MatMultTransposeConstrained" 2561 /*@ 2562 MatMultTransposeConstrained - The inner multiplication routine for a 2563 constrained matrix P^T A^T P. 2564 2565 Neighbor-wise Collective on Mat and Vec 2566 2567 Input Parameters: 2568 + mat - the matrix 2569 - x - the vector to be multilplied 2570 2571 Output Parameters: 2572 . y - the result 2573 2574 Notes: 2575 The vectors x and y cannot be the same. I.e., one cannot 2576 call MatMult(A,y,y). 2577 2578 Level: beginner 2579 2580 .keywords: matrix, multiply, matrix-vector product, constraint 2581 .seealso: MatMult(), MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 2582 @*/ 2583 PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y) 2584 { 2585 PetscErrorCode ierr; 2586 2587 PetscFunctionBegin; 2588 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2589 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 2590 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 2591 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2592 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2593 if (x == y) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 2594 if (mat->rmap->N != x->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 2595 if (mat->cmap->N != y->map->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N); 2596 2597 ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 2598 ierr = (*mat->ops->multtransposeconstrained)(mat,x,y);CHKERRQ(ierr); 2599 ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 2600 ierr = PetscObjectStateIncrease((PetscObject)y);CHKERRQ(ierr); 2601 PetscFunctionReturn(0); 2602 } 2603 2604 #undef __FUNCT__ 2605 #define __FUNCT__ "MatGetFactorType" 2606 /*@C 2607 MatGetFactorType - gets the type of factorization it is 2608 2609 Note Collective 2610 as the flag 2611 2612 Input Parameters: 2613 . mat - the matrix 2614 2615 Output Parameters: 2616 . t - the type, one of MAT_FACTOR_NONE, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ILU, MAT_FACTOR_ICC,MAT_FACTOR_ILUDT 2617 2618 Level: intermediate 2619 2620 .seealso: MatFactorType, MatGetFactor() 2621 @*/ 2622 PetscErrorCode MatGetFactorType(Mat mat,MatFactorType *t) 2623 { 2624 PetscFunctionBegin; 2625 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2626 PetscValidType(mat,1); 2627 *t = mat->factortype; 2628 PetscFunctionReturn(0); 2629 } 2630 2631 /* ------------------------------------------------------------*/ 2632 #undef __FUNCT__ 2633 #define __FUNCT__ "MatGetInfo" 2634 /*@C 2635 MatGetInfo - Returns information about matrix storage (number of 2636 nonzeros, memory, etc.). 2637 2638 Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used as the flag 2639 2640 Input Parameters: 2641 . mat - the matrix 2642 2643 Output Parameters: 2644 + flag - flag indicating the type of parameters to be returned 2645 (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors, 2646 MAT_GLOBAL_SUM - sum over all processors) 2647 - info - matrix information context 2648 2649 Notes: 2650 The MatInfo context contains a variety of matrix data, including 2651 number of nonzeros allocated and used, number of mallocs during 2652 matrix assembly, etc. Additional information for factored matrices 2653 is provided (such as the fill ratio, number of mallocs during 2654 factorization, etc.). Much of this info is printed to PETSC_STDOUT 2655 when using the runtime options 2656 $ -info -mat_view ::ascii_info 2657 2658 Example for C/C++ Users: 2659 See the file ${PETSC_DIR}/include/petscmat.h for a complete list of 2660 data within the MatInfo context. For example, 2661 .vb 2662 MatInfo info; 2663 Mat A; 2664 double mal, nz_a, nz_u; 2665 2666 MatGetInfo(A,MAT_LOCAL,&info); 2667 mal = info.mallocs; 2668 nz_a = info.nz_allocated; 2669 .ve 2670 2671 Example for Fortran Users: 2672 Fortran users should declare info as a double precision 2673 array of dimension MAT_INFO_SIZE, and then extract the parameters 2674 of interest. See the file ${PETSC_DIR}/include/finclude/petscmat.h 2675 a complete list of parameter names. 2676 .vb 2677 double precision info(MAT_INFO_SIZE) 2678 double precision mal, nz_a 2679 Mat A 2680 integer ierr 2681 2682 call MatGetInfo(A,MAT_LOCAL,info,ierr) 2683 mal = info(MAT_INFO_MALLOCS) 2684 nz_a = info(MAT_INFO_NZ_ALLOCATED) 2685 .ve 2686 2687 Level: intermediate 2688 2689 Concepts: matrices^getting information on 2690 2691 Developer Note: fortran interface is not autogenerated as the f90 2692 interface defintion cannot be generated correctly [due to MatInfo] 2693 2694 .seealso: MatStashGetInfo() 2695 2696 @*/ 2697 PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info) 2698 { 2699 PetscErrorCode ierr; 2700 2701 PetscFunctionBegin; 2702 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2703 PetscValidType(mat,1); 2704 PetscValidPointer(info,3); 2705 if (!mat->ops->getinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2706 MatCheckPreallocated(mat,1); 2707 ierr = (*mat->ops->getinfo)(mat,flag,info);CHKERRQ(ierr); 2708 PetscFunctionReturn(0); 2709 } 2710 2711 /* ----------------------------------------------------------*/ 2712 2713 #undef __FUNCT__ 2714 #define __FUNCT__ "MatLUFactor" 2715 /*@C 2716 MatLUFactor - Performs in-place LU factorization of matrix. 2717 2718 Collective on Mat 2719 2720 Input Parameters: 2721 + mat - the matrix 2722 . row - row permutation 2723 . col - column permutation 2724 - info - options for factorization, includes 2725 $ fill - expected fill as ratio of original fill. 2726 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 2727 $ Run with the option -info to determine an optimal value to use 2728 2729 Notes: 2730 Most users should employ the simplified KSP interface for linear solvers 2731 instead of working directly with matrix algebra routines such as this. 2732 See, e.g., KSPCreate(). 2733 2734 This changes the state of the matrix to a factored matrix; it cannot be used 2735 for example with MatSetValues() unless one first calls MatSetUnfactored(). 2736 2737 Level: developer 2738 2739 Concepts: matrices^LU factorization 2740 2741 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), 2742 MatGetOrdering(), MatSetUnfactored(), MatFactorInfo, MatGetFactor() 2743 2744 Developer Note: fortran interface is not autogenerated as the f90 2745 interface defintion cannot be generated correctly [due to MatFactorInfo] 2746 2747 @*/ 2748 PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info) 2749 { 2750 PetscErrorCode ierr; 2751 MatFactorInfo tinfo; 2752 2753 PetscFunctionBegin; 2754 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2755 if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); 2756 if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); 2757 if (info) PetscValidPointer(info,4); 2758 PetscValidType(mat,1); 2759 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2760 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2761 if (!mat->ops->lufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2762 MatCheckPreallocated(mat,1); 2763 if (!info) { 2764 ierr = MatFactorInfoInitialize(&tinfo);CHKERRQ(ierr); 2765 info = &tinfo; 2766 } 2767 2768 ierr = PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); 2769 ierr = (*mat->ops->lufactor)(mat,row,col,info);CHKERRQ(ierr); 2770 ierr = PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); 2771 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 2772 PetscFunctionReturn(0); 2773 } 2774 2775 #undef __FUNCT__ 2776 #define __FUNCT__ "MatILUFactor" 2777 /*@C 2778 MatILUFactor - Performs in-place ILU factorization of matrix. 2779 2780 Collective on Mat 2781 2782 Input Parameters: 2783 + mat - the matrix 2784 . row - row permutation 2785 . col - column permutation 2786 - info - structure containing 2787 $ levels - number of levels of fill. 2788 $ expected fill - as ratio of original fill. 2789 $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices 2790 missing diagonal entries) 2791 2792 Notes: 2793 Probably really in-place only when level of fill is zero, otherwise allocates 2794 new space to store factored matrix and deletes previous memory. 2795 2796 Most users should employ the simplified KSP interface for linear solvers 2797 instead of working directly with matrix algebra routines such as this. 2798 See, e.g., KSPCreate(). 2799 2800 Level: developer 2801 2802 Concepts: matrices^ILU factorization 2803 2804 .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 2805 2806 Developer Note: fortran interface is not autogenerated as the f90 2807 interface defintion cannot be generated correctly [due to MatFactorInfo] 2808 2809 @*/ 2810 PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,const MatFactorInfo *info) 2811 { 2812 PetscErrorCode ierr; 2813 2814 PetscFunctionBegin; 2815 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2816 if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); 2817 if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); 2818 PetscValidPointer(info,4); 2819 PetscValidType(mat,1); 2820 if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square"); 2821 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2822 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2823 if (!mat->ops->ilufactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2824 MatCheckPreallocated(mat,1); 2825 2826 ierr = PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 2827 ierr = (*mat->ops->ilufactor)(mat,row,col,info);CHKERRQ(ierr); 2828 ierr = PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 2829 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 2830 PetscFunctionReturn(0); 2831 } 2832 2833 #undef __FUNCT__ 2834 #define __FUNCT__ "MatLUFactorSymbolic" 2835 /*@C 2836 MatLUFactorSymbolic - Performs symbolic LU factorization of matrix. 2837 Call this routine before calling MatLUFactorNumeric(). 2838 2839 Collective on Mat 2840 2841 Input Parameters: 2842 + fact - the factor matrix obtained with MatGetFactor() 2843 . mat - the matrix 2844 . row, col - row and column permutations 2845 - info - options for factorization, includes 2846 $ fill - expected fill as ratio of original fill. 2847 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 2848 $ Run with the option -info to determine an optimal value to use 2849 2850 2851 Notes: 2852 See the <a href="../../docs/manual.pdf">users manual</a> for additional information about 2853 choosing the fill factor for better efficiency. 2854 2855 Most users should employ the simplified KSP interface for linear solvers 2856 instead of working directly with matrix algebra routines such as this. 2857 See, e.g., KSPCreate(). 2858 2859 Level: developer 2860 2861 Concepts: matrices^LU symbolic factorization 2862 2863 .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 2864 2865 Developer Note: fortran interface is not autogenerated as the f90 2866 interface defintion cannot be generated correctly [due to MatFactorInfo] 2867 2868 @*/ 2869 PetscErrorCode MatLUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info) 2870 { 2871 PetscErrorCode ierr; 2872 2873 PetscFunctionBegin; 2874 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2875 if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); 2876 if (col) PetscValidHeaderSpecific(col,IS_CLASSID,3); 2877 if (info) PetscValidPointer(info,4); 2878 PetscValidType(mat,1); 2879 PetscValidPointer(fact,5); 2880 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2881 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2882 if (!(fact)->ops->lufactorsymbolic) { 2883 const MatSolverPackage spackage; 2884 ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); 2885 SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic LU using solver package %s",((PetscObject)mat)->type_name,spackage); 2886 } 2887 MatCheckPreallocated(mat,2); 2888 2889 ierr = PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 2890 ierr = (fact->ops->lufactorsymbolic)(fact,mat,row,col,info);CHKERRQ(ierr); 2891 ierr = PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 2892 ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); 2893 PetscFunctionReturn(0); 2894 } 2895 2896 #undef __FUNCT__ 2897 #define __FUNCT__ "MatLUFactorNumeric" 2898 /*@C 2899 MatLUFactorNumeric - Performs numeric LU factorization of a matrix. 2900 Call this routine after first calling MatLUFactorSymbolic(). 2901 2902 Collective on Mat 2903 2904 Input Parameters: 2905 + fact - the factor matrix obtained with MatGetFactor() 2906 . mat - the matrix 2907 - info - options for factorization 2908 2909 Notes: 2910 See MatLUFactor() for in-place factorization. See 2911 MatCholeskyFactorNumeric() for the symmetric, positive definite case. 2912 2913 Most users should employ the simplified KSP interface for linear solvers 2914 instead of working directly with matrix algebra routines such as this. 2915 See, e.g., KSPCreate(). 2916 2917 Level: developer 2918 2919 Concepts: matrices^LU numeric factorization 2920 2921 .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor() 2922 2923 Developer Note: fortran interface is not autogenerated as the f90 2924 interface defintion cannot be generated correctly [due to MatFactorInfo] 2925 2926 @*/ 2927 PetscErrorCode MatLUFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info) 2928 { 2929 PetscErrorCode ierr; 2930 2931 PetscFunctionBegin; 2932 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2933 PetscValidType(mat,1); 2934 PetscValidPointer(fact,2); 2935 PetscValidHeaderSpecific(fact,MAT_CLASSID,2); 2936 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2937 if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Mat fact: global dimensions are different %D should = %D %D should = %D",mat->rmap->N,(fact)->rmap->N,mat->cmap->N,(fact)->cmap->N); 2938 2939 if (!(fact)->ops->lufactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric LU",((PetscObject)mat)->type_name); 2940 MatCheckPreallocated(mat,2); 2941 ierr = PetscLogEventBegin(MAT_LUFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); 2942 ierr = (fact->ops->lufactornumeric)(fact,mat,info);CHKERRQ(ierr); 2943 ierr = PetscLogEventEnd(MAT_LUFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); 2944 2945 if (fact->viewonassembly) { 2946 ierr = PetscViewerPushFormat(fact->viewonassembly,fact->viewformatonassembly);CHKERRQ(ierr); 2947 ierr = MatView(fact,fact->viewonassembly);CHKERRQ(ierr); 2948 ierr = PetscViewerPopFormat(fact->viewonassembly);CHKERRQ(ierr); 2949 } 2950 ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); 2951 PetscFunctionReturn(0); 2952 } 2953 2954 #undef __FUNCT__ 2955 #define __FUNCT__ "MatCholeskyFactor" 2956 /*@C 2957 MatCholeskyFactor - Performs in-place Cholesky factorization of a 2958 symmetric matrix. 2959 2960 Collective on Mat 2961 2962 Input Parameters: 2963 + mat - the matrix 2964 . perm - row and column permutations 2965 - f - expected fill as ratio of original fill 2966 2967 Notes: 2968 See MatLUFactor() for the nonsymmetric case. See also 2969 MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric(). 2970 2971 Most users should employ the simplified KSP interface for linear solvers 2972 instead of working directly with matrix algebra routines such as this. 2973 See, e.g., KSPCreate(). 2974 2975 Level: developer 2976 2977 Concepts: matrices^Cholesky factorization 2978 2979 .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric() 2980 MatGetOrdering() 2981 2982 Developer Note: fortran interface is not autogenerated as the f90 2983 interface defintion cannot be generated correctly [due to MatFactorInfo] 2984 2985 @*/ 2986 PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,const MatFactorInfo *info) 2987 { 2988 PetscErrorCode ierr; 2989 2990 PetscFunctionBegin; 2991 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 2992 PetscValidType(mat,1); 2993 if (perm) PetscValidHeaderSpecific(perm,IS_CLASSID,2); 2994 if (info) PetscValidPointer(info,3); 2995 if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square"); 2996 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2997 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2998 if (!mat->ops->choleskyfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 2999 MatCheckPreallocated(mat,1); 3000 3001 ierr = PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); 3002 ierr = (*mat->ops->choleskyfactor)(mat,perm,info);CHKERRQ(ierr); 3003 ierr = PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); 3004 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 3005 PetscFunctionReturn(0); 3006 } 3007 3008 #undef __FUNCT__ 3009 #define __FUNCT__ "MatCholeskyFactorSymbolic" 3010 /*@C 3011 MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization 3012 of a symmetric matrix. 3013 3014 Collective on Mat 3015 3016 Input Parameters: 3017 + fact - the factor matrix obtained with MatGetFactor() 3018 . mat - the matrix 3019 . perm - row and column permutations 3020 - info - options for factorization, includes 3021 $ fill - expected fill as ratio of original fill. 3022 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 3023 $ Run with the option -info to determine an optimal value to use 3024 3025 Notes: 3026 See MatLUFactorSymbolic() for the nonsymmetric case. See also 3027 MatCholeskyFactor() and MatCholeskyFactorNumeric(). 3028 3029 Most users should employ the simplified KSP interface for linear solvers 3030 instead of working directly with matrix algebra routines such as this. 3031 See, e.g., KSPCreate(). 3032 3033 Level: developer 3034 3035 Concepts: matrices^Cholesky symbolic factorization 3036 3037 .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric() 3038 MatGetOrdering() 3039 3040 Developer Note: fortran interface is not autogenerated as the f90 3041 interface defintion cannot be generated correctly [due to MatFactorInfo] 3042 3043 @*/ 3044 PetscErrorCode MatCholeskyFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info) 3045 { 3046 PetscErrorCode ierr; 3047 3048 PetscFunctionBegin; 3049 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3050 PetscValidType(mat,1); 3051 if (perm) PetscValidHeaderSpecific(perm,IS_CLASSID,2); 3052 if (info) PetscValidPointer(info,3); 3053 PetscValidPointer(fact,4); 3054 if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"Matrix must be square"); 3055 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3056 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3057 if (!(fact)->ops->choleskyfactorsymbolic) { 3058 const MatSolverPackage spackage; 3059 ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); 3060 SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s symbolic factor Cholesky using solver package %s",((PetscObject)mat)->type_name,spackage); 3061 } 3062 MatCheckPreallocated(mat,2); 3063 3064 ierr = PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 3065 ierr = (fact->ops->choleskyfactorsymbolic)(fact,mat,perm,info);CHKERRQ(ierr); 3066 ierr = PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 3067 ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); 3068 PetscFunctionReturn(0); 3069 } 3070 3071 #undef __FUNCT__ 3072 #define __FUNCT__ "MatCholeskyFactorNumeric" 3073 /*@C 3074 MatCholeskyFactorNumeric - Performs numeric Cholesky factorization 3075 of a symmetric matrix. Call this routine after first calling 3076 MatCholeskyFactorSymbolic(). 3077 3078 Collective on Mat 3079 3080 Input Parameters: 3081 + fact - the factor matrix obtained with MatGetFactor() 3082 . mat - the initial matrix 3083 . info - options for factorization 3084 - fact - the symbolic factor of mat 3085 3086 3087 Notes: 3088 Most users should employ the simplified KSP interface for linear solvers 3089 instead of working directly with matrix algebra routines such as this. 3090 See, e.g., KSPCreate(). 3091 3092 Level: developer 3093 3094 Concepts: matrices^Cholesky numeric factorization 3095 3096 .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric() 3097 3098 Developer Note: fortran interface is not autogenerated as the f90 3099 interface defintion cannot be generated correctly [due to MatFactorInfo] 3100 3101 @*/ 3102 PetscErrorCode MatCholeskyFactorNumeric(Mat fact,Mat mat,const MatFactorInfo *info) 3103 { 3104 PetscErrorCode ierr; 3105 3106 PetscFunctionBegin; 3107 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3108 PetscValidType(mat,1); 3109 PetscValidPointer(fact,2); 3110 PetscValidHeaderSpecific(fact,MAT_CLASSID,2); 3111 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3112 if (!(fact)->ops->choleskyfactornumeric) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s numeric factor Cholesky",((PetscObject)mat)->type_name); 3113 if (mat->rmap->N != (fact)->rmap->N || mat->cmap->N != (fact)->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Mat fact: global dim %D should = %D %D should = %D",mat->rmap->N,(fact)->rmap->N,mat->cmap->N,(fact)->cmap->N); 3114 MatCheckPreallocated(mat,2); 3115 3116 ierr = PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); 3117 ierr = (fact->ops->choleskyfactornumeric)(fact,mat,info);CHKERRQ(ierr); 3118 ierr = PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,fact,0,0);CHKERRQ(ierr); 3119 3120 if (fact->viewonassembly) { 3121 ierr = PetscViewerPushFormat(fact->viewonassembly,fact->viewformatonassembly);CHKERRQ(ierr); 3122 ierr = MatView(fact,fact->viewonassembly);CHKERRQ(ierr); 3123 ierr = PetscViewerPopFormat(fact->viewonassembly);CHKERRQ(ierr); 3124 } 3125 ierr = PetscObjectStateIncrease((PetscObject)fact);CHKERRQ(ierr); 3126 PetscFunctionReturn(0); 3127 } 3128 3129 /* ----------------------------------------------------------------*/ 3130 #undef __FUNCT__ 3131 #define __FUNCT__ "MatSolve" 3132 /*@ 3133 MatSolve - Solves A x = b, given a factored matrix. 3134 3135 Neighbor-wise Collective on Mat and Vec 3136 3137 Input Parameters: 3138 + mat - the factored matrix 3139 - b - the right-hand-side vector 3140 3141 Output Parameter: 3142 . x - the result vector 3143 3144 Notes: 3145 The vectors b and x cannot be the same. I.e., one cannot 3146 call MatSolve(A,x,x). 3147 3148 Notes: 3149 Most users should employ the simplified KSP interface for linear solvers 3150 instead of working directly with matrix algebra routines such as this. 3151 See, e.g., KSPCreate(). 3152 3153 Level: developer 3154 3155 Concepts: matrices^triangular solves 3156 3157 .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd() 3158 @*/ 3159 PetscErrorCode MatSolve(Mat mat,Vec b,Vec x) 3160 { 3161 PetscErrorCode ierr; 3162 3163 PetscFunctionBegin; 3164 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3165 PetscValidType(mat,1); 3166 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 3167 PetscValidHeaderSpecific(x,VEC_CLASSID,3); 3168 PetscCheckSameComm(mat,1,b,2); 3169 PetscCheckSameComm(mat,1,x,3); 3170 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3171 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3172 if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 3173 if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 3174 if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n); 3175 if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(0); 3176 if (!mat->ops->solve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 3177 MatCheckPreallocated(mat,1); 3178 3179 ierr = PetscLogEventBegin(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); 3180 ierr = (*mat->ops->solve)(mat,b,x);CHKERRQ(ierr); 3181 ierr = PetscLogEventEnd(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); 3182 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3183 PetscFunctionReturn(0); 3184 } 3185 3186 #undef __FUNCT__ 3187 #define __FUNCT__ "MatMatSolve_Basic" 3188 PetscErrorCode MatMatSolve_Basic(Mat A,Mat B,Mat X) 3189 { 3190 PetscErrorCode ierr; 3191 Vec b,x; 3192 PetscInt m,N,i; 3193 PetscScalar *bb,*xx; 3194 PetscBool flg; 3195 3196 PetscFunctionBegin; 3197 ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 3198 if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix"); 3199 ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr); 3200 if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); 3201 3202 ierr = MatDenseGetArray(B,&bb);CHKERRQ(ierr); 3203 ierr = MatDenseGetArray(X,&xx);CHKERRQ(ierr); 3204 ierr = MatGetLocalSize(B,&m,NULL);CHKERRQ(ierr); /* number local rows */ 3205 ierr = MatGetSize(B,NULL,&N);CHKERRQ(ierr); /* total columns in dense matrix */ 3206 ierr = MatGetVecs(A,&x,&b);CHKERRQ(ierr); 3207 for (i=0; i<N; i++) { 3208 ierr = VecPlaceArray(b,bb + i*m);CHKERRQ(ierr); 3209 ierr = VecPlaceArray(x,xx + i*m);CHKERRQ(ierr); 3210 ierr = MatSolve(A,b,x);CHKERRQ(ierr); 3211 ierr = VecResetArray(x);CHKERRQ(ierr); 3212 ierr = VecResetArray(b);CHKERRQ(ierr); 3213 } 3214 ierr = VecDestroy(&b);CHKERRQ(ierr); 3215 ierr = VecDestroy(&x);CHKERRQ(ierr); 3216 ierr = MatDenseRestoreArray(B,&bb);CHKERRQ(ierr); 3217 ierr = MatDenseRestoreArray(X,&xx);CHKERRQ(ierr); 3218 PetscFunctionReturn(0); 3219 } 3220 3221 #undef __FUNCT__ 3222 #define __FUNCT__ "MatMatSolve" 3223 /*@ 3224 MatMatSolve - Solves A X = B, given a factored matrix. 3225 3226 Neighbor-wise Collective on Mat 3227 3228 Input Parameters: 3229 + mat - the factored matrix 3230 - B - the right-hand-side matrix (dense matrix) 3231 3232 Output Parameter: 3233 . X - the result matrix (dense matrix) 3234 3235 Notes: 3236 The matrices b and x cannot be the same. I.e., one cannot 3237 call MatMatSolve(A,x,x). 3238 3239 Notes: 3240 Most users should usually employ the simplified KSP interface for linear solvers 3241 instead of working directly with matrix algebra routines such as this. 3242 See, e.g., KSPCreate(). However KSP can only solve for one vector (column of X) 3243 at a time. 3244 3245 When using SuperLU_Dist as a parallel solver PETSc will use the SuperLU_Dist functionality to solve multiple right hand sides simultaneously. For MUMPS 3246 it calls a separate solve for each right hand side since MUMPS does not yet support distributed right hand sides. 3247 3248 Since the resulting matrix X must always be dense we do not support sparse representation of the matrix B. 3249 3250 Level: developer 3251 3252 Concepts: matrices^triangular solves 3253 3254 .seealso: MatMatSolveAdd(), MatMatSolveTranspose(), MatMatSolveTransposeAdd(), MatLUFactor(), MatCholeskyFactor() 3255 @*/ 3256 PetscErrorCode MatMatSolve(Mat A,Mat B,Mat X) 3257 { 3258 PetscErrorCode ierr; 3259 3260 PetscFunctionBegin; 3261 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 3262 PetscValidType(A,1); 3263 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 3264 PetscValidHeaderSpecific(X,MAT_CLASSID,3); 3265 PetscCheckSameComm(A,1,B,2); 3266 PetscCheckSameComm(A,1,X,3); 3267 if (X == B) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_IDN,"X and B must be different matrices"); 3268 if (!A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3269 if (A->cmap->N != X->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat X: global dim %D %D",A->cmap->N,X->rmap->N); 3270 if (A->rmap->N != B->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D",A->rmap->N,B->rmap->N); 3271 if (A->rmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat A,Mat B: local dim %D %D",A->rmap->n,B->rmap->n); 3272 if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(0); 3273 MatCheckPreallocated(A,1); 3274 3275 ierr = PetscLogEventBegin(MAT_MatSolve,A,B,X,0);CHKERRQ(ierr); 3276 if (!A->ops->matsolve) { 3277 ierr = PetscInfo1(A,"Mat type %s using basic MatMatSolve",((PetscObject)A)->type_name);CHKERRQ(ierr); 3278 ierr = MatMatSolve_Basic(A,B,X);CHKERRQ(ierr); 3279 } else { 3280 ierr = (*A->ops->matsolve)(A,B,X);CHKERRQ(ierr); 3281 } 3282 ierr = PetscLogEventEnd(MAT_MatSolve,A,B,X,0);CHKERRQ(ierr); 3283 ierr = PetscObjectStateIncrease((PetscObject)X);CHKERRQ(ierr); 3284 PetscFunctionReturn(0); 3285 } 3286 3287 3288 #undef __FUNCT__ 3289 #define __FUNCT__ "MatForwardSolve" 3290 /*@ 3291 MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or 3292 U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U, 3293 3294 Neighbor-wise Collective on Mat and Vec 3295 3296 Input Parameters: 3297 + mat - the factored matrix 3298 - b - the right-hand-side vector 3299 3300 Output Parameter: 3301 . x - the result vector 3302 3303 Notes: 3304 MatSolve() should be used for most applications, as it performs 3305 a forward solve followed by a backward solve. 3306 3307 The vectors b and x cannot be the same, i.e., one cannot 3308 call MatForwardSolve(A,x,x). 3309 3310 For matrix in seqsbaij format with block size larger than 1, 3311 the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet. 3312 MatForwardSolve() solves U^T*D y = b, and 3313 MatBackwardSolve() solves U x = y. 3314 Thus they do not provide a symmetric preconditioner. 3315 3316 Most users should employ the simplified KSP interface for linear solvers 3317 instead of working directly with matrix algebra routines such as this. 3318 See, e.g., KSPCreate(). 3319 3320 Level: developer 3321 3322 Concepts: matrices^forward solves 3323 3324 .seealso: MatSolve(), MatBackwardSolve() 3325 @*/ 3326 PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x) 3327 { 3328 PetscErrorCode ierr; 3329 3330 PetscFunctionBegin; 3331 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3332 PetscValidType(mat,1); 3333 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 3334 PetscValidHeaderSpecific(x,VEC_CLASSID,3); 3335 PetscCheckSameComm(mat,1,b,2); 3336 PetscCheckSameComm(mat,1,x,3); 3337 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3338 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3339 if (!mat->ops->forwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 3340 if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 3341 if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 3342 if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n); 3343 MatCheckPreallocated(mat,1); 3344 ierr = PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); 3345 ierr = (*mat->ops->forwardsolve)(mat,b,x);CHKERRQ(ierr); 3346 ierr = PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); 3347 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3348 PetscFunctionReturn(0); 3349 } 3350 3351 #undef __FUNCT__ 3352 #define __FUNCT__ "MatBackwardSolve" 3353 /*@ 3354 MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU. 3355 D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U, 3356 3357 Neighbor-wise Collective on Mat and Vec 3358 3359 Input Parameters: 3360 + mat - the factored matrix 3361 - b - the right-hand-side vector 3362 3363 Output Parameter: 3364 . x - the result vector 3365 3366 Notes: 3367 MatSolve() should be used for most applications, as it performs 3368 a forward solve followed by a backward solve. 3369 3370 The vectors b and x cannot be the same. I.e., one cannot 3371 call MatBackwardSolve(A,x,x). 3372 3373 For matrix in seqsbaij format with block size larger than 1, 3374 the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet. 3375 MatForwardSolve() solves U^T*D y = b, and 3376 MatBackwardSolve() solves U x = y. 3377 Thus they do not provide a symmetric preconditioner. 3378 3379 Most users should employ the simplified KSP interface for linear solvers 3380 instead of working directly with matrix algebra routines such as this. 3381 See, e.g., KSPCreate(). 3382 3383 Level: developer 3384 3385 Concepts: matrices^backward solves 3386 3387 .seealso: MatSolve(), MatForwardSolve() 3388 @*/ 3389 PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x) 3390 { 3391 PetscErrorCode ierr; 3392 3393 PetscFunctionBegin; 3394 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3395 PetscValidType(mat,1); 3396 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 3397 PetscValidHeaderSpecific(x,VEC_CLASSID,3); 3398 PetscCheckSameComm(mat,1,b,2); 3399 PetscCheckSameComm(mat,1,x,3); 3400 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3401 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3402 if (!mat->ops->backwardsolve) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 3403 if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 3404 if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 3405 if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n); 3406 MatCheckPreallocated(mat,1); 3407 3408 ierr = PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); 3409 ierr = (*mat->ops->backwardsolve)(mat,b,x);CHKERRQ(ierr); 3410 ierr = PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); 3411 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3412 PetscFunctionReturn(0); 3413 } 3414 3415 #undef __FUNCT__ 3416 #define __FUNCT__ "MatSolveAdd" 3417 /*@ 3418 MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix. 3419 3420 Neighbor-wise Collective on Mat and Vec 3421 3422 Input Parameters: 3423 + mat - the factored matrix 3424 . b - the right-hand-side vector 3425 - y - the vector to be added to 3426 3427 Output Parameter: 3428 . x - the result vector 3429 3430 Notes: 3431 The vectors b and x cannot be the same. I.e., one cannot 3432 call MatSolveAdd(A,x,y,x). 3433 3434 Most users should employ the simplified KSP interface for linear solvers 3435 instead of working directly with matrix algebra routines such as this. 3436 See, e.g., KSPCreate(). 3437 3438 Level: developer 3439 3440 Concepts: matrices^triangular solves 3441 3442 .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd() 3443 @*/ 3444 PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x) 3445 { 3446 PetscScalar one = 1.0; 3447 Vec tmp; 3448 PetscErrorCode ierr; 3449 3450 PetscFunctionBegin; 3451 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3452 PetscValidType(mat,1); 3453 PetscValidHeaderSpecific(y,VEC_CLASSID,2); 3454 PetscValidHeaderSpecific(b,VEC_CLASSID,3); 3455 PetscValidHeaderSpecific(x,VEC_CLASSID,4); 3456 PetscCheckSameComm(mat,1,b,2); 3457 PetscCheckSameComm(mat,1,y,2); 3458 PetscCheckSameComm(mat,1,x,3); 3459 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3460 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3461 if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 3462 if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 3463 if (mat->rmap->N != y->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->rmap->N,y->map->N); 3464 if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n); 3465 if (x->map->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->map->n,y->map->n); 3466 MatCheckPreallocated(mat,1); 3467 3468 ierr = PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); 3469 if (mat->ops->solveadd) { 3470 ierr = (*mat->ops->solveadd)(mat,b,y,x);CHKERRQ(ierr); 3471 } else { 3472 /* do the solve then the add manually */ 3473 if (x != y) { 3474 ierr = MatSolve(mat,b,x);CHKERRQ(ierr); 3475 ierr = VecAXPY(x,one,y);CHKERRQ(ierr); 3476 } else { 3477 ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); 3478 ierr = PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);CHKERRQ(ierr); 3479 ierr = VecCopy(x,tmp);CHKERRQ(ierr); 3480 ierr = MatSolve(mat,b,x);CHKERRQ(ierr); 3481 ierr = VecAXPY(x,one,tmp);CHKERRQ(ierr); 3482 ierr = VecDestroy(&tmp);CHKERRQ(ierr); 3483 } 3484 } 3485 ierr = PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); 3486 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3487 PetscFunctionReturn(0); 3488 } 3489 3490 #undef __FUNCT__ 3491 #define __FUNCT__ "MatSolveTranspose" 3492 /*@ 3493 MatSolveTranspose - Solves A' x = b, given a factored matrix. 3494 3495 Neighbor-wise Collective on Mat and Vec 3496 3497 Input Parameters: 3498 + mat - the factored matrix 3499 - b - the right-hand-side vector 3500 3501 Output Parameter: 3502 . x - the result vector 3503 3504 Notes: 3505 The vectors b and x cannot be the same. I.e., one cannot 3506 call MatSolveTranspose(A,x,x). 3507 3508 Most users should employ the simplified KSP interface for linear solvers 3509 instead of working directly with matrix algebra routines such as this. 3510 See, e.g., KSPCreate(). 3511 3512 Level: developer 3513 3514 Concepts: matrices^triangular solves 3515 3516 .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd() 3517 @*/ 3518 PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x) 3519 { 3520 PetscErrorCode ierr; 3521 3522 PetscFunctionBegin; 3523 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3524 PetscValidType(mat,1); 3525 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 3526 PetscValidHeaderSpecific(x,VEC_CLASSID,3); 3527 PetscCheckSameComm(mat,1,b,2); 3528 PetscCheckSameComm(mat,1,x,3); 3529 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3530 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3531 if (!mat->ops->solvetranspose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s",((PetscObject)mat)->type_name); 3532 if (mat->rmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); 3533 if (mat->cmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N); 3534 MatCheckPreallocated(mat,1); 3535 ierr = PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); 3536 ierr = (*mat->ops->solvetranspose)(mat,b,x);CHKERRQ(ierr); 3537 ierr = PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); 3538 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3539 PetscFunctionReturn(0); 3540 } 3541 3542 #undef __FUNCT__ 3543 #define __FUNCT__ "MatSolveTransposeAdd" 3544 /*@ 3545 MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a 3546 factored matrix. 3547 3548 Neighbor-wise Collective on Mat and Vec 3549 3550 Input Parameters: 3551 + mat - the factored matrix 3552 . b - the right-hand-side vector 3553 - y - the vector to be added to 3554 3555 Output Parameter: 3556 . x - the result vector 3557 3558 Notes: 3559 The vectors b and x cannot be the same. I.e., one cannot 3560 call MatSolveTransposeAdd(A,x,y,x). 3561 3562 Most users should employ the simplified KSP interface for linear solvers 3563 instead of working directly with matrix algebra routines such as this. 3564 See, e.g., KSPCreate(). 3565 3566 Level: developer 3567 3568 Concepts: matrices^triangular solves 3569 3570 .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose() 3571 @*/ 3572 PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x) 3573 { 3574 PetscScalar one = 1.0; 3575 PetscErrorCode ierr; 3576 Vec tmp; 3577 3578 PetscFunctionBegin; 3579 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3580 PetscValidType(mat,1); 3581 PetscValidHeaderSpecific(y,VEC_CLASSID,2); 3582 PetscValidHeaderSpecific(b,VEC_CLASSID,3); 3583 PetscValidHeaderSpecific(x,VEC_CLASSID,4); 3584 PetscCheckSameComm(mat,1,b,2); 3585 PetscCheckSameComm(mat,1,y,3); 3586 PetscCheckSameComm(mat,1,x,4); 3587 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 3588 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 3589 if (mat->rmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->rmap->N,x->map->N); 3590 if (mat->cmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->cmap->N,b->map->N); 3591 if (mat->cmap->N != y->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->cmap->N,y->map->N); 3592 if (x->map->n != y->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->map->n,y->map->n); 3593 MatCheckPreallocated(mat,1); 3594 3595 ierr = PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); 3596 if (mat->ops->solvetransposeadd) { 3597 ierr = (*mat->ops->solvetransposeadd)(mat,b,y,x);CHKERRQ(ierr); 3598 } else { 3599 /* do the solve then the add manually */ 3600 if (x != y) { 3601 ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); 3602 ierr = VecAXPY(x,one,y);CHKERRQ(ierr); 3603 } else { 3604 ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); 3605 ierr = PetscLogObjectParent((PetscObject)mat,(PetscObject)tmp);CHKERRQ(ierr); 3606 ierr = VecCopy(x,tmp);CHKERRQ(ierr); 3607 ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); 3608 ierr = VecAXPY(x,one,tmp);CHKERRQ(ierr); 3609 ierr = VecDestroy(&tmp);CHKERRQ(ierr); 3610 } 3611 } 3612 ierr = PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); 3613 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3614 PetscFunctionReturn(0); 3615 } 3616 /* ----------------------------------------------------------------*/ 3617 3618 #undef __FUNCT__ 3619 #define __FUNCT__ "MatSOR" 3620 /*@ 3621 MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps. 3622 3623 Neighbor-wise Collective on Mat and Vec 3624 3625 Input Parameters: 3626 + mat - the matrix 3627 . b - the right hand side 3628 . omega - the relaxation factor 3629 . flag - flag indicating the type of SOR (see below) 3630 . shift - diagonal shift 3631 . its - the number of iterations 3632 - lits - the number of local iterations 3633 3634 Output Parameters: 3635 . x - the solution (can contain an initial guess, use option SOR_ZERO_INITIAL_GUESS to indicate no guess) 3636 3637 SOR Flags: 3638 . SOR_FORWARD_SWEEP - forward SOR 3639 . SOR_BACKWARD_SWEEP - backward SOR 3640 . SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR) 3641 . SOR_LOCAL_FORWARD_SWEEP - local forward SOR 3642 . SOR_LOCAL_BACKWARD_SWEEP - local forward SOR 3643 . SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR 3644 . SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies 3645 upper/lower triangular part of matrix to 3646 vector (with omega) 3647 . SOR_ZERO_INITIAL_GUESS - zero initial guess 3648 3649 Notes: 3650 SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and 3651 SOR_LOCAL_SYMMETRIC_SWEEP perform separate independent smoothings 3652 on each processor. 3653 3654 Application programmers will not generally use MatSOR() directly, 3655 but instead will employ the KSP/PC interface. 3656 3657 Notes: for BAIJ, SBAIJ, and AIJ matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing 3658 3659 Notes for Advanced Users: 3660 The flags are implemented as bitwise inclusive or operations. 3661 For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP) 3662 to specify a zero initial guess for SSOR. 3663 3664 Most users should employ the simplified KSP interface for linear solvers 3665 instead of working directly with matrix algebra routines such as this. 3666 See, e.g., KSPCreate(). 3667 3668 Vectors x and b CANNOT be the same 3669 3670 Developer Note: We should add block SOR support for AIJ matrices with block size set to great than one and no inodes 3671 3672 Level: developer 3673 3674 Concepts: matrices^relaxation 3675 Concepts: matrices^SOR 3676 Concepts: matrices^Gauss-Seidel 3677 3678 @*/ 3679 PetscErrorCode MatSOR(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) 3680 { 3681 PetscErrorCode ierr; 3682 3683 PetscFunctionBegin; 3684 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3685 PetscValidType(mat,1); 3686 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 3687 PetscValidHeaderSpecific(x,VEC_CLASSID,8); 3688 PetscCheckSameComm(mat,1,b,2); 3689 PetscCheckSameComm(mat,1,x,8); 3690 if (!mat->ops->sor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 3691 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3692 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3693 if (mat->cmap->N != x->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->cmap->N,x->map->N); 3694 if (mat->rmap->N != b->map->N) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->rmap->N,b->map->N); 3695 if (mat->rmap->n != b->map->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->rmap->n,b->map->n); 3696 if (its <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D positive",its); 3697 if (lits <= 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires local its %D positive",lits); 3698 if (b == x) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_IDN,"b and x vector cannot be the same"); 3699 3700 MatCheckPreallocated(mat,1); 3701 ierr = PetscLogEventBegin(MAT_SOR,mat,b,x,0);CHKERRQ(ierr); 3702 ierr =(*mat->ops->sor)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); 3703 ierr = PetscLogEventEnd(MAT_SOR,mat,b,x,0);CHKERRQ(ierr); 3704 ierr = PetscObjectStateIncrease((PetscObject)x);CHKERRQ(ierr); 3705 PetscFunctionReturn(0); 3706 } 3707 3708 #undef __FUNCT__ 3709 #define __FUNCT__ "MatCopy_Basic" 3710 /* 3711 Default matrix copy routine. 3712 */ 3713 PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str) 3714 { 3715 PetscErrorCode ierr; 3716 PetscInt i,rstart = 0,rend = 0,nz; 3717 const PetscInt *cwork; 3718 const PetscScalar *vwork; 3719 3720 PetscFunctionBegin; 3721 if (B->assembled) { 3722 ierr = MatZeroEntries(B);CHKERRQ(ierr); 3723 } 3724 ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); 3725 for (i=rstart; i<rend; i++) { 3726 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 3727 ierr = MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 3728 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 3729 } 3730 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3731 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3732 ierr = PetscObjectStateIncrease((PetscObject)B);CHKERRQ(ierr); 3733 PetscFunctionReturn(0); 3734 } 3735 3736 #undef __FUNCT__ 3737 #define __FUNCT__ "MatCopy" 3738 /*@ 3739 MatCopy - Copys a matrix to another matrix. 3740 3741 Collective on Mat 3742 3743 Input Parameters: 3744 + A - the matrix 3745 - str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN 3746 3747 Output Parameter: 3748 . B - where the copy is put 3749 3750 Notes: 3751 If you use SAME_NONZERO_PATTERN then the two matrices had better have the 3752 same nonzero pattern or the routine will crash. 3753 3754 MatCopy() copies the matrix entries of a matrix to another existing 3755 matrix (after first zeroing the second matrix). A related routine is 3756 MatConvert(), which first creates a new matrix and then copies the data. 3757 3758 Level: intermediate 3759 3760 Concepts: matrices^copying 3761 3762 .seealso: MatConvert(), MatDuplicate() 3763 3764 @*/ 3765 PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str) 3766 { 3767 PetscErrorCode ierr; 3768 PetscInt i; 3769 3770 PetscFunctionBegin; 3771 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 3772 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 3773 PetscValidType(A,1); 3774 PetscValidType(B,2); 3775 PetscCheckSameComm(A,1,B,2); 3776 MatCheckPreallocated(B,2); 3777 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3778 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3779 if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim (%D,%D) (%D,%D)",A->rmap->N,B->rmap->N,A->cmap->N,B->cmap->N); 3780 MatCheckPreallocated(A,1); 3781 3782 ierr = PetscLogEventBegin(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 3783 if (A->ops->copy) { 3784 ierr = (*A->ops->copy)(A,B,str);CHKERRQ(ierr); 3785 } else { /* generic conversion */ 3786 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 3787 } 3788 3789 B->stencil.dim = A->stencil.dim; 3790 B->stencil.noc = A->stencil.noc; 3791 for (i=0; i<=A->stencil.dim; i++) { 3792 B->stencil.dims[i] = A->stencil.dims[i]; 3793 B->stencil.starts[i] = A->stencil.starts[i]; 3794 } 3795 3796 ierr = PetscLogEventEnd(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 3797 ierr = PetscObjectStateIncrease((PetscObject)B);CHKERRQ(ierr); 3798 PetscFunctionReturn(0); 3799 } 3800 3801 #undef __FUNCT__ 3802 #define __FUNCT__ "MatConvert" 3803 /*@C 3804 MatConvert - Converts a matrix to another matrix, either of the same 3805 or different type. 3806 3807 Collective on Mat 3808 3809 Input Parameters: 3810 + mat - the matrix 3811 . newtype - new matrix type. Use MATSAME to create a new matrix of the 3812 same type as the original matrix. 3813 - reuse - denotes if the destination matrix is to be created or reused. Currently 3814 MAT_REUSE_MATRIX is only supported for inplace conversion, otherwise use 3815 MAT_INITIAL_MATRIX. 3816 3817 Output Parameter: 3818 . M - pointer to place new matrix 3819 3820 Notes: 3821 MatConvert() first creates a new matrix and then copies the data from 3822 the first matrix. A related routine is MatCopy(), which copies the matrix 3823 entries of one matrix to another already existing matrix context. 3824 3825 Cannot be used to convert a sequential matrix to parallel or parallel to sequential, 3826 the MPI communicator of the generated matrix is always the same as the communicator 3827 of the input matrix. 3828 3829 Level: intermediate 3830 3831 Concepts: matrices^converting between storage formats 3832 3833 .seealso: MatCopy(), MatDuplicate() 3834 @*/ 3835 PetscErrorCode MatConvert(Mat mat, MatType newtype,MatReuse reuse,Mat *M) 3836 { 3837 PetscErrorCode ierr; 3838 PetscBool sametype,issame,flg; 3839 char convname[256],mtype[256]; 3840 Mat B; 3841 3842 PetscFunctionBegin; 3843 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3844 PetscValidType(mat,1); 3845 PetscValidPointer(M,3); 3846 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3847 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3848 MatCheckPreallocated(mat,1); 3849 ierr = MatSetOption(mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);CHKERRQ(ierr); 3850 3851 ierr = PetscOptionsGetString(((PetscObject)mat)->prefix,"-matconvert_type",mtype,256,&flg);CHKERRQ(ierr); 3852 if (flg) { 3853 newtype = mtype; 3854 } 3855 ierr = PetscObjectTypeCompare((PetscObject)mat,newtype,&sametype);CHKERRQ(ierr); 3856 ierr = PetscStrcmp(newtype,"same",&issame);CHKERRQ(ierr); 3857 if ((reuse == MAT_REUSE_MATRIX) && (mat != *M)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"MAT_REUSE_MATRIX only supported for in-place conversion currently"); 3858 3859 if ((reuse == MAT_REUSE_MATRIX) && (issame || sametype)) PetscFunctionReturn(0); 3860 3861 if ((sametype || issame) && (reuse==MAT_INITIAL_MATRIX) && mat->ops->duplicate) { 3862 ierr = (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);CHKERRQ(ierr); 3863 } else { 3864 PetscErrorCode (*conv)(Mat, MatType,MatReuse,Mat*)=NULL; 3865 const char *prefix[3] = {"seq","mpi",""}; 3866 PetscInt i; 3867 /* 3868 Order of precedence: 3869 1) See if a specialized converter is known to the current matrix. 3870 2) See if a specialized converter is known to the desired matrix class. 3871 3) See if a good general converter is registered for the desired class 3872 (as of 6/27/03 only MATMPIADJ falls into this category). 3873 4) See if a good general converter is known for the current matrix. 3874 5) Use a really basic converter. 3875 */ 3876 3877 /* 1) See if a specialized converter is known to the current matrix and the desired class */ 3878 for (i=0; i<3; i++) { 3879 ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); 3880 ierr = PetscStrcat(convname,((PetscObject)mat)->type_name);CHKERRQ(ierr); 3881 ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); 3882 ierr = PetscStrcat(convname,prefix[i]);CHKERRQ(ierr); 3883 ierr = PetscStrcat(convname,issame ? ((PetscObject)mat)->type_name : newtype);CHKERRQ(ierr); 3884 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 3885 ierr = PetscObjectQueryFunction((PetscObject)mat,convname,&conv);CHKERRQ(ierr); 3886 if (conv) goto foundconv; 3887 } 3888 3889 /* 2) See if a specialized converter is known to the desired matrix class. */ 3890 ierr = MatCreate(PetscObjectComm((PetscObject)mat),&B);CHKERRQ(ierr); 3891 ierr = MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->N,mat->cmap->N);CHKERRQ(ierr); 3892 ierr = MatSetType(B,newtype);CHKERRQ(ierr); 3893 for (i=0; i<3; i++) { 3894 ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); 3895 ierr = PetscStrcat(convname,((PetscObject)mat)->type_name);CHKERRQ(ierr); 3896 ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); 3897 ierr = PetscStrcat(convname,prefix[i]);CHKERRQ(ierr); 3898 ierr = PetscStrcat(convname,newtype);CHKERRQ(ierr); 3899 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 3900 ierr = PetscObjectQueryFunction((PetscObject)B,convname,&conv);CHKERRQ(ierr); 3901 if (conv) { 3902 ierr = MatDestroy(&B);CHKERRQ(ierr); 3903 goto foundconv; 3904 } 3905 } 3906 3907 /* 3) See if a good general converter is registered for the desired class */ 3908 conv = B->ops->convertfrom; 3909 ierr = MatDestroy(&B);CHKERRQ(ierr); 3910 if (conv) goto foundconv; 3911 3912 /* 4) See if a good general converter is known for the current matrix */ 3913 if (mat->ops->convert) { 3914 conv = mat->ops->convert; 3915 } 3916 if (conv) goto foundconv; 3917 3918 /* 5) Use a really basic converter. */ 3919 conv = MatConvert_Basic; 3920 3921 foundconv: 3922 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 3923 ierr = (*conv)(mat,newtype,reuse,M);CHKERRQ(ierr); 3924 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 3925 } 3926 ierr = PetscObjectStateIncrease((PetscObject)*M);CHKERRQ(ierr); 3927 3928 /* Copy Mat options */ 3929 if (mat->symmetric) {ierr = MatSetOption(*M,MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr);} 3930 if (mat->hermitian) {ierr = MatSetOption(*M,MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr);} 3931 PetscFunctionReturn(0); 3932 } 3933 3934 #undef __FUNCT__ 3935 #define __FUNCT__ "MatFactorGetSolverPackage" 3936 /*@C 3937 MatFactorGetSolverPackage - Returns name of the package providing the factorization routines 3938 3939 Not Collective 3940 3941 Input Parameter: 3942 . mat - the matrix, must be a factored matrix 3943 3944 Output Parameter: 3945 . type - the string name of the package (do not free this string) 3946 3947 Notes: 3948 In Fortran you pass in a empty string and the package name will be copied into it. 3949 (Make sure the string is long enough) 3950 3951 Level: intermediate 3952 3953 .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable(), MatGetFactor() 3954 @*/ 3955 PetscErrorCode MatFactorGetSolverPackage(Mat mat, const MatSolverPackage *type) 3956 { 3957 PetscErrorCode ierr, (*conv)(Mat,const MatSolverPackage*); 3958 3959 PetscFunctionBegin; 3960 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 3961 PetscValidType(mat,1); 3962 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Only for factored matrix"); 3963 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatFactorGetSolverPackage_C",&conv);CHKERRQ(ierr); 3964 if (!conv) { 3965 *type = MATSOLVERPETSC; 3966 } else { 3967 ierr = (*conv)(mat,type);CHKERRQ(ierr); 3968 } 3969 PetscFunctionReturn(0); 3970 } 3971 3972 #undef __FUNCT__ 3973 #define __FUNCT__ "MatGetFactor" 3974 /*@C 3975 MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic() 3976 3977 Collective on Mat 3978 3979 Input Parameters: 3980 + mat - the matrix 3981 . type - name of solver type, for example, superlu, petsc (to use PETSc's default) 3982 - ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU, 3983 3984 Output Parameters: 3985 . f - the factor matrix used with MatXXFactorSymbolic() calls 3986 3987 Notes: 3988 Some PETSc matrix formats have alternative solvers available that are contained in alternative packages 3989 such as pastix, superlu, mumps etc. 3990 3991 PETSc must have been ./configure to use the external solver, using the option --download-package 3992 3993 Level: intermediate 3994 3995 .seealso: MatCopy(), MatDuplicate(), MatGetFactorAvailable() 3996 @*/ 3997 PetscErrorCode MatGetFactor(Mat mat, const MatSolverPackage type,MatFactorType ftype,Mat *f) 3998 { 3999 PetscErrorCode ierr,(*conv)(Mat,MatFactorType,Mat*); 4000 char convname[256]; 4001 4002 PetscFunctionBegin; 4003 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4004 PetscValidType(mat,1); 4005 4006 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4007 MatCheckPreallocated(mat,1); 4008 4009 ierr = PetscStrcpy(convname,"MatGetFactor_");CHKERRQ(ierr); 4010 ierr = PetscStrcat(convname,type);CHKERRQ(ierr); 4011 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 4012 ierr = PetscObjectQueryFunction((PetscObject)mat,convname,&conv);CHKERRQ(ierr); 4013 if (!conv) { 4014 PetscBool flag; 4015 MPI_Comm comm; 4016 4017 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 4018 ierr = PetscStrcasecmp(MATSOLVERPETSC,type,&flag);CHKERRQ(ierr); 4019 if (flag) SETERRQ2(comm,PETSC_ERR_SUP,"Matrix format %s does not have a built-in PETSc %s",((PetscObject)mat)->type_name,MatFactorTypes[ftype]); 4020 else SETERRQ4(comm,PETSC_ERR_SUP,"Matrix format %s does not have a solver package %s for %s. Perhaps you must ./configure with --download-%s",((PetscObject)mat)->type_name,type,MatFactorTypes[ftype],type); 4021 } 4022 ierr = (*conv)(mat,ftype,f);CHKERRQ(ierr); 4023 PetscFunctionReturn(0); 4024 } 4025 4026 #undef __FUNCT__ 4027 #define __FUNCT__ "MatGetFactorAvailable" 4028 /*@C 4029 MatGetFactorAvailable - Returns a a flag if matrix supports particular package and factor type 4030 4031 Not Collective 4032 4033 Input Parameters: 4034 + mat - the matrix 4035 . type - name of solver type, for example, superlu, petsc (to use PETSc's default) 4036 - ftype - factor type, MAT_FACTOR_LU, MAT_FACTOR_CHOLESKY, MAT_FACTOR_ICC, MAT_FACTOR_ILU, 4037 4038 Output Parameter: 4039 . flg - PETSC_TRUE if the factorization is available 4040 4041 Notes: 4042 Some PETSc matrix formats have alternative solvers available that are contained in alternative packages 4043 such as pastix, superlu, mumps etc. 4044 4045 PETSc must have been ./configure to use the external solver, using the option --download-package 4046 4047 Level: intermediate 4048 4049 .seealso: MatCopy(), MatDuplicate(), MatGetFactor() 4050 @*/ 4051 PetscErrorCode MatGetFactorAvailable(Mat mat, const MatSolverPackage type,MatFactorType ftype,PetscBool *flg) 4052 { 4053 PetscErrorCode ierr, (*conv)(Mat,MatFactorType,PetscBool*); 4054 char convname[256]; 4055 4056 PetscFunctionBegin; 4057 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4058 PetscValidType(mat,1); 4059 4060 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4061 MatCheckPreallocated(mat,1); 4062 4063 ierr = PetscStrcpy(convname,"MatGetFactorAvailable_");CHKERRQ(ierr); 4064 ierr = PetscStrcat(convname,type);CHKERRQ(ierr); 4065 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 4066 ierr = PetscObjectQueryFunction((PetscObject)mat,convname,&conv);CHKERRQ(ierr); 4067 if (!conv) { 4068 *flg = PETSC_FALSE; 4069 } else { 4070 ierr = (*conv)(mat,ftype,flg);CHKERRQ(ierr); 4071 } 4072 PetscFunctionReturn(0); 4073 } 4074 4075 4076 #undef __FUNCT__ 4077 #define __FUNCT__ "MatDuplicate" 4078 /*@ 4079 MatDuplicate - Duplicates a matrix including the non-zero structure. 4080 4081 Collective on Mat 4082 4083 Input Parameters: 4084 + mat - the matrix 4085 - op - either MAT_DO_NOT_COPY_VALUES or MAT_COPY_VALUES, cause it to copy the numerical values in the matrix 4086 MAT_SHARE_NONZERO_PATTERN to share the nonzero patterns with the previous matrix and not copy them. 4087 4088 Output Parameter: 4089 . M - pointer to place new matrix 4090 4091 Level: intermediate 4092 4093 Concepts: matrices^duplicating 4094 4095 Notes: You cannot change the nonzero pattern for the parent or child matrix if you use MAT_SHARE_NONZERO_PATTERN. 4096 4097 .seealso: MatCopy(), MatConvert() 4098 @*/ 4099 PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M) 4100 { 4101 PetscErrorCode ierr; 4102 Mat B; 4103 PetscInt i; 4104 4105 PetscFunctionBegin; 4106 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4107 PetscValidType(mat,1); 4108 PetscValidPointer(M,3); 4109 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4110 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4111 MatCheckPreallocated(mat,1); 4112 4113 *M = 0; 4114 if (!mat->ops->duplicate) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not written for this matrix type"); 4115 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 4116 ierr = (*mat->ops->duplicate)(mat,op,M);CHKERRQ(ierr); 4117 B = *M; 4118 4119 B->stencil.dim = mat->stencil.dim; 4120 B->stencil.noc = mat->stencil.noc; 4121 for (i=0; i<=mat->stencil.dim; i++) { 4122 B->stencil.dims[i] = mat->stencil.dims[i]; 4123 B->stencil.starts[i] = mat->stencil.starts[i]; 4124 } 4125 4126 B->nooffproczerorows = mat->nooffproczerorows; 4127 B->nooffprocentries = mat->nooffprocentries; 4128 4129 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 4130 ierr = PetscObjectStateIncrease((PetscObject)B);CHKERRQ(ierr); 4131 PetscFunctionReturn(0); 4132 } 4133 4134 #undef __FUNCT__ 4135 #define __FUNCT__ "MatGetDiagonal" 4136 /*@ 4137 MatGetDiagonal - Gets the diagonal of a matrix. 4138 4139 Logically Collective on Mat and Vec 4140 4141 Input Parameters: 4142 + mat - the matrix 4143 - v - the vector for storing the diagonal 4144 4145 Output Parameter: 4146 . v - the diagonal of the matrix 4147 4148 Level: intermediate 4149 4150 Note: 4151 Currently only correct in parallel for square matrices. 4152 4153 Concepts: matrices^accessing diagonals 4154 4155 .seealso: MatGetRow(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs() 4156 @*/ 4157 PetscErrorCode MatGetDiagonal(Mat mat,Vec v) 4158 { 4159 PetscErrorCode ierr; 4160 4161 PetscFunctionBegin; 4162 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4163 PetscValidType(mat,1); 4164 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4165 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4166 if (!mat->ops->getdiagonal) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4167 MatCheckPreallocated(mat,1); 4168 4169 ierr = (*mat->ops->getdiagonal)(mat,v);CHKERRQ(ierr); 4170 ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); 4171 PetscFunctionReturn(0); 4172 } 4173 4174 #undef __FUNCT__ 4175 #define __FUNCT__ "MatGetRowMin" 4176 /*@ 4177 MatGetRowMin - Gets the minimum value (of the real part) of each 4178 row of the matrix 4179 4180 Logically Collective on Mat and Vec 4181 4182 Input Parameters: 4183 . mat - the matrix 4184 4185 Output Parameter: 4186 + v - the vector for storing the maximums 4187 - idx - the indices of the column found for each row (optional) 4188 4189 Level: intermediate 4190 4191 Notes: The result of this call are the same as if one converted the matrix to dense format 4192 and found the minimum value in each row (i.e. the implicit zeros are counted as zeros). 4193 4194 This code is only implemented for a couple of matrix formats. 4195 4196 Concepts: matrices^getting row maximums 4197 4198 .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs(), 4199 MatGetRowMax() 4200 @*/ 4201 PetscErrorCode MatGetRowMin(Mat mat,Vec v,PetscInt idx[]) 4202 { 4203 PetscErrorCode ierr; 4204 4205 PetscFunctionBegin; 4206 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4207 PetscValidType(mat,1); 4208 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4209 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4210 if (!mat->ops->getrowmax) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4211 MatCheckPreallocated(mat,1); 4212 4213 ierr = (*mat->ops->getrowmin)(mat,v,idx);CHKERRQ(ierr); 4214 ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); 4215 PetscFunctionReturn(0); 4216 } 4217 4218 #undef __FUNCT__ 4219 #define __FUNCT__ "MatGetRowMinAbs" 4220 /*@ 4221 MatGetRowMinAbs - Gets the minimum value (in absolute value) of each 4222 row of the matrix 4223 4224 Logically Collective on Mat and Vec 4225 4226 Input Parameters: 4227 . mat - the matrix 4228 4229 Output Parameter: 4230 + v - the vector for storing the minimums 4231 - idx - the indices of the column found for each row (or NULL if not needed) 4232 4233 Level: intermediate 4234 4235 Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that 4236 row is 0 (the first column). 4237 4238 This code is only implemented for a couple of matrix formats. 4239 4240 Concepts: matrices^getting row maximums 4241 4242 .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMaxAbs(), MatGetRowMin() 4243 @*/ 4244 PetscErrorCode MatGetRowMinAbs(Mat mat,Vec v,PetscInt idx[]) 4245 { 4246 PetscErrorCode ierr; 4247 4248 PetscFunctionBegin; 4249 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4250 PetscValidType(mat,1); 4251 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4252 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4253 if (!mat->ops->getrowminabs) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4254 MatCheckPreallocated(mat,1); 4255 if (idx) {ierr = PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));CHKERRQ(ierr);} 4256 4257 ierr = (*mat->ops->getrowminabs)(mat,v,idx);CHKERRQ(ierr); 4258 ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); 4259 PetscFunctionReturn(0); 4260 } 4261 4262 #undef __FUNCT__ 4263 #define __FUNCT__ "MatGetRowMax" 4264 /*@ 4265 MatGetRowMax - Gets the maximum value (of the real part) of each 4266 row of the matrix 4267 4268 Logically Collective on Mat and Vec 4269 4270 Input Parameters: 4271 . mat - the matrix 4272 4273 Output Parameter: 4274 + v - the vector for storing the maximums 4275 - idx - the indices of the column found for each row (optional) 4276 4277 Level: intermediate 4278 4279 Notes: The result of this call are the same as if one converted the matrix to dense format 4280 and found the minimum value in each row (i.e. the implicit zeros are counted as zeros). 4281 4282 This code is only implemented for a couple of matrix formats. 4283 4284 Concepts: matrices^getting row maximums 4285 4286 .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMaxAbs(), MatGetRowMin() 4287 @*/ 4288 PetscErrorCode MatGetRowMax(Mat mat,Vec v,PetscInt idx[]) 4289 { 4290 PetscErrorCode ierr; 4291 4292 PetscFunctionBegin; 4293 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4294 PetscValidType(mat,1); 4295 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4296 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4297 if (!mat->ops->getrowmax) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4298 MatCheckPreallocated(mat,1); 4299 4300 ierr = (*mat->ops->getrowmax)(mat,v,idx);CHKERRQ(ierr); 4301 ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); 4302 PetscFunctionReturn(0); 4303 } 4304 4305 #undef __FUNCT__ 4306 #define __FUNCT__ "MatGetRowMaxAbs" 4307 /*@ 4308 MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each 4309 row of the matrix 4310 4311 Logically Collective on Mat and Vec 4312 4313 Input Parameters: 4314 . mat - the matrix 4315 4316 Output Parameter: 4317 + v - the vector for storing the maximums 4318 - idx - the indices of the column found for each row (or NULL if not needed) 4319 4320 Level: intermediate 4321 4322 Notes: if a row is completely empty or has only 0.0 values then the idx[] value for that 4323 row is 0 (the first column). 4324 4325 This code is only implemented for a couple of matrix formats. 4326 4327 Concepts: matrices^getting row maximums 4328 4329 .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMin() 4330 @*/ 4331 PetscErrorCode MatGetRowMaxAbs(Mat mat,Vec v,PetscInt idx[]) 4332 { 4333 PetscErrorCode ierr; 4334 4335 PetscFunctionBegin; 4336 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4337 PetscValidType(mat,1); 4338 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4339 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4340 if (!mat->ops->getrowmaxabs) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4341 MatCheckPreallocated(mat,1); 4342 if (idx) {ierr = PetscMemzero(idx,mat->rmap->n*sizeof(PetscInt));CHKERRQ(ierr);} 4343 4344 ierr = (*mat->ops->getrowmaxabs)(mat,v,idx);CHKERRQ(ierr); 4345 ierr = PetscObjectStateIncrease((PetscObject)v);CHKERRQ(ierr); 4346 PetscFunctionReturn(0); 4347 } 4348 4349 #undef __FUNCT__ 4350 #define __FUNCT__ "MatGetRowSum" 4351 /*@ 4352 MatGetRowSum - Gets the sum of each row of the matrix 4353 4354 Logically Collective on Mat and Vec 4355 4356 Input Parameters: 4357 . mat - the matrix 4358 4359 Output Parameter: 4360 . v - the vector for storing the sum of rows 4361 4362 Level: intermediate 4363 4364 Notes: This code is slow since it is not currently specialized for different formats 4365 4366 Concepts: matrices^getting row sums 4367 4368 .seealso: MatGetDiagonal(), MatGetSubMatrices(), MatGetSubmatrix(), MatGetRowMax(), MatGetRowMin() 4369 @*/ 4370 PetscErrorCode MatGetRowSum(Mat mat, Vec v) 4371 { 4372 PetscInt start = 0, end = 0, row; 4373 PetscScalar *array; 4374 PetscErrorCode ierr; 4375 4376 PetscFunctionBegin; 4377 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4378 PetscValidType(mat,1); 4379 PetscValidHeaderSpecific(v,VEC_CLASSID,2); 4380 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4381 MatCheckPreallocated(mat,1); 4382 ierr = MatGetOwnershipRange(mat, &start, &end);CHKERRQ(ierr); 4383 ierr = VecGetArray(v, &array);CHKERRQ(ierr); 4384 for (row = start; row < end; ++row) { 4385 PetscInt ncols, col; 4386 const PetscInt *cols; 4387 const PetscScalar *vals; 4388 4389 array[row - start] = 0.0; 4390 4391 ierr = MatGetRow(mat, row, &ncols, &cols, &vals);CHKERRQ(ierr); 4392 for (col = 0; col < ncols; col++) { 4393 array[row - start] += vals[col]; 4394 } 4395 ierr = MatRestoreRow(mat, row, &ncols, &cols, &vals);CHKERRQ(ierr); 4396 } 4397 ierr = VecRestoreArray(v, &array);CHKERRQ(ierr); 4398 ierr = PetscObjectStateIncrease((PetscObject) v);CHKERRQ(ierr); 4399 PetscFunctionReturn(0); 4400 } 4401 4402 #undef __FUNCT__ 4403 #define __FUNCT__ "MatTranspose" 4404 /*@ 4405 MatTranspose - Computes an in-place or out-of-place transpose of a matrix. 4406 4407 Collective on Mat 4408 4409 Input Parameter: 4410 + mat - the matrix to transpose 4411 - reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 4412 4413 Output Parameters: 4414 . B - the transpose 4415 4416 Notes: 4417 If you pass in &mat for B the transpose will be done in place, for example MatTranspose(mat,MAT_REUSE_MATRIX,&mat); 4418 4419 Consider using MatCreateTranspose() instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed. 4420 4421 Level: intermediate 4422 4423 Concepts: matrices^transposing 4424 4425 .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse 4426 @*/ 4427 PetscErrorCode MatTranspose(Mat mat,MatReuse reuse,Mat *B) 4428 { 4429 PetscErrorCode ierr; 4430 4431 PetscFunctionBegin; 4432 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4433 PetscValidType(mat,1); 4434 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4435 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4436 if (!mat->ops->transpose) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4437 MatCheckPreallocated(mat,1); 4438 4439 ierr = PetscLogEventBegin(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 4440 ierr = (*mat->ops->transpose)(mat,reuse,B);CHKERRQ(ierr); 4441 ierr = PetscLogEventEnd(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 4442 if (B) {ierr = PetscObjectStateIncrease((PetscObject)*B);CHKERRQ(ierr);} 4443 PetscFunctionReturn(0); 4444 } 4445 4446 #undef __FUNCT__ 4447 #define __FUNCT__ "MatIsTranspose" 4448 /*@ 4449 MatIsTranspose - Test whether a matrix is another one's transpose, 4450 or its own, in which case it tests symmetry. 4451 4452 Collective on Mat 4453 4454 Input Parameter: 4455 + A - the matrix to test 4456 - B - the matrix to test against, this can equal the first parameter 4457 4458 Output Parameters: 4459 . flg - the result 4460 4461 Notes: 4462 Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm 4463 has a running time of the order of the number of nonzeros; the parallel 4464 test involves parallel copies of the block-offdiagonal parts of the matrix. 4465 4466 Level: intermediate 4467 4468 Concepts: matrices^transposing, matrix^symmetry 4469 4470 .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian() 4471 @*/ 4472 PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscBool *flg) 4473 { 4474 PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*); 4475 4476 PetscFunctionBegin; 4477 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 4478 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 4479 PetscValidPointer(flg,3); 4480 ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",&f);CHKERRQ(ierr); 4481 ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",&g);CHKERRQ(ierr); 4482 *flg = PETSC_FALSE; 4483 if (f && g) { 4484 if (f == g) { 4485 ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); 4486 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test"); 4487 } else { 4488 MatType mattype; 4489 if (!f) { 4490 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 4491 } else { 4492 ierr = MatGetType(B,&mattype);CHKERRQ(ierr); 4493 } 4494 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for transpose",mattype); 4495 } 4496 PetscFunctionReturn(0); 4497 } 4498 4499 #undef __FUNCT__ 4500 #define __FUNCT__ "MatHermitianTranspose" 4501 /*@ 4502 MatHermitianTranspose - Computes an in-place or out-of-place transpose of a matrix in complex conjugate. 4503 4504 Collective on Mat 4505 4506 Input Parameter: 4507 + mat - the matrix to transpose and complex conjugate 4508 - reuse - store the transpose matrix in the provided B 4509 4510 Output Parameters: 4511 . B - the Hermitian 4512 4513 Notes: 4514 If you pass in &mat for B the Hermitian will be done in place 4515 4516 Level: intermediate 4517 4518 Concepts: matrices^transposing, complex conjugatex 4519 4520 .seealso: MatTranspose(), MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose(), MatReuse 4521 @*/ 4522 PetscErrorCode MatHermitianTranspose(Mat mat,MatReuse reuse,Mat *B) 4523 { 4524 PetscErrorCode ierr; 4525 4526 PetscFunctionBegin; 4527 ierr = MatTranspose(mat,reuse,B);CHKERRQ(ierr); 4528 #if defined(PETSC_USE_COMPLEX) 4529 ierr = MatConjugate(*B);CHKERRQ(ierr); 4530 #endif 4531 PetscFunctionReturn(0); 4532 } 4533 4534 #undef __FUNCT__ 4535 #define __FUNCT__ "MatIsHermitianTranspose" 4536 /*@ 4537 MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose, 4538 4539 Collective on Mat 4540 4541 Input Parameter: 4542 + A - the matrix to test 4543 - B - the matrix to test against, this can equal the first parameter 4544 4545 Output Parameters: 4546 . flg - the result 4547 4548 Notes: 4549 Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm 4550 has a running time of the order of the number of nonzeros; the parallel 4551 test involves parallel copies of the block-offdiagonal parts of the matrix. 4552 4553 Level: intermediate 4554 4555 Concepts: matrices^transposing, matrix^symmetry 4556 4557 .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian(), MatIsTranspose() 4558 @*/ 4559 PetscErrorCode MatIsHermitianTranspose(Mat A,Mat B,PetscReal tol,PetscBool *flg) 4560 { 4561 PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscBool*),(*g)(Mat,Mat,PetscReal,PetscBool*); 4562 4563 PetscFunctionBegin; 4564 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 4565 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 4566 PetscValidPointer(flg,3); 4567 ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsHermitianTranspose_C",&f);CHKERRQ(ierr); 4568 ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsHermitianTranspose_C",&g);CHKERRQ(ierr); 4569 if (f && g) { 4570 if (f==g) { 4571 ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); 4572 } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for Hermitian test"); 4573 } 4574 PetscFunctionReturn(0); 4575 } 4576 4577 #undef __FUNCT__ 4578 #define __FUNCT__ "MatPermute" 4579 /*@ 4580 MatPermute - Creates a new matrix with rows and columns permuted from the 4581 original. 4582 4583 Collective on Mat 4584 4585 Input Parameters: 4586 + mat - the matrix to permute 4587 . row - row permutation, each processor supplies only the permutation for its rows 4588 - col - column permutation, each processor supplies only the permutation for its columns 4589 4590 Output Parameters: 4591 . B - the permuted matrix 4592 4593 Level: advanced 4594 4595 Note: 4596 The index sets map from row/col of permuted matrix to row/col of original matrix. 4597 The index sets should be on the same communicator as Mat and have the same local sizes. 4598 4599 Concepts: matrices^permuting 4600 4601 .seealso: MatGetOrdering(), ISAllGather() 4602 4603 @*/ 4604 PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B) 4605 { 4606 PetscErrorCode ierr; 4607 4608 PetscFunctionBegin; 4609 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4610 PetscValidType(mat,1); 4611 PetscValidHeaderSpecific(row,IS_CLASSID,2); 4612 PetscValidHeaderSpecific(col,IS_CLASSID,3); 4613 PetscValidPointer(B,4); 4614 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4615 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4616 if (!mat->ops->permute) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatPermute not available for Mat type %s",((PetscObject)mat)->type_name); 4617 MatCheckPreallocated(mat,1); 4618 4619 ierr = (*mat->ops->permute)(mat,row,col,B);CHKERRQ(ierr); 4620 ierr = PetscObjectStateIncrease((PetscObject)*B);CHKERRQ(ierr); 4621 PetscFunctionReturn(0); 4622 } 4623 4624 #undef __FUNCT__ 4625 #define __FUNCT__ "MatEqual" 4626 /*@ 4627 MatEqual - Compares two matrices. 4628 4629 Collective on Mat 4630 4631 Input Parameters: 4632 + A - the first matrix 4633 - B - the second matrix 4634 4635 Output Parameter: 4636 . flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise. 4637 4638 Level: intermediate 4639 4640 Concepts: matrices^equality between 4641 @*/ 4642 PetscErrorCode MatEqual(Mat A,Mat B,PetscBool *flg) 4643 { 4644 PetscErrorCode ierr; 4645 4646 PetscFunctionBegin; 4647 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 4648 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 4649 PetscValidType(A,1); 4650 PetscValidType(B,2); 4651 PetscValidIntPointer(flg,3); 4652 PetscCheckSameComm(A,1,B,2); 4653 MatCheckPreallocated(B,2); 4654 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4655 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4656 if (A->rmap->N != B->rmap->N || A->cmap->N != B->cmap->N) SETERRQ4(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D %D %D",A->rmap->N,B->rmap->N,A->cmap->N,B->cmap->N); 4657 if (!A->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)A)->type_name); 4658 if (!B->ops->equal) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type %s",((PetscObject)B)->type_name); 4659 if (A->ops->equal != B->ops->equal) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"A is type: %s\nB is type: %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); 4660 MatCheckPreallocated(A,1); 4661 4662 ierr = (*A->ops->equal)(A,B,flg);CHKERRQ(ierr); 4663 PetscFunctionReturn(0); 4664 } 4665 4666 #undef __FUNCT__ 4667 #define __FUNCT__ "MatDiagonalScale" 4668 /*@ 4669 MatDiagonalScale - Scales a matrix on the left and right by diagonal 4670 matrices that are stored as vectors. Either of the two scaling 4671 matrices can be NULL. 4672 4673 Collective on Mat 4674 4675 Input Parameters: 4676 + mat - the matrix to be scaled 4677 . l - the left scaling vector (or NULL) 4678 - r - the right scaling vector (or NULL) 4679 4680 Notes: 4681 MatDiagonalScale() computes A = LAR, where 4682 L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector) 4683 The L scales the rows of the matrix, the R scales the columns of the matrix. 4684 4685 Level: intermediate 4686 4687 Concepts: matrices^diagonal scaling 4688 Concepts: diagonal scaling of matrices 4689 4690 .seealso: MatScale() 4691 @*/ 4692 PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r) 4693 { 4694 PetscErrorCode ierr; 4695 4696 PetscFunctionBegin; 4697 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4698 PetscValidType(mat,1); 4699 if (!mat->ops->diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4700 if (l) {PetscValidHeaderSpecific(l,VEC_CLASSID,2);PetscCheckSameComm(mat,1,l,2);} 4701 if (r) {PetscValidHeaderSpecific(r,VEC_CLASSID,3);PetscCheckSameComm(mat,1,r,3);} 4702 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4703 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4704 MatCheckPreallocated(mat,1); 4705 4706 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 4707 ierr = (*mat->ops->diagonalscale)(mat,l,r);CHKERRQ(ierr); 4708 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 4709 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 4710 #if defined(PETSC_HAVE_CUSP) 4711 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 4712 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 4713 } 4714 #endif 4715 #if defined(PETSC_HAVE_VIENNACL) 4716 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 4717 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 4718 } 4719 #endif 4720 PetscFunctionReturn(0); 4721 } 4722 4723 #undef __FUNCT__ 4724 #define __FUNCT__ "MatScale" 4725 /*@ 4726 MatScale - Scales all elements of a matrix by a given number. 4727 4728 Logically Collective on Mat 4729 4730 Input Parameters: 4731 + mat - the matrix to be scaled 4732 - a - the scaling value 4733 4734 Output Parameter: 4735 . mat - the scaled matrix 4736 4737 Level: intermediate 4738 4739 Concepts: matrices^scaling all entries 4740 4741 .seealso: MatDiagonalScale() 4742 @*/ 4743 PetscErrorCode MatScale(Mat mat,PetscScalar a) 4744 { 4745 PetscErrorCode ierr; 4746 4747 PetscFunctionBegin; 4748 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4749 PetscValidType(mat,1); 4750 if (a != (PetscScalar)1.0 && !mat->ops->scale) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4751 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4752 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4753 PetscValidLogicalCollectiveScalar(mat,a,2); 4754 MatCheckPreallocated(mat,1); 4755 4756 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 4757 if (a != (PetscScalar)1.0) { 4758 ierr = (*mat->ops->scale)(mat,a);CHKERRQ(ierr); 4759 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 4760 } 4761 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 4762 #if defined(PETSC_HAVE_CUSP) 4763 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 4764 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 4765 } 4766 #endif 4767 #if defined(PETSC_HAVE_VIENNACL) 4768 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 4769 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 4770 } 4771 #endif 4772 PetscFunctionReturn(0); 4773 } 4774 4775 #undef __FUNCT__ 4776 #define __FUNCT__ "MatNorm" 4777 /*@ 4778 MatNorm - Calculates various norms of a matrix. 4779 4780 Collective on Mat 4781 4782 Input Parameters: 4783 + mat - the matrix 4784 - type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY 4785 4786 Output Parameters: 4787 . nrm - the resulting norm 4788 4789 Level: intermediate 4790 4791 Concepts: matrices^norm 4792 Concepts: norm^of matrix 4793 @*/ 4794 PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm) 4795 { 4796 PetscErrorCode ierr; 4797 4798 PetscFunctionBegin; 4799 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4800 PetscValidType(mat,1); 4801 PetscValidScalarPointer(nrm,3); 4802 4803 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4804 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4805 if (!mat->ops->norm) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 4806 MatCheckPreallocated(mat,1); 4807 4808 ierr = (*mat->ops->norm)(mat,type,nrm);CHKERRQ(ierr); 4809 PetscFunctionReturn(0); 4810 } 4811 4812 /* 4813 This variable is used to prevent counting of MatAssemblyBegin() that 4814 are called from within a MatAssemblyEnd(). 4815 */ 4816 static PetscInt MatAssemblyEnd_InUse = 0; 4817 #undef __FUNCT__ 4818 #define __FUNCT__ "MatAssemblyBegin" 4819 /*@ 4820 MatAssemblyBegin - Begins assembling the matrix. This routine should 4821 be called after completing all calls to MatSetValues(). 4822 4823 Collective on Mat 4824 4825 Input Parameters: 4826 + mat - the matrix 4827 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 4828 4829 Notes: 4830 MatSetValues() generally caches the values. The matrix is ready to 4831 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 4832 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 4833 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 4834 using the matrix. 4835 4836 ALL processes that share a matrix MUST call MatAssemblyBegin() and MatAssemblyEnd() the SAME NUMBER of times, and each time with the 4837 same flag of MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY for all processes. Thus you CANNOT locally change from ADD_VALUES to INSERT_VALUES, that is 4838 a global collective operation requring all processes that share the matrix. 4839 4840 Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed 4841 out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros 4842 before MAT_FINAL_ASSEMBLY so the space is not compressed out. 4843 4844 Level: beginner 4845 4846 Concepts: matrices^assembling 4847 4848 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled() 4849 @*/ 4850 PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type) 4851 { 4852 PetscErrorCode ierr; 4853 4854 PetscFunctionBegin; 4855 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4856 PetscValidType(mat,1); 4857 MatCheckPreallocated(mat,1); 4858 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?"); 4859 if (mat->assembled) { 4860 mat->was_assembled = PETSC_TRUE; 4861 mat->assembled = PETSC_FALSE; 4862 } 4863 if (!MatAssemblyEnd_InUse) { 4864 ierr = PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 4865 if (mat->ops->assemblybegin) {ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} 4866 ierr = PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 4867 } else if (mat->ops->assemblybegin) { 4868 ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr); 4869 } 4870 PetscFunctionReturn(0); 4871 } 4872 4873 #undef __FUNCT__ 4874 #define __FUNCT__ "MatAssembled" 4875 /*@ 4876 MatAssembled - Indicates if a matrix has been assembled and is ready for 4877 use; for example, in matrix-vector product. 4878 4879 Not Collective 4880 4881 Input Parameter: 4882 . mat - the matrix 4883 4884 Output Parameter: 4885 . assembled - PETSC_TRUE or PETSC_FALSE 4886 4887 Level: advanced 4888 4889 Concepts: matrices^assembled? 4890 4891 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin() 4892 @*/ 4893 PetscErrorCode MatAssembled(Mat mat,PetscBool *assembled) 4894 { 4895 PetscFunctionBegin; 4896 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4897 PetscValidType(mat,1); 4898 PetscValidPointer(assembled,2); 4899 *assembled = mat->assembled; 4900 PetscFunctionReturn(0); 4901 } 4902 4903 #undef __FUNCT__ 4904 #define __FUNCT__ "MatViewFromOptions" 4905 /* 4906 MatViewFromOptions - Processes command line options to determine if/how a matrix is to be viewed. Called from higher level packages. 4907 4908 Collective on Vec 4909 4910 Input Parameters: 4911 + mat - the matrix 4912 . prefix - prefix to use for viewing, or NULL to use prefix of 'mat' 4913 - optionname - option to activate viewing 4914 4915 Level: intermediate 4916 4917 .keywords: Mat, view, options, database 4918 .seealso: MatViewFromOptions() 4919 */ 4920 PetscErrorCode MatViewFromOptions(Mat mat,const char prefix[],const char optionname[]) 4921 { 4922 PetscErrorCode ierr; 4923 PetscViewer viewer; 4924 PetscBool flg; 4925 static PetscBool incall = PETSC_FALSE; 4926 PetscViewerFormat format; 4927 4928 PetscFunctionBegin; 4929 if (incall) PetscFunctionReturn(0); 4930 incall = PETSC_TRUE; 4931 if (prefix) { 4932 ierr = PetscOptionsGetViewer(PetscObjectComm((PetscObject)mat),prefix,optionname,&viewer,&format,&flg);CHKERRQ(ierr); 4933 } else { 4934 ierr = PetscOptionsGetViewer(PetscObjectComm((PetscObject)mat),((PetscObject)mat)->prefix,optionname,&viewer,&format,&flg);CHKERRQ(ierr); 4935 } 4936 if (flg) { 4937 ierr = PetscViewerPushFormat(viewer,format);CHKERRQ(ierr); 4938 ierr = MatView(mat,viewer);CHKERRQ(ierr); 4939 ierr = PetscViewerPopFormat(viewer);CHKERRQ(ierr); 4940 ierr = PetscViewerDestroy(&viewer);CHKERRQ(ierr); 4941 } 4942 incall = PETSC_FALSE; 4943 PetscFunctionReturn(0); 4944 } 4945 4946 #undef __FUNCT__ 4947 #define __FUNCT__ "MatAssemblyEnd" 4948 /*@ 4949 MatAssemblyEnd - Completes assembling the matrix. This routine should 4950 be called after MatAssemblyBegin(). 4951 4952 Collective on Mat 4953 4954 Input Parameters: 4955 + mat - the matrix 4956 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 4957 4958 Options Database Keys: 4959 + -mat_view ::ascii_info - Prints info on matrix at conclusion of MatEndAssembly() 4960 . -mat_view ::ascii_info_detail - Prints more detailed info 4961 . -mat_view - Prints matrix in ASCII format 4962 . -mat_view ::ascii_matlab - Prints matrix in Matlab format 4963 . -mat_view draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). 4964 . -display <name> - Sets display name (default is host) 4965 . -draw_pause <sec> - Sets number of seconds to pause after display 4966 . -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See the <a href="../../docs/manual.pdf">users manual</a>) 4967 . -viewer_socket_machine <machine> 4968 . -viewer_socket_port <port> 4969 . -mat_view binary - save matrix to file in binary format 4970 - -viewer_binary_filename <name> 4971 4972 Notes: 4973 MatSetValues() generally caches the values. The matrix is ready to 4974 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 4975 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 4976 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 4977 using the matrix. 4978 4979 Space for preallocated nonzeros that is not filled by a call to MatSetValues() or a related routine are compressed 4980 out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros 4981 before MAT_FINAL_ASSEMBLY so the space is not compressed out. 4982 4983 Level: beginner 4984 4985 .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), PetscDrawCreate(), MatView(), MatAssembled(), PetscViewerSocketOpen() 4986 @*/ 4987 PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type) 4988 { 4989 PetscErrorCode ierr; 4990 static PetscInt inassm = 0; 4991 PetscBool flg = PETSC_FALSE; 4992 4993 PetscFunctionBegin; 4994 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 4995 PetscValidType(mat,1); 4996 4997 inassm++; 4998 MatAssemblyEnd_InUse++; 4999 if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */ 5000 ierr = PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 5001 if (mat->ops->assemblyend) { 5002 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 5003 } 5004 ierr = PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 5005 } else if (mat->ops->assemblyend) { 5006 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 5007 } 5008 5009 /* Flush assembly is not a true assembly */ 5010 if (type != MAT_FLUSH_ASSEMBLY) { 5011 mat->assembled = PETSC_TRUE; mat->num_ass++; 5012 } 5013 mat->insertmode = NOT_SET_VALUES; 5014 MatAssemblyEnd_InUse--; 5015 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5016 if (!mat->symmetric_eternal) { 5017 mat->symmetric_set = PETSC_FALSE; 5018 mat->hermitian_set = PETSC_FALSE; 5019 mat->structurally_symmetric_set = PETSC_FALSE; 5020 } 5021 #if defined(PETSC_HAVE_CUSP) 5022 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5023 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5024 } 5025 #endif 5026 #if defined(PETSC_HAVE_VIENNACL) 5027 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5028 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5029 } 5030 #endif 5031 if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) { 5032 if (mat->viewonassembly) { 5033 ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); 5034 ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); 5035 ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); 5036 } 5037 5038 if (mat->checksymmetryonassembly) { 5039 ierr = MatIsSymmetric(mat,mat->checksymmetrytol,&flg);CHKERRQ(ierr); 5040 if (flg) { 5041 ierr = PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is symmetric (tolerance %G)\n",mat->checksymmetrytol);CHKERRQ(ierr); 5042 } else { 5043 ierr = PetscPrintf(PetscObjectComm((PetscObject)mat),"Matrix is not symmetric (tolerance %G)\n",mat->checksymmetrytol);CHKERRQ(ierr); 5044 } 5045 } 5046 if (mat->nullsp && mat->checknullspaceonassembly) { 5047 ierr = MatNullSpaceTest(mat->nullsp,mat,NULL);CHKERRQ(ierr); 5048 } 5049 } 5050 inassm--; 5051 PetscFunctionReturn(0); 5052 } 5053 5054 #undef __FUNCT__ 5055 #define __FUNCT__ "MatSetOption" 5056 /*@ 5057 MatSetOption - Sets a parameter option for a matrix. Some options 5058 may be specific to certain storage formats. Some options 5059 determine how values will be inserted (or added). Sorted, 5060 row-oriented input will generally assemble the fastest. The default 5061 is row-oriented. 5062 5063 Logically Collective on Mat for certain operations, such as MAT_SPD, not collective for MAT_ROW_ORIENTED, see MatOption 5064 5065 Input Parameters: 5066 + mat - the matrix 5067 . option - the option, one of those listed below (and possibly others), 5068 - flg - turn the option on (PETSC_TRUE) or off (PETSC_FALSE) 5069 5070 Options Describing Matrix Structure: 5071 + MAT_SPD - symmetric positive definite 5072 - MAT_SYMMETRIC - symmetric in terms of both structure and value 5073 . MAT_HERMITIAN - transpose is the complex conjugation 5074 . MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure 5075 - MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag 5076 you set to be kept with all future use of the matrix 5077 including after MatAssemblyBegin/End() which could 5078 potentially change the symmetry structure, i.e. you 5079 KNOW the matrix will ALWAYS have the property you set. 5080 5081 5082 Options For Use with MatSetValues(): 5083 Insert a logically dense subblock, which can be 5084 . MAT_ROW_ORIENTED - row-oriented (default) 5085 5086 Note these options reflect the data you pass in with MatSetValues(); it has 5087 nothing to do with how the data is stored internally in the matrix 5088 data structure. 5089 5090 When (re)assembling a matrix, we can restrict the input for 5091 efficiency/debugging purposes. These options include 5092 + MAT_NEW_NONZERO_LOCATIONS - additional insertions will be 5093 allowed if they generate a new nonzero 5094 . MAT_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only) 5095 . MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries 5096 . MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry 5097 . MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly 5098 + MAT_NO_OFF_PROC_ENTRIES - you know each process will only set values for its own rows, will generate an error if 5099 any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves 5100 performance for very large process counts. 5101 5102 Notes: 5103 Some options are relevant only for particular matrix types and 5104 are thus ignored by others. Other options are not supported by 5105 certain matrix types and will generate an error message if set. 5106 5107 If using a Fortran 77 module to compute a matrix, one may need to 5108 use the column-oriented option (or convert to the row-oriented 5109 format). 5110 5111 MAT_NEW_NONZERO_LOCATIONS set to PETSC_FALSE indicates that any add or insertion 5112 that would generate a new entry in the nonzero structure is instead 5113 ignored. Thus, if memory has not alredy been allocated for this particular 5114 data, then the insertion is ignored. For dense matrices, in which 5115 the entire array is allocated, no entries are ever ignored. 5116 Set after the first MatAssemblyEnd() 5117 5118 MAT_NEW_NONZERO_LOCATION_ERR indicates that any add or insertion 5119 that would generate a new entry in the nonzero structure instead produces 5120 an error. (Currently supported for AIJ and BAIJ formats only.) 5121 This is a useful flag when using SAME_NONZERO_PATTERN in calling 5122 KSPSetOperators() to ensure that the nonzero pattern truely does 5123 remain unchanged. Set after the first MatAssemblyEnd() 5124 5125 MAT_NEW_NONZERO_ALLOCATION_ERR indicates that any add or insertion 5126 that would generate a new entry that has not been preallocated will 5127 instead produce an error. (Currently supported for AIJ and BAIJ formats 5128 only.) This is a useful flag when debugging matrix memory preallocation. 5129 5130 MAT_IGNORE_OFF_PROC_ENTRIES indicates entries destined for 5131 other processors should be dropped, rather than stashed. 5132 This is useful if you know that the "owning" processor is also 5133 always generating the correct matrix entries, so that PETSc need 5134 not transfer duplicate entries generated on another processor. 5135 5136 MAT_USE_HASH_TABLE indicates that a hash table be used to improve the 5137 searches during matrix assembly. When this flag is set, the hash table 5138 is created during the first Matrix Assembly. This hash table is 5139 used the next time through, during MatSetVaules()/MatSetVaulesBlocked() 5140 to improve the searching of indices. MAT_NEW_NONZERO_LOCATIONS flag 5141 should be used with MAT_USE_HASH_TABLE flag. This option is currently 5142 supported by MATMPIBAIJ format only. 5143 5144 MAT_KEEP_NONZERO_PATTERN indicates when MatZeroRows() is called the zeroed entries 5145 are kept in the nonzero structure 5146 5147 MAT_IGNORE_ZERO_ENTRIES - for AIJ/IS matrices this will stop zero values from creating 5148 a zero location in the matrix 5149 5150 MAT_USE_INODES - indicates using inode version of the code - works with AIJ and 5151 ROWBS matrix types 5152 5153 MAT_NO_OFF_PROC_ZERO_ROWS - you know each process will only zero its own rows. This avoids all reductions in the 5154 zero row routines and thus improves performance for very large process counts. 5155 5156 MAT_IGNORE_LOWER_TRIANGULAR - For SBAIJ matrices will ignore any insertions you make in the lower triangular 5157 part of the matrix (since they should match the upper triangular part). 5158 5159 Notes: Can only be called after MatSetSizes() and MatSetType() have been set. 5160 5161 Level: intermediate 5162 5163 Concepts: matrices^setting options 5164 5165 .seealso: MatOption, Mat 5166 5167 @*/ 5168 PetscErrorCode MatSetOption(Mat mat,MatOption op,PetscBool flg) 5169 { 5170 PetscErrorCode ierr; 5171 5172 PetscFunctionBegin; 5173 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5174 PetscValidType(mat,1); 5175 if (op > 0) { 5176 PetscValidLogicalCollectiveEnum(mat,op,2); 5177 PetscValidLogicalCollectiveBool(mat,flg,3); 5178 } 5179 5180 if (((int) op) <= MAT_OPTION_MIN || ((int) op) >= MAT_OPTION_MAX) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Options %d is out of range",(int)op); 5181 if (!((PetscObject)mat)->type_name) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_TYPENOTSET,"Cannot set options until type and size have been set, see MatSetType() and MatSetSizes()"); 5182 5183 switch (op) { 5184 case MAT_NO_OFF_PROC_ENTRIES: 5185 mat->nooffprocentries = flg; 5186 PetscFunctionReturn(0); 5187 break; 5188 case MAT_NO_OFF_PROC_ZERO_ROWS: 5189 mat->nooffproczerorows = flg; 5190 PetscFunctionReturn(0); 5191 break; 5192 case MAT_SPD: 5193 mat->spd_set = PETSC_TRUE; 5194 mat->spd = flg; 5195 if (flg) { 5196 mat->symmetric = PETSC_TRUE; 5197 mat->structurally_symmetric = PETSC_TRUE; 5198 mat->symmetric_set = PETSC_TRUE; 5199 mat->structurally_symmetric_set = PETSC_TRUE; 5200 } 5201 break; 5202 case MAT_SYMMETRIC: 5203 mat->symmetric = flg; 5204 if (flg) mat->structurally_symmetric = PETSC_TRUE; 5205 mat->symmetric_set = PETSC_TRUE; 5206 mat->structurally_symmetric_set = flg; 5207 break; 5208 case MAT_HERMITIAN: 5209 mat->hermitian = flg; 5210 if (flg) mat->structurally_symmetric = PETSC_TRUE; 5211 mat->hermitian_set = PETSC_TRUE; 5212 mat->structurally_symmetric_set = flg; 5213 break; 5214 case MAT_STRUCTURALLY_SYMMETRIC: 5215 mat->structurally_symmetric = flg; 5216 mat->structurally_symmetric_set = PETSC_TRUE; 5217 break; 5218 case MAT_SYMMETRY_ETERNAL: 5219 mat->symmetric_eternal = flg; 5220 break; 5221 default: 5222 break; 5223 } 5224 if (mat->ops->setoption) { 5225 ierr = (*mat->ops->setoption)(mat,op,flg);CHKERRQ(ierr); 5226 } 5227 PetscFunctionReturn(0); 5228 } 5229 5230 #undef __FUNCT__ 5231 #define __FUNCT__ "MatZeroEntries" 5232 /*@ 5233 MatZeroEntries - Zeros all entries of a matrix. For sparse matrices 5234 this routine retains the old nonzero structure. 5235 5236 Logically Collective on Mat 5237 5238 Input Parameters: 5239 . mat - the matrix 5240 5241 Level: intermediate 5242 5243 Notes: If the matrix was not preallocated then a default, likely poor preallocation will be set in the matrix, so this should be called after the preallocation phase. 5244 See the Performance chapter of the users manual for information on preallocating matrices. 5245 5246 Concepts: matrices^zeroing 5247 5248 .seealso: MatZeroRows() 5249 @*/ 5250 PetscErrorCode MatZeroEntries(Mat mat) 5251 { 5252 PetscErrorCode ierr; 5253 5254 PetscFunctionBegin; 5255 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5256 PetscValidType(mat,1); 5257 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5258 if (mat->insertmode != NOT_SET_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for matrices where you have set values but not yet assembled"); 5259 if (!mat->ops->zeroentries) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 5260 MatCheckPreallocated(mat,1); 5261 5262 ierr = PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 5263 ierr = (*mat->ops->zeroentries)(mat);CHKERRQ(ierr); 5264 ierr = PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 5265 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5266 #if defined(PETSC_HAVE_CUSP) 5267 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5268 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5269 } 5270 #endif 5271 #if defined(PETSC_HAVE_VIENNACL) 5272 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5273 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5274 } 5275 #endif 5276 PetscFunctionReturn(0); 5277 } 5278 5279 #undef __FUNCT__ 5280 #define __FUNCT__ "MatZeroRowsColumns" 5281 /*@C 5282 MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal) 5283 of a set of rows and columns of a matrix. 5284 5285 Collective on Mat 5286 5287 Input Parameters: 5288 + mat - the matrix 5289 . numRows - the number of rows to remove 5290 . rows - the global row indices 5291 . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) 5292 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5293 - b - optional vector of right hand side, that will be adjusted by provided solution 5294 5295 Notes: 5296 This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix. 5297 5298 The user can set a value in the diagonal entry (or for the AIJ and 5299 row formats can optionally remove the main diagonal entry from the 5300 nonzero structure as well, by passing 0.0 as the final argument). 5301 5302 For the parallel case, all processes that share the matrix (i.e., 5303 those in the communicator used for matrix creation) MUST call this 5304 routine, regardless of whether any rows being zeroed are owned by 5305 them. 5306 5307 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5308 list only rows local to itself). 5309 5310 The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine. 5311 5312 Level: intermediate 5313 5314 Concepts: matrices^zeroing rows 5315 5316 .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(), MatZeroRowsColumnsIS() 5317 @*/ 5318 PetscErrorCode MatZeroRowsColumns(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 5319 { 5320 PetscErrorCode ierr; 5321 5322 PetscFunctionBegin; 5323 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5324 PetscValidType(mat,1); 5325 if (numRows) PetscValidIntPointer(rows,3); 5326 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5327 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5328 if (!mat->ops->zerorowscolumns) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 5329 MatCheckPreallocated(mat,1); 5330 5331 ierr = (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5332 if (mat->viewonassembly) { 5333 ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); 5334 ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); 5335 ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); 5336 } 5337 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5338 #if defined(PETSC_HAVE_CUSP) 5339 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5340 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5341 } 5342 #endif 5343 #if defined(PETSC_HAVE_VIENNACL) 5344 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5345 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5346 } 5347 #endif 5348 PetscFunctionReturn(0); 5349 } 5350 5351 #undef __FUNCT__ 5352 #define __FUNCT__ "MatZeroRowsColumnsIS" 5353 /*@C 5354 MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal) 5355 of a set of rows and columns of a matrix. 5356 5357 Collective on Mat 5358 5359 Input Parameters: 5360 + mat - the matrix 5361 . is - the rows to zero 5362 . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) 5363 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5364 - b - optional vector of right hand side, that will be adjusted by provided solution 5365 5366 Notes: 5367 This does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix. 5368 5369 The user can set a value in the diagonal entry (or for the AIJ and 5370 row formats can optionally remove the main diagonal entry from the 5371 nonzero structure as well, by passing 0.0 as the final argument). 5372 5373 For the parallel case, all processes that share the matrix (i.e., 5374 those in the communicator used for matrix creation) MUST call this 5375 routine, regardless of whether any rows being zeroed are owned by 5376 them. 5377 5378 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5379 list only rows local to itself). 5380 5381 The option MAT_NO_OFF_PROC_ZERO_ROWS does not apply to this routine. 5382 5383 Level: intermediate 5384 5385 Concepts: matrices^zeroing rows 5386 5387 .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption(), MatZeroRowsColumns() 5388 @*/ 5389 PetscErrorCode MatZeroRowsColumnsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) 5390 { 5391 PetscErrorCode ierr; 5392 PetscInt numRows; 5393 const PetscInt *rows; 5394 5395 PetscFunctionBegin; 5396 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5397 PetscValidHeaderSpecific(is,IS_CLASSID,2); 5398 PetscValidType(mat,1); 5399 PetscValidType(is,2); 5400 ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); 5401 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 5402 ierr = MatZeroRowsColumns(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5403 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 5404 PetscFunctionReturn(0); 5405 } 5406 5407 #undef __FUNCT__ 5408 #define __FUNCT__ "MatZeroRows" 5409 /*@C 5410 MatZeroRows - Zeros all entries (except possibly the main diagonal) 5411 of a set of rows of a matrix. 5412 5413 Collective on Mat 5414 5415 Input Parameters: 5416 + mat - the matrix 5417 . numRows - the number of rows to remove 5418 . rows - the global row indices 5419 . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) 5420 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5421 - b - optional vector of right hand side, that will be adjusted by provided solution 5422 5423 Notes: 5424 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 5425 but does not release memory. For the dense and block diagonal 5426 formats this does not alter the nonzero structure. 5427 5428 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5429 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5430 merely zeroed. 5431 5432 The user can set a value in the diagonal entry (or for the AIJ and 5433 row formats can optionally remove the main diagonal entry from the 5434 nonzero structure as well, by passing 0.0 as the final argument). 5435 5436 For the parallel case, all processes that share the matrix (i.e., 5437 those in the communicator used for matrix creation) MUST call this 5438 routine, regardless of whether any rows being zeroed are owned by 5439 them. 5440 5441 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5442 list only rows local to itself). 5443 5444 You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it 5445 owns that are to be zeroed. This saves a global synchronization in the implementation. 5446 5447 Level: intermediate 5448 5449 Concepts: matrices^zeroing rows 5450 5451 .seealso: MatZeroRowsIS(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 5452 @*/ 5453 PetscErrorCode MatZeroRows(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 5454 { 5455 PetscErrorCode ierr; 5456 5457 PetscFunctionBegin; 5458 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5459 PetscValidType(mat,1); 5460 if (numRows) PetscValidIntPointer(rows,3); 5461 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5462 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5463 if (!mat->ops->zerorows) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 5464 MatCheckPreallocated(mat,1); 5465 5466 ierr = (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5467 if (mat->viewonassembly) { 5468 ierr = PetscViewerPushFormat(mat->viewonassembly,mat->viewformatonassembly);CHKERRQ(ierr); 5469 ierr = MatView(mat,mat->viewonassembly);CHKERRQ(ierr); 5470 ierr = PetscViewerPopFormat(mat->viewonassembly);CHKERRQ(ierr); 5471 } 5472 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5473 #if defined(PETSC_HAVE_CUSP) 5474 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5475 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5476 } 5477 #endif 5478 #if defined(PETSC_HAVE_VIENNACL) 5479 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5480 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5481 } 5482 #endif 5483 PetscFunctionReturn(0); 5484 } 5485 5486 #undef __FUNCT__ 5487 #define __FUNCT__ "MatZeroRowsIS" 5488 /*@C 5489 MatZeroRowsIS - Zeros all entries (except possibly the main diagonal) 5490 of a set of rows of a matrix. 5491 5492 Collective on Mat 5493 5494 Input Parameters: 5495 + mat - the matrix 5496 . is - index set of rows to remove 5497 . diag - value put in all diagonals of eliminated rows 5498 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5499 - b - optional vector of right hand side, that will be adjusted by provided solution 5500 5501 Notes: 5502 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 5503 but does not release memory. For the dense and block diagonal 5504 formats this does not alter the nonzero structure. 5505 5506 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5507 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5508 merely zeroed. 5509 5510 The user can set a value in the diagonal entry (or for the AIJ and 5511 row formats can optionally remove the main diagonal entry from the 5512 nonzero structure as well, by passing 0.0 as the final argument). 5513 5514 For the parallel case, all processes that share the matrix (i.e., 5515 those in the communicator used for matrix creation) MUST call this 5516 routine, regardless of whether any rows being zeroed are owned by 5517 them. 5518 5519 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5520 list only rows local to itself). 5521 5522 You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it 5523 owns that are to be zeroed. This saves a global synchronization in the implementation. 5524 5525 Level: intermediate 5526 5527 Concepts: matrices^zeroing rows 5528 5529 .seealso: MatZeroRows(), MatZeroRowsStencil(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 5530 @*/ 5531 PetscErrorCode MatZeroRowsIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) 5532 { 5533 PetscInt numRows; 5534 const PetscInt *rows; 5535 PetscErrorCode ierr; 5536 5537 PetscFunctionBegin; 5538 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5539 PetscValidType(mat,1); 5540 PetscValidHeaderSpecific(is,IS_CLASSID,2); 5541 ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); 5542 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 5543 ierr = MatZeroRows(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5544 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 5545 PetscFunctionReturn(0); 5546 } 5547 5548 #undef __FUNCT__ 5549 #define __FUNCT__ "MatZeroRowsStencil" 5550 /*@C 5551 MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal) 5552 of a set of rows of a matrix. These rows must be local to the process. 5553 5554 Collective on Mat 5555 5556 Input Parameters: 5557 + mat - the matrix 5558 . numRows - the number of rows to remove 5559 . rows - the grid coordinates (and component number when dof > 1) for matrix rows 5560 . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) 5561 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5562 - b - optional vector of right hand side, that will be adjusted by provided solution 5563 5564 Notes: 5565 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 5566 but does not release memory. For the dense and block diagonal 5567 formats this does not alter the nonzero structure. 5568 5569 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5570 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5571 merely zeroed. 5572 5573 The user can set a value in the diagonal entry (or for the AIJ and 5574 row formats can optionally remove the main diagonal entry from the 5575 nonzero structure as well, by passing 0.0 as the final argument). 5576 5577 For the parallel case, all processes that share the matrix (i.e., 5578 those in the communicator used for matrix creation) MUST call this 5579 routine, regardless of whether any rows being zeroed are owned by 5580 them. 5581 5582 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5583 list only rows local to itself). 5584 5585 The grid coordinates are across the entire grid, not just the local portion 5586 5587 In Fortran idxm and idxn should be declared as 5588 $ MatStencil idxm(4,m) 5589 and the values inserted using 5590 $ idxm(MatStencil_i,1) = i 5591 $ idxm(MatStencil_j,1) = j 5592 $ idxm(MatStencil_k,1) = k 5593 $ idxm(MatStencil_c,1) = c 5594 etc 5595 5596 For periodic boundary conditions use negative indices for values to the left (below 0; that are to be 5597 obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one 5598 etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the 5599 DMDA_BOUNDARY_PERIODIC boundary type. 5600 5601 For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have 5602 a single value per point) you can skip filling those indices. 5603 5604 Level: intermediate 5605 5606 Concepts: matrices^zeroing rows 5607 5608 .seealso: MatZeroRows(), MatZeroRowsIS(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 5609 @*/ 5610 PetscErrorCode MatZeroRowsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b) 5611 { 5612 PetscInt dim = mat->stencil.dim; 5613 PetscInt sdim = dim - (1 - (PetscInt) mat->stencil.noc); 5614 PetscInt *dims = mat->stencil.dims+1; 5615 PetscInt *starts = mat->stencil.starts; 5616 PetscInt *dxm = (PetscInt*) rows; 5617 PetscInt *jdxm, i, j, tmp, numNewRows = 0; 5618 PetscErrorCode ierr; 5619 5620 PetscFunctionBegin; 5621 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5622 PetscValidType(mat,1); 5623 if (numRows) PetscValidIntPointer(rows,3); 5624 5625 ierr = PetscMalloc(numRows*sizeof(PetscInt), &jdxm);CHKERRQ(ierr); 5626 for (i = 0; i < numRows; ++i) { 5627 /* Skip unused dimensions (they are ordered k, j, i, c) */ 5628 for (j = 0; j < 3-sdim; ++j) dxm++; 5629 /* Local index in X dir */ 5630 tmp = *dxm++ - starts[0]; 5631 /* Loop over remaining dimensions */ 5632 for (j = 0; j < dim-1; ++j) { 5633 /* If nonlocal, set index to be negative */ 5634 if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT; 5635 /* Update local index */ 5636 else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; 5637 } 5638 /* Skip component slot if necessary */ 5639 if (mat->stencil.noc) dxm++; 5640 /* Local row number */ 5641 if (tmp >= 0) { 5642 jdxm[numNewRows++] = tmp; 5643 } 5644 } 5645 ierr = MatZeroRowsLocal(mat,numNewRows,jdxm,diag,x,b);CHKERRQ(ierr); 5646 ierr = PetscFree(jdxm);CHKERRQ(ierr); 5647 PetscFunctionReturn(0); 5648 } 5649 5650 #undef __FUNCT__ 5651 #define __FUNCT__ "MatZeroRowsColumnsStencil" 5652 /*@C 5653 MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal) 5654 of a set of rows and columns of a matrix. 5655 5656 Collective on Mat 5657 5658 Input Parameters: 5659 + mat - the matrix 5660 . numRows - the number of rows/columns to remove 5661 . rows - the grid coordinates (and component number when dof > 1) for matrix rows 5662 . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry) 5663 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5664 - b - optional vector of right hand side, that will be adjusted by provided solution 5665 5666 Notes: 5667 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 5668 but does not release memory. For the dense and block diagonal 5669 formats this does not alter the nonzero structure. 5670 5671 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5672 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5673 merely zeroed. 5674 5675 The user can set a value in the diagonal entry (or for the AIJ and 5676 row formats can optionally remove the main diagonal entry from the 5677 nonzero structure as well, by passing 0.0 as the final argument). 5678 5679 For the parallel case, all processes that share the matrix (i.e., 5680 those in the communicator used for matrix creation) MUST call this 5681 routine, regardless of whether any rows being zeroed are owned by 5682 them. 5683 5684 Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to 5685 list only rows local to itself, but the row/column numbers are given in local numbering). 5686 5687 The grid coordinates are across the entire grid, not just the local portion 5688 5689 In Fortran idxm and idxn should be declared as 5690 $ MatStencil idxm(4,m) 5691 and the values inserted using 5692 $ idxm(MatStencil_i,1) = i 5693 $ idxm(MatStencil_j,1) = j 5694 $ idxm(MatStencil_k,1) = k 5695 $ idxm(MatStencil_c,1) = c 5696 etc 5697 5698 For periodic boundary conditions use negative indices for values to the left (below 0; that are to be 5699 obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one 5700 etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the 5701 DMDA_BOUNDARY_PERIODIC boundary type. 5702 5703 For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have 5704 a single value per point) you can skip filling those indices. 5705 5706 Level: intermediate 5707 5708 Concepts: matrices^zeroing rows 5709 5710 .seealso: MatZeroRows(), MatZeroRowsIS(), MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 5711 @*/ 5712 PetscErrorCode MatZeroRowsColumnsStencil(Mat mat,PetscInt numRows,const MatStencil rows[],PetscScalar diag,Vec x,Vec b) 5713 { 5714 PetscInt dim = mat->stencil.dim; 5715 PetscInt sdim = dim - (1 - (PetscInt) mat->stencil.noc); 5716 PetscInt *dims = mat->stencil.dims+1; 5717 PetscInt *starts = mat->stencil.starts; 5718 PetscInt *dxm = (PetscInt*) rows; 5719 PetscInt *jdxm, i, j, tmp, numNewRows = 0; 5720 PetscErrorCode ierr; 5721 5722 PetscFunctionBegin; 5723 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5724 PetscValidType(mat,1); 5725 if (numRows) PetscValidIntPointer(rows,3); 5726 5727 ierr = PetscMalloc(numRows*sizeof(PetscInt), &jdxm);CHKERRQ(ierr); 5728 for (i = 0; i < numRows; ++i) { 5729 /* Skip unused dimensions (they are ordered k, j, i, c) */ 5730 for (j = 0; j < 3-sdim; ++j) dxm++; 5731 /* Local index in X dir */ 5732 tmp = *dxm++ - starts[0]; 5733 /* Loop over remaining dimensions */ 5734 for (j = 0; j < dim-1; ++j) { 5735 /* If nonlocal, set index to be negative */ 5736 if ((*dxm++ - starts[j+1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT; 5737 /* Update local index */ 5738 else tmp = tmp*dims[j] + *(dxm-1) - starts[j+1]; 5739 } 5740 /* Skip component slot if necessary */ 5741 if (mat->stencil.noc) dxm++; 5742 /* Local row number */ 5743 if (tmp >= 0) { 5744 jdxm[numNewRows++] = tmp; 5745 } 5746 } 5747 ierr = MatZeroRowsColumnsLocal(mat,numNewRows,jdxm,diag,x,b);CHKERRQ(ierr); 5748 ierr = PetscFree(jdxm);CHKERRQ(ierr); 5749 PetscFunctionReturn(0); 5750 } 5751 5752 #undef __FUNCT__ 5753 #define __FUNCT__ "MatZeroRowsLocal" 5754 /*@C 5755 MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal) 5756 of a set of rows of a matrix; using local numbering of rows. 5757 5758 Collective on Mat 5759 5760 Input Parameters: 5761 + mat - the matrix 5762 . numRows - the number of rows to remove 5763 . rows - the global row indices 5764 . diag - value put in all diagonals of eliminated rows 5765 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5766 - b - optional vector of right hand side, that will be adjusted by provided solution 5767 5768 Notes: 5769 Before calling MatZeroRowsLocal(), the user must first set the 5770 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 5771 5772 For the AIJ matrix formats this removes the old nonzero structure, 5773 but does not release memory. For the dense and block diagonal 5774 formats this does not alter the nonzero structure. 5775 5776 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5777 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5778 merely zeroed. 5779 5780 The user can set a value in the diagonal entry (or for the AIJ and 5781 row formats can optionally remove the main diagonal entry from the 5782 nonzero structure as well, by passing 0.0 as the final argument). 5783 5784 You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it 5785 owns that are to be zeroed. This saves a global synchronization in the implementation. 5786 5787 Level: intermediate 5788 5789 Concepts: matrices^zeroing 5790 5791 .seealso: MatZeroRows(), MatZeroRowsLocalIS(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 5792 @*/ 5793 PetscErrorCode MatZeroRowsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 5794 { 5795 PetscErrorCode ierr; 5796 PetscMPIInt size; 5797 5798 PetscFunctionBegin; 5799 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5800 PetscValidType(mat,1); 5801 if (numRows) PetscValidIntPointer(rows,3); 5802 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5803 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5804 MatCheckPreallocated(mat,1); 5805 5806 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 5807 if (mat->ops->zerorowslocal) { 5808 ierr = (*mat->ops->zerorowslocal)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5809 } else if (size == 1) { 5810 ierr = (*mat->ops->zerorows)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5811 } else { 5812 IS is, newis; 5813 const PetscInt *newRows; 5814 5815 if (!mat->rmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); 5816 ierr = ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);CHKERRQ(ierr); 5817 ierr = ISLocalToGlobalMappingApplyIS(mat->rmap->mapping,is,&newis);CHKERRQ(ierr); 5818 ierr = ISGetIndices(newis,&newRows);CHKERRQ(ierr); 5819 ierr = (*mat->ops->zerorows)(mat,numRows,newRows,diag,x,b);CHKERRQ(ierr); 5820 ierr = ISRestoreIndices(newis,&newRows);CHKERRQ(ierr); 5821 ierr = ISDestroy(&newis);CHKERRQ(ierr); 5822 ierr = ISDestroy(&is);CHKERRQ(ierr); 5823 } 5824 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5825 #if defined(PETSC_HAVE_CUSP) 5826 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5827 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5828 } 5829 #endif 5830 #if defined(PETSC_HAVE_VIENNACL) 5831 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5832 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5833 } 5834 #endif 5835 PetscFunctionReturn(0); 5836 } 5837 5838 #undef __FUNCT__ 5839 #define __FUNCT__ "MatZeroRowsLocalIS" 5840 /*@C 5841 MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal) 5842 of a set of rows of a matrix; using local numbering of rows. 5843 5844 Collective on Mat 5845 5846 Input Parameters: 5847 + mat - the matrix 5848 . is - index set of rows to remove 5849 . diag - value put in all diagonals of eliminated rows 5850 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5851 - b - optional vector of right hand side, that will be adjusted by provided solution 5852 5853 Notes: 5854 Before calling MatZeroRowsLocalIS(), the user must first set the 5855 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 5856 5857 For the AIJ matrix formats this removes the old nonzero structure, 5858 but does not release memory. For the dense and block diagonal 5859 formats this does not alter the nonzero structure. 5860 5861 If the option MatSetOption(mat,MAT_KEEP_NONZERO_PATTERN,PETSC_TRUE) the nonzero structure 5862 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 5863 merely zeroed. 5864 5865 The user can set a value in the diagonal entry (or for the AIJ and 5866 row formats can optionally remove the main diagonal entry from the 5867 nonzero structure as well, by passing 0.0 as the final argument). 5868 5869 You can call MatSetOption(mat,MAT_NO_OFF_PROC_ZERO_ROWS,PETSC_TRUE) if each process indicates only rows it 5870 owns that are to be zeroed. This saves a global synchronization in the implementation. 5871 5872 Level: intermediate 5873 5874 Concepts: matrices^zeroing 5875 5876 .seealso: MatZeroRows(), MatZeroRowsLocal(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 5877 @*/ 5878 PetscErrorCode MatZeroRowsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) 5879 { 5880 PetscErrorCode ierr; 5881 PetscInt numRows; 5882 const PetscInt *rows; 5883 5884 PetscFunctionBegin; 5885 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5886 PetscValidType(mat,1); 5887 PetscValidHeaderSpecific(is,IS_CLASSID,2); 5888 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5889 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5890 MatCheckPreallocated(mat,1); 5891 5892 ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); 5893 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 5894 ierr = MatZeroRowsLocal(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5895 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 5896 PetscFunctionReturn(0); 5897 } 5898 5899 #undef __FUNCT__ 5900 #define __FUNCT__ "MatZeroRowsColumnsLocal" 5901 /*@C 5902 MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal) 5903 of a set of rows and columns of a matrix; using local numbering of rows. 5904 5905 Collective on Mat 5906 5907 Input Parameters: 5908 + mat - the matrix 5909 . numRows - the number of rows to remove 5910 . rows - the global row indices 5911 . diag - value put in all diagonals of eliminated rows 5912 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5913 - b - optional vector of right hand side, that will be adjusted by provided solution 5914 5915 Notes: 5916 Before calling MatZeroRowsColumnsLocal(), the user must first set the 5917 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 5918 5919 The user can set a value in the diagonal entry (or for the AIJ and 5920 row formats can optionally remove the main diagonal entry from the 5921 nonzero structure as well, by passing 0.0 as the final argument). 5922 5923 Level: intermediate 5924 5925 Concepts: matrices^zeroing 5926 5927 .seealso: MatZeroRows(), MatZeroRowsLocalIS(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 5928 @*/ 5929 PetscErrorCode MatZeroRowsColumnsLocal(Mat mat,PetscInt numRows,const PetscInt rows[],PetscScalar diag,Vec x,Vec b) 5930 { 5931 PetscErrorCode ierr; 5932 PetscMPIInt size; 5933 5934 PetscFunctionBegin; 5935 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 5936 PetscValidType(mat,1); 5937 if (numRows) PetscValidIntPointer(rows,3); 5938 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5939 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5940 MatCheckPreallocated(mat,1); 5941 5942 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 5943 if (size == 1) { 5944 ierr = (*mat->ops->zerorowscolumns)(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 5945 } else { 5946 IS is, newis; 5947 const PetscInt *newRows; 5948 5949 if (!mat->cmap->mapping) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); 5950 ierr = ISCreateGeneral(PETSC_COMM_SELF,numRows,rows,PETSC_COPY_VALUES,&is);CHKERRQ(ierr); 5951 ierr = ISLocalToGlobalMappingApplyIS(mat->cmap->mapping,is,&newis);CHKERRQ(ierr); 5952 ierr = ISGetIndices(newis,&newRows);CHKERRQ(ierr); 5953 ierr = (*mat->ops->zerorowscolumns)(mat,numRows,newRows,diag,x,b);CHKERRQ(ierr); 5954 ierr = ISRestoreIndices(newis,&newRows);CHKERRQ(ierr); 5955 ierr = ISDestroy(&newis);CHKERRQ(ierr); 5956 ierr = ISDestroy(&is);CHKERRQ(ierr); 5957 } 5958 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 5959 #if defined(PETSC_HAVE_CUSP) 5960 if (mat->valid_GPU_matrix != PETSC_CUSP_UNALLOCATED) { 5961 mat->valid_GPU_matrix = PETSC_CUSP_CPU; 5962 } 5963 #endif 5964 #if defined(PETSC_HAVE_VIENNACL) 5965 if (mat->valid_GPU_matrix != PETSC_VIENNACL_UNALLOCATED) { 5966 mat->valid_GPU_matrix = PETSC_VIENNACL_CPU; 5967 } 5968 #endif 5969 PetscFunctionReturn(0); 5970 } 5971 5972 #undef __FUNCT__ 5973 #define __FUNCT__ "MatZeroRowsColumnsLocalIS" 5974 /*@C 5975 MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal) 5976 of a set of rows and columns of a matrix; using local numbering of rows. 5977 5978 Collective on Mat 5979 5980 Input Parameters: 5981 + mat - the matrix 5982 . is - index set of rows to remove 5983 . diag - value put in all diagonals of eliminated rows 5984 . x - optional vector of solutions for zeroed rows (other entries in vector are not used) 5985 - b - optional vector of right hand side, that will be adjusted by provided solution 5986 5987 Notes: 5988 Before calling MatZeroRowsColumnsLocalIS(), the user must first set the 5989 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 5990 5991 The user can set a value in the diagonal entry (or for the AIJ and 5992 row formats can optionally remove the main diagonal entry from the 5993 nonzero structure as well, by passing 0.0 as the final argument). 5994 5995 Level: intermediate 5996 5997 Concepts: matrices^zeroing 5998 5999 .seealso: MatZeroRows(), MatZeroRowsLocal(), MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 6000 @*/ 6001 PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat,IS is,PetscScalar diag,Vec x,Vec b) 6002 { 6003 PetscErrorCode ierr; 6004 PetscInt numRows; 6005 const PetscInt *rows; 6006 6007 PetscFunctionBegin; 6008 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6009 PetscValidType(mat,1); 6010 PetscValidHeaderSpecific(is,IS_CLASSID,2); 6011 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6012 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6013 MatCheckPreallocated(mat,1); 6014 6015 ierr = ISGetLocalSize(is,&numRows);CHKERRQ(ierr); 6016 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 6017 ierr = MatZeroRowsColumnsLocal(mat,numRows,rows,diag,x,b);CHKERRQ(ierr); 6018 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 6019 PetscFunctionReturn(0); 6020 } 6021 6022 #undef __FUNCT__ 6023 #define __FUNCT__ "MatGetSize" 6024 /*@ 6025 MatGetSize - Returns the numbers of rows and columns in a matrix. 6026 6027 Not Collective 6028 6029 Input Parameter: 6030 . mat - the matrix 6031 6032 Output Parameters: 6033 + m - the number of global rows 6034 - n - the number of global columns 6035 6036 Note: both output parameters can be NULL on input. 6037 6038 Level: beginner 6039 6040 Concepts: matrices^size 6041 6042 .seealso: MatGetLocalSize() 6043 @*/ 6044 PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt *n) 6045 { 6046 PetscFunctionBegin; 6047 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6048 if (m) *m = mat->rmap->N; 6049 if (n) *n = mat->cmap->N; 6050 PetscFunctionReturn(0); 6051 } 6052 6053 #undef __FUNCT__ 6054 #define __FUNCT__ "MatGetLocalSize" 6055 /*@ 6056 MatGetLocalSize - Returns the number of rows and columns in a matrix 6057 stored locally. This information may be implementation dependent, so 6058 use with care. 6059 6060 Not Collective 6061 6062 Input Parameters: 6063 . mat - the matrix 6064 6065 Output Parameters: 6066 + m - the number of local rows 6067 - n - the number of local columns 6068 6069 Note: both output parameters can be NULL on input. 6070 6071 Level: beginner 6072 6073 Concepts: matrices^local size 6074 6075 .seealso: MatGetSize() 6076 @*/ 6077 PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt *n) 6078 { 6079 PetscFunctionBegin; 6080 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6081 if (m) PetscValidIntPointer(m,2); 6082 if (n) PetscValidIntPointer(n,3); 6083 if (m) *m = mat->rmap->n; 6084 if (n) *n = mat->cmap->n; 6085 PetscFunctionReturn(0); 6086 } 6087 6088 #undef __FUNCT__ 6089 #define __FUNCT__ "MatGetOwnershipRangeColumn" 6090 /*@ 6091 MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by 6092 this processor. (The columns of the "diagonal block") 6093 6094 Not Collective, unless matrix has not been allocated, then collective on Mat 6095 6096 Input Parameters: 6097 . mat - the matrix 6098 6099 Output Parameters: 6100 + m - the global index of the first local column 6101 - n - one more than the global index of the last local column 6102 6103 Notes: both output parameters can be NULL on input. 6104 6105 Level: developer 6106 6107 Concepts: matrices^column ownership 6108 6109 .seealso: MatGetOwnershipRange(), MatGetOwnershipRanges(), MatGetOwnershipRangesColumn() 6110 6111 @*/ 6112 PetscErrorCode MatGetOwnershipRangeColumn(Mat mat,PetscInt *m,PetscInt *n) 6113 { 6114 PetscFunctionBegin; 6115 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6116 PetscValidType(mat,1); 6117 if (m) PetscValidIntPointer(m,2); 6118 if (n) PetscValidIntPointer(n,3); 6119 MatCheckPreallocated(mat,1); 6120 if (m) *m = mat->cmap->rstart; 6121 if (n) *n = mat->cmap->rend; 6122 PetscFunctionReturn(0); 6123 } 6124 6125 #undef __FUNCT__ 6126 #define __FUNCT__ "MatGetOwnershipRange" 6127 /*@ 6128 MatGetOwnershipRange - Returns the range of matrix rows owned by 6129 this processor, assuming that the matrix is laid out with the first 6130 n1 rows on the first processor, the next n2 rows on the second, etc. 6131 For certain parallel layouts this range may not be well defined. 6132 6133 Not Collective 6134 6135 Input Parameters: 6136 . mat - the matrix 6137 6138 Output Parameters: 6139 + m - the global index of the first local row 6140 - n - one more than the global index of the last local row 6141 6142 Note: Both output parameters can be NULL on input. 6143 $ This function requires that the matrix be preallocated. If you have not preallocated, consider using 6144 $ PetscSplitOwnership(MPI_Comm comm, PetscInt *n, PetscInt *N) 6145 $ and then MPI_Scan() to calculate prefix sums of the local sizes. 6146 6147 Level: beginner 6148 6149 Concepts: matrices^row ownership 6150 6151 .seealso: MatGetOwnershipRanges(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn(), PetscSplitOwnership(), PetscSplitOwnershipBlock() 6152 6153 @*/ 6154 PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt *n) 6155 { 6156 PetscFunctionBegin; 6157 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6158 PetscValidType(mat,1); 6159 if (m) PetscValidIntPointer(m,2); 6160 if (n) PetscValidIntPointer(n,3); 6161 MatCheckPreallocated(mat,1); 6162 if (m) *m = mat->rmap->rstart; 6163 if (n) *n = mat->rmap->rend; 6164 PetscFunctionReturn(0); 6165 } 6166 6167 #undef __FUNCT__ 6168 #define __FUNCT__ "MatGetOwnershipRanges" 6169 /*@C 6170 MatGetOwnershipRanges - Returns the range of matrix rows owned by 6171 each process 6172 6173 Not Collective, unless matrix has not been allocated, then collective on Mat 6174 6175 Input Parameters: 6176 . mat - the matrix 6177 6178 Output Parameters: 6179 . ranges - start of each processors portion plus one more then the total length at the end 6180 6181 Level: beginner 6182 6183 Concepts: matrices^row ownership 6184 6185 .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRangesColumn() 6186 6187 @*/ 6188 PetscErrorCode MatGetOwnershipRanges(Mat mat,const PetscInt **ranges) 6189 { 6190 PetscErrorCode ierr; 6191 6192 PetscFunctionBegin; 6193 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6194 PetscValidType(mat,1); 6195 MatCheckPreallocated(mat,1); 6196 ierr = PetscLayoutGetRanges(mat->rmap,ranges);CHKERRQ(ierr); 6197 PetscFunctionReturn(0); 6198 } 6199 6200 #undef __FUNCT__ 6201 #define __FUNCT__ "MatGetOwnershipRangesColumn" 6202 /*@C 6203 MatGetOwnershipRangesColumn - Returns the range of matrix columns associated with rows of a vector one multiplies by that owned by 6204 this processor. (The columns of the "diagonal blocks" for each process) 6205 6206 Not Collective, unless matrix has not been allocated, then collective on Mat 6207 6208 Input Parameters: 6209 . mat - the matrix 6210 6211 Output Parameters: 6212 . ranges - start of each processors portion plus one more then the total length at the end 6213 6214 Level: beginner 6215 6216 Concepts: matrices^column ownership 6217 6218 .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatGetOwnershipRanges() 6219 6220 @*/ 6221 PetscErrorCode MatGetOwnershipRangesColumn(Mat mat,const PetscInt **ranges) 6222 { 6223 PetscErrorCode ierr; 6224 6225 PetscFunctionBegin; 6226 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6227 PetscValidType(mat,1); 6228 MatCheckPreallocated(mat,1); 6229 ierr = PetscLayoutGetRanges(mat->cmap,ranges);CHKERRQ(ierr); 6230 PetscFunctionReturn(0); 6231 } 6232 6233 #undef __FUNCT__ 6234 #define __FUNCT__ "MatGetOwnershipIS" 6235 /*@C 6236 MatGetOwnershipIS - Get row and column ownership as index sets 6237 6238 Not Collective 6239 6240 Input Arguments: 6241 . A - matrix of type Elemental 6242 6243 Output Arguments: 6244 + rows - rows in which this process owns elements 6245 . cols - columns in which this process owns elements 6246 6247 Level: intermediate 6248 6249 .seealso: MatGetOwnershipRange(), MatGetOwnershipRangeColumn(), MatSetValues(), MATELEMENTAL, MatSetValues() 6250 @*/ 6251 PetscErrorCode MatGetOwnershipIS(Mat A,IS *rows,IS *cols) 6252 { 6253 PetscErrorCode ierr,(*f)(Mat,IS*,IS*); 6254 6255 PetscFunctionBegin; 6256 MatCheckPreallocated(A,1); 6257 ierr = PetscObjectQueryFunction((PetscObject)A,"MatGetOwnershipIS_C",&f);CHKERRQ(ierr); 6258 if (f) { 6259 ierr = (*f)(A,rows,cols);CHKERRQ(ierr); 6260 } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */ 6261 if (rows) {ierr = ISCreateStride(PETSC_COMM_SELF,A->rmap->n,A->rmap->rstart,1,rows);CHKERRQ(ierr);} 6262 if (cols) {ierr = ISCreateStride(PETSC_COMM_SELF,A->cmap->N,0,1,cols);CHKERRQ(ierr);} 6263 } 6264 PetscFunctionReturn(0); 6265 } 6266 6267 #undef __FUNCT__ 6268 #define __FUNCT__ "MatILUFactorSymbolic" 6269 /*@C 6270 MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix. 6271 Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric() 6272 to complete the factorization. 6273 6274 Collective on Mat 6275 6276 Input Parameters: 6277 + mat - the matrix 6278 . row - row permutation 6279 . column - column permutation 6280 - info - structure containing 6281 $ levels - number of levels of fill. 6282 $ expected fill - as ratio of original fill. 6283 $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices 6284 missing diagonal entries) 6285 6286 Output Parameters: 6287 . fact - new matrix that has been symbolically factored 6288 6289 Notes: 6290 See the <a href="../../docs/manual.pdf">users manual</a> for additional information about 6291 choosing the fill factor for better efficiency. 6292 6293 Most users should employ the simplified KSP interface for linear solvers 6294 instead of working directly with matrix algebra routines such as this. 6295 See, e.g., KSPCreate(). 6296 6297 Level: developer 6298 6299 Concepts: matrices^symbolic LU factorization 6300 Concepts: matrices^factorization 6301 Concepts: LU^symbolic factorization 6302 6303 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 6304 MatGetOrdering(), MatFactorInfo 6305 6306 Developer Note: fortran interface is not autogenerated as the f90 6307 interface defintion cannot be generated correctly [due to MatFactorInfo] 6308 6309 @*/ 6310 PetscErrorCode MatILUFactorSymbolic(Mat fact,Mat mat,IS row,IS col,const MatFactorInfo *info) 6311 { 6312 PetscErrorCode ierr; 6313 6314 PetscFunctionBegin; 6315 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6316 PetscValidType(mat,1); 6317 PetscValidHeaderSpecific(row,IS_CLASSID,2); 6318 PetscValidHeaderSpecific(col,IS_CLASSID,3); 6319 PetscValidPointer(info,4); 6320 PetscValidPointer(fact,5); 6321 if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels); 6322 if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %G",info->fill); 6323 if (!(fact)->ops->ilufactorsymbolic) { 6324 const MatSolverPackage spackage; 6325 ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); 6326 SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ILU using solver package %s",((PetscObject)mat)->type_name,spackage); 6327 } 6328 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6329 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6330 MatCheckPreallocated(mat,2); 6331 6332 ierr = PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 6333 ierr = (fact->ops->ilufactorsymbolic)(fact,mat,row,col,info);CHKERRQ(ierr); 6334 ierr = PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 6335 PetscFunctionReturn(0); 6336 } 6337 6338 #undef __FUNCT__ 6339 #define __FUNCT__ "MatICCFactorSymbolic" 6340 /*@C 6341 MatICCFactorSymbolic - Performs symbolic incomplete 6342 Cholesky factorization for a symmetric matrix. Use 6343 MatCholeskyFactorNumeric() to complete the factorization. 6344 6345 Collective on Mat 6346 6347 Input Parameters: 6348 + mat - the matrix 6349 . perm - row and column permutation 6350 - info - structure containing 6351 $ levels - number of levels of fill. 6352 $ expected fill - as ratio of original fill. 6353 6354 Output Parameter: 6355 . fact - the factored matrix 6356 6357 Notes: 6358 Most users should employ the KSP interface for linear solvers 6359 instead of working directly with matrix algebra routines such as this. 6360 See, e.g., KSPCreate(). 6361 6362 Level: developer 6363 6364 Concepts: matrices^symbolic incomplete Cholesky factorization 6365 Concepts: matrices^factorization 6366 Concepts: Cholsky^symbolic factorization 6367 6368 .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 6369 6370 Developer Note: fortran interface is not autogenerated as the f90 6371 interface defintion cannot be generated correctly [due to MatFactorInfo] 6372 6373 @*/ 6374 PetscErrorCode MatICCFactorSymbolic(Mat fact,Mat mat,IS perm,const MatFactorInfo *info) 6375 { 6376 PetscErrorCode ierr; 6377 6378 PetscFunctionBegin; 6379 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6380 PetscValidType(mat,1); 6381 PetscValidHeaderSpecific(perm,IS_CLASSID,2); 6382 PetscValidPointer(info,3); 6383 PetscValidPointer(fact,4); 6384 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6385 if (info->levels < 0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels); 6386 if (info->fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %G",info->fill); 6387 if (!(fact)->ops->iccfactorsymbolic) { 6388 const MatSolverPackage spackage; 6389 ierr = MatFactorGetSolverPackage(fact,&spackage);CHKERRQ(ierr); 6390 SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Matrix type %s symbolic ICC using solver package %s",((PetscObject)mat)->type_name,spackage); 6391 } 6392 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6393 MatCheckPreallocated(mat,2); 6394 6395 ierr = PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 6396 ierr = (fact->ops->iccfactorsymbolic)(fact,mat,perm,info);CHKERRQ(ierr); 6397 ierr = PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 6398 PetscFunctionReturn(0); 6399 } 6400 6401 #undef __FUNCT__ 6402 #define __FUNCT__ "MatGetSubMatrices" 6403 /*@C 6404 MatGetSubMatrices - Extracts several submatrices from a matrix. If submat 6405 points to an array of valid matrices, they may be reused to store the new 6406 submatrices. 6407 6408 Collective on Mat 6409 6410 Input Parameters: 6411 + mat - the matrix 6412 . n - the number of submatrixes to be extracted (on this processor, may be zero) 6413 . irow, icol - index sets of rows and columns to extract (must be sorted) 6414 - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 6415 6416 Output Parameter: 6417 . submat - the array of submatrices 6418 6419 Notes: 6420 MatGetSubMatrices() can extract ONLY sequential submatrices 6421 (from both sequential and parallel matrices). Use MatGetSubMatrix() 6422 to extract a parallel submatrix. 6423 6424 Currently both row and column indices must be sorted to guarantee 6425 correctness with all matrix types. 6426 6427 When extracting submatrices from a parallel matrix, each processor can 6428 form a different submatrix by setting the rows and columns of its 6429 individual index sets according to the local submatrix desired. 6430 6431 When finished using the submatrices, the user should destroy 6432 them with MatDestroyMatrices(). 6433 6434 MAT_REUSE_MATRIX can only be used when the nonzero structure of the 6435 original matrix has not changed from that last call to MatGetSubMatrices(). 6436 6437 This routine creates the matrices in submat; you should NOT create them before 6438 calling it. It also allocates the array of matrix pointers submat. 6439 6440 For BAIJ matrices the index sets must respect the block structure, that is if they 6441 request one row/column in a block, they must request all rows/columns that are in 6442 that block. For example, if the block size is 2 you cannot request just row 0 and 6443 column 0. 6444 6445 Fortran Note: 6446 The Fortran interface is slightly different from that given below; it 6447 requires one to pass in as submat a Mat (integer) array of size at least m. 6448 6449 Level: advanced 6450 6451 Concepts: matrices^accessing submatrices 6452 Concepts: submatrices 6453 6454 .seealso: MatDestroyMatrices(), MatGetSubMatrix(), MatGetRow(), MatGetDiagonal(), MatReuse 6455 @*/ 6456 PetscErrorCode MatGetSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) 6457 { 6458 PetscErrorCode ierr; 6459 PetscInt i; 6460 PetscBool eq; 6461 6462 PetscFunctionBegin; 6463 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6464 PetscValidType(mat,1); 6465 if (n) { 6466 PetscValidPointer(irow,3); 6467 PetscValidHeaderSpecific(*irow,IS_CLASSID,3); 6468 PetscValidPointer(icol,4); 6469 PetscValidHeaderSpecific(*icol,IS_CLASSID,4); 6470 } 6471 PetscValidPointer(submat,6); 6472 if (n && scall == MAT_REUSE_MATRIX) { 6473 PetscValidPointer(*submat,6); 6474 PetscValidHeaderSpecific(**submat,MAT_CLASSID,6); 6475 } 6476 if (!mat->ops->getsubmatrices) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 6477 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6478 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6479 MatCheckPreallocated(mat,1); 6480 6481 ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 6482 ierr = (*mat->ops->getsubmatrices)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); 6483 ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 6484 for (i=0; i<n; i++) { 6485 if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) { 6486 ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); 6487 if (eq) { 6488 if (mat->symmetric) { 6489 ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); 6490 } else if (mat->hermitian) { 6491 ierr = MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr); 6492 } else if (mat->structurally_symmetric) { 6493 ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); 6494 } 6495 } 6496 } 6497 } 6498 PetscFunctionReturn(0); 6499 } 6500 6501 #undef __FUNCT__ 6502 #define __FUNCT__ "MatGetSubMatricesParallel" 6503 PetscErrorCode MatGetSubMatricesParallel(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) 6504 { 6505 PetscErrorCode ierr; 6506 PetscInt i; 6507 PetscBool eq; 6508 6509 PetscFunctionBegin; 6510 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6511 PetscValidType(mat,1); 6512 if (n) { 6513 PetscValidPointer(irow,3); 6514 PetscValidHeaderSpecific(*irow,IS_CLASSID,3); 6515 PetscValidPointer(icol,4); 6516 PetscValidHeaderSpecific(*icol,IS_CLASSID,4); 6517 } 6518 PetscValidPointer(submat,6); 6519 if (n && scall == MAT_REUSE_MATRIX) { 6520 PetscValidPointer(*submat,6); 6521 PetscValidHeaderSpecific(**submat,MAT_CLASSID,6); 6522 } 6523 if (!mat->ops->getsubmatricesparallel) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 6524 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6525 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6526 MatCheckPreallocated(mat,1); 6527 6528 ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 6529 ierr = (*mat->ops->getsubmatricesparallel)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); 6530 ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 6531 for (i=0; i<n; i++) { 6532 if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) { 6533 ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); 6534 if (eq) { 6535 if (mat->symmetric) { 6536 ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); 6537 } else if (mat->hermitian) { 6538 ierr = MatSetOption((*submat)[i],MAT_HERMITIAN,PETSC_TRUE);CHKERRQ(ierr); 6539 } else if (mat->structurally_symmetric) { 6540 ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);CHKERRQ(ierr); 6541 } 6542 } 6543 } 6544 } 6545 PetscFunctionReturn(0); 6546 } 6547 6548 #undef __FUNCT__ 6549 #define __FUNCT__ "MatDestroyMatrices" 6550 /*@C 6551 MatDestroyMatrices - Destroys a set of matrices obtained with MatGetSubMatrices(). 6552 6553 Collective on Mat 6554 6555 Input Parameters: 6556 + n - the number of local matrices 6557 - mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling 6558 sequence of MatGetSubMatrices()) 6559 6560 Level: advanced 6561 6562 Notes: Frees not only the matrices, but also the array that contains the matrices 6563 In Fortran will not free the array. 6564 6565 .seealso: MatGetSubMatrices() 6566 @*/ 6567 PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[]) 6568 { 6569 PetscErrorCode ierr; 6570 PetscInt i; 6571 6572 PetscFunctionBegin; 6573 if (!*mat) PetscFunctionReturn(0); 6574 if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n); 6575 PetscValidPointer(mat,2); 6576 for (i=0; i<n; i++) { 6577 ierr = MatDestroy(&(*mat)[i]);CHKERRQ(ierr); 6578 } 6579 /* memory is allocated even if n = 0 */ 6580 ierr = PetscFree(*mat);CHKERRQ(ierr); 6581 *mat = NULL; 6582 PetscFunctionReturn(0); 6583 } 6584 6585 #undef __FUNCT__ 6586 #define __FUNCT__ "MatGetSeqNonzeroStructure" 6587 /*@C 6588 MatGetSeqNonzeroStructure - Extracts the sequential nonzero structure from a matrix. 6589 6590 Collective on Mat 6591 6592 Input Parameters: 6593 . mat - the matrix 6594 6595 Output Parameter: 6596 . matstruct - the sequential matrix with the nonzero structure of mat 6597 6598 Level: intermediate 6599 6600 .seealso: MatDestroySeqNonzeroStructure(), MatGetSubMatrices(), MatDestroyMatrices() 6601 @*/ 6602 PetscErrorCode MatGetSeqNonzeroStructure(Mat mat,Mat *matstruct) 6603 { 6604 PetscErrorCode ierr; 6605 6606 PetscFunctionBegin; 6607 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6608 PetscValidPointer(matstruct,2); 6609 6610 PetscValidType(mat,1); 6611 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6612 MatCheckPreallocated(mat,1); 6613 6614 if (!mat->ops->getseqnonzerostructure) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Not for matrix type %s\n",((PetscObject)mat)->type_name); 6615 ierr = PetscLogEventBegin(MAT_GetSeqNonzeroStructure,mat,0,0,0);CHKERRQ(ierr); 6616 ierr = (*mat->ops->getseqnonzerostructure)(mat,matstruct);CHKERRQ(ierr); 6617 ierr = PetscLogEventEnd(MAT_GetSeqNonzeroStructure,mat,0,0,0);CHKERRQ(ierr); 6618 PetscFunctionReturn(0); 6619 } 6620 6621 #undef __FUNCT__ 6622 #define __FUNCT__ "MatDestroySeqNonzeroStructure" 6623 /*@C 6624 MatDestroySeqNonzeroStructure - Destroys matrix obtained with MatGetSeqNonzeroStructure(). 6625 6626 Collective on Mat 6627 6628 Input Parameters: 6629 . mat - the matrix (note that this is a pointer to the array of matrices, just to match the calling 6630 sequence of MatGetSequentialNonzeroStructure()) 6631 6632 Level: advanced 6633 6634 Notes: Frees not only the matrices, but also the array that contains the matrices 6635 6636 .seealso: MatGetSeqNonzeroStructure() 6637 @*/ 6638 PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat) 6639 { 6640 PetscErrorCode ierr; 6641 6642 PetscFunctionBegin; 6643 PetscValidPointer(mat,1); 6644 ierr = MatDestroy(mat);CHKERRQ(ierr); 6645 PetscFunctionReturn(0); 6646 } 6647 6648 #undef __FUNCT__ 6649 #define __FUNCT__ "MatIncreaseOverlap" 6650 /*@ 6651 MatIncreaseOverlap - Given a set of submatrices indicated by index sets, 6652 replaces the index sets by larger ones that represent submatrices with 6653 additional overlap. 6654 6655 Collective on Mat 6656 6657 Input Parameters: 6658 + mat - the matrix 6659 . n - the number of index sets 6660 . is - the array of index sets (these index sets will changed during the call) 6661 - ov - the additional overlap requested 6662 6663 Level: developer 6664 6665 Concepts: overlap 6666 Concepts: ASM^computing overlap 6667 6668 .seealso: MatGetSubMatrices() 6669 @*/ 6670 PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov) 6671 { 6672 PetscErrorCode ierr; 6673 6674 PetscFunctionBegin; 6675 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6676 PetscValidType(mat,1); 6677 if (n < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n); 6678 if (n) { 6679 PetscValidPointer(is,3); 6680 PetscValidHeaderSpecific(*is,IS_CLASSID,3); 6681 } 6682 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 6683 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 6684 MatCheckPreallocated(mat,1); 6685 6686 if (!ov) PetscFunctionReturn(0); 6687 if (!mat->ops->increaseoverlap) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 6688 ierr = PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 6689 ierr = (*mat->ops->increaseoverlap)(mat,n,is,ov);CHKERRQ(ierr); 6690 ierr = PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 6691 PetscFunctionReturn(0); 6692 } 6693 6694 #undef __FUNCT__ 6695 #define __FUNCT__ "MatGetBlockSize" 6696 /*@ 6697 MatGetBlockSize - Returns the matrix block size; useful especially for the 6698 block row and block diagonal formats. 6699 6700 Not Collective 6701 6702 Input Parameter: 6703 . mat - the matrix 6704 6705 Output Parameter: 6706 . bs - block size 6707 6708 Notes: 6709 Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ 6710 6711 Level: intermediate 6712 6713 Concepts: matrices^block size 6714 6715 .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSizes() 6716 @*/ 6717 PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs) 6718 { 6719 PetscFunctionBegin; 6720 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6721 PetscValidType(mat,1); 6722 PetscValidIntPointer(bs,2); 6723 MatCheckPreallocated(mat,1); 6724 *bs = mat->rmap->bs; 6725 PetscFunctionReturn(0); 6726 } 6727 6728 #undef __FUNCT__ 6729 #define __FUNCT__ "MatGetBlockSizes" 6730 /*@ 6731 MatGetBlockSizes - Returns the matrix block row and column sizes; 6732 useful especially for the block row and block diagonal formats. 6733 6734 Not Collective 6735 6736 Input Parameter: 6737 . mat - the matrix 6738 6739 Output Parameter: 6740 . rbs - row block size 6741 . cbs - coumn block size 6742 6743 Notes: 6744 Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ 6745 6746 Level: intermediate 6747 6748 Concepts: matrices^block size 6749 6750 .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() 6751 @*/ 6752 PetscErrorCode MatGetBlockSizes(Mat mat,PetscInt *rbs, PetscInt *cbs) 6753 { 6754 PetscFunctionBegin; 6755 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6756 PetscValidType(mat,1); 6757 if (rbs) PetscValidIntPointer(rbs,2); 6758 if (cbs) PetscValidIntPointer(cbs,3); 6759 MatCheckPreallocated(mat,1); 6760 if (rbs) *rbs = mat->rmap->bs; 6761 if (cbs) *cbs = mat->cmap->bs; 6762 PetscFunctionReturn(0); 6763 } 6764 6765 #undef __FUNCT__ 6766 #define __FUNCT__ "MatSetBlockSize" 6767 /*@ 6768 MatSetBlockSize - Sets the matrix block size. 6769 6770 Logically Collective on Mat 6771 6772 Input Parameters: 6773 + mat - the matrix 6774 - bs - block size 6775 6776 Notes: 6777 This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later 6778 6779 Level: intermediate 6780 6781 Concepts: matrices^block size 6782 6783 .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() 6784 @*/ 6785 PetscErrorCode MatSetBlockSize(Mat mat,PetscInt bs) 6786 { 6787 PetscErrorCode ierr; 6788 6789 PetscFunctionBegin; 6790 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6791 PetscValidLogicalCollectiveInt(mat,bs,2); 6792 ierr = PetscLayoutSetBlockSize(mat->rmap,bs);CHKERRQ(ierr); 6793 ierr = PetscLayoutSetBlockSize(mat->cmap,bs);CHKERRQ(ierr); 6794 PetscFunctionReturn(0); 6795 } 6796 6797 #undef __FUNCT__ 6798 #define __FUNCT__ "MatSetBlockSizes" 6799 /*@ 6800 MatSetBlockSizes - Sets the matrix block row and column sizes. 6801 6802 Logically Collective on Mat 6803 6804 Input Parameters: 6805 + mat - the matrix 6806 - rbs - row block size 6807 - cbs - column block size 6808 6809 Notes: 6810 This must be called before MatSetUp() or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later 6811 6812 Level: intermediate 6813 6814 Concepts: matrices^block size 6815 6816 .seealso: MatCreateSeqBAIJ(), MatCreateBAIJ(), MatGetBlockSize() 6817 @*/ 6818 PetscErrorCode MatSetBlockSizes(Mat mat,PetscInt rbs,PetscInt cbs) 6819 { 6820 PetscErrorCode ierr; 6821 6822 PetscFunctionBegin; 6823 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6824 PetscValidLogicalCollectiveInt(mat,rbs,2); 6825 ierr = PetscLayoutSetBlockSize(mat->rmap,rbs);CHKERRQ(ierr); 6826 ierr = PetscLayoutSetBlockSize(mat->cmap,cbs);CHKERRQ(ierr); 6827 PetscFunctionReturn(0); 6828 } 6829 6830 #undef __FUNCT__ 6831 #define __FUNCT__ "MatResidual" 6832 /*@ 6833 MatResidual - Default routine to calculate the residual. 6834 6835 Collective on Mat and Vec 6836 6837 Input Parameters: 6838 + mat - the matrix 6839 . b - the right-hand-side 6840 - x - the approximate solution 6841 6842 Output Parameter: 6843 . r - location to store the residual 6844 6845 Level: developer 6846 6847 .keywords: MG, default, multigrid, residual 6848 6849 .seealso: PCMGSetResidual() 6850 @*/ 6851 PetscErrorCode MatResidual(Mat mat,Vec b,Vec x,Vec r) 6852 { 6853 PetscErrorCode ierr; 6854 6855 PetscFunctionBegin; 6856 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6857 PetscValidHeaderSpecific(b,VEC_CLASSID,2); 6858 PetscValidHeaderSpecific(x,VEC_CLASSID,3); 6859 PetscValidHeaderSpecific(r,VEC_CLASSID,4); 6860 PetscValidType(mat,1); 6861 MatCheckPreallocated(mat,1); 6862 ierr = PetscLogEventBegin(MAT_Residual,mat,0,0,0);CHKERRQ(ierr); 6863 if (!mat->ops->residual) { 6864 ierr = MatMult(mat,x,r);CHKERRQ(ierr); 6865 ierr = VecAYPX(r,-1.0,b);CHKERRQ(ierr); 6866 } else { 6867 ierr = (*mat->ops->residual)(mat,b,x,r);CHKERRQ(ierr); 6868 } 6869 ierr = PetscLogEventEnd(MAT_Residual,mat,0,0,0);CHKERRQ(ierr); 6870 PetscFunctionReturn(0); 6871 } 6872 6873 #undef __FUNCT__ 6874 #define __FUNCT__ "MatGetRowIJ" 6875 /*@C 6876 MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices. 6877 6878 Collective on Mat 6879 6880 Input Parameters: 6881 + mat - the matrix 6882 . shift - 0 or 1 indicating we want the indices starting at 0 or 1 6883 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be symmetrized 6884 - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the 6885 inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is 6886 always used. 6887 6888 Output Parameters: 6889 + n - number of rows in the (possibly compressed) matrix 6890 . ia - the row pointers [of length n+1] 6891 . ja - the column indices 6892 - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers 6893 are responsible for handling the case when done == PETSC_FALSE and ia and ja are not set 6894 6895 Level: developer 6896 6897 Notes: You CANNOT change any of the ia[] or ja[] values. 6898 6899 Use MatRestoreRowIJ() when you are finished accessing the ia[] and ja[] values 6900 6901 Fortran Node 6902 6903 In Fortran use 6904 $ PetscInt ia(1), ja(1) 6905 $ PetscOffset iia, jja 6906 $ call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,iia,ja,jja,done,ierr) 6907 $ 6908 $ or 6909 $ 6910 $ PetscScalar, pointer :: xx_v(:) 6911 $ call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr) 6912 6913 6914 Acess the ith and jth entries via ia(iia + i) and ja(jja + j) 6915 6916 .seealso: MatGetColumnIJ(), MatRestoreRowIJ(), MatSeqAIJGetArray() 6917 @*/ 6918 PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 6919 { 6920 PetscErrorCode ierr; 6921 6922 PetscFunctionBegin; 6923 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6924 PetscValidType(mat,1); 6925 PetscValidIntPointer(n,4); 6926 if (ia) PetscValidIntPointer(ia,5); 6927 if (ja) PetscValidIntPointer(ja,6); 6928 PetscValidIntPointer(done,7); 6929 MatCheckPreallocated(mat,1); 6930 if (!mat->ops->getrowij) *done = PETSC_FALSE; 6931 else { 6932 *done = PETSC_TRUE; 6933 ierr = PetscLogEventBegin(MAT_GetRowIJ,mat,0,0,0);CHKERRQ(ierr); 6934 ierr = (*mat->ops->getrowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 6935 ierr = PetscLogEventEnd(MAT_GetRowIJ,mat,0,0,0);CHKERRQ(ierr); 6936 } 6937 PetscFunctionReturn(0); 6938 } 6939 6940 #undef __FUNCT__ 6941 #define __FUNCT__ "MatGetColumnIJ" 6942 /*@C 6943 MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices. 6944 6945 Collective on Mat 6946 6947 Input Parameters: 6948 + mat - the matrix 6949 . shift - 1 or zero indicating we want the indices starting at 0 or 1 6950 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 6951 symmetrized 6952 - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the 6953 inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is 6954 always used. 6955 6956 Output Parameters: 6957 + n - number of columns in the (possibly compressed) matrix 6958 . ia - the column pointers 6959 . ja - the row indices 6960 - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned 6961 6962 Level: developer 6963 6964 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 6965 @*/ 6966 PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 6967 { 6968 PetscErrorCode ierr; 6969 6970 PetscFunctionBegin; 6971 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 6972 PetscValidType(mat,1); 6973 PetscValidIntPointer(n,4); 6974 if (ia) PetscValidIntPointer(ia,5); 6975 if (ja) PetscValidIntPointer(ja,6); 6976 PetscValidIntPointer(done,7); 6977 MatCheckPreallocated(mat,1); 6978 if (!mat->ops->getcolumnij) *done = PETSC_FALSE; 6979 else { 6980 *done = PETSC_TRUE; 6981 ierr = (*mat->ops->getcolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 6982 } 6983 PetscFunctionReturn(0); 6984 } 6985 6986 #undef __FUNCT__ 6987 #define __FUNCT__ "MatRestoreRowIJ" 6988 /*@C 6989 MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with 6990 MatGetRowIJ(). 6991 6992 Collective on Mat 6993 6994 Input Parameters: 6995 + mat - the matrix 6996 . shift - 1 or zero indicating we want the indices starting at 0 or 1 6997 . symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 6998 symmetrized 6999 - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the 7000 inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is 7001 always used. 7002 7003 Output Parameters: 7004 + n - size of (possibly compressed) matrix 7005 . ia - the row pointers 7006 . ja - the column indices 7007 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 7008 7009 Level: developer 7010 7011 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 7012 @*/ 7013 PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 7014 { 7015 PetscErrorCode ierr; 7016 7017 PetscFunctionBegin; 7018 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7019 PetscValidType(mat,1); 7020 if (ia) PetscValidIntPointer(ia,5); 7021 if (ja) PetscValidIntPointer(ja,6); 7022 PetscValidIntPointer(done,7); 7023 MatCheckPreallocated(mat,1); 7024 7025 if (!mat->ops->restorerowij) *done = PETSC_FALSE; 7026 else { 7027 *done = PETSC_TRUE; 7028 ierr = (*mat->ops->restorerowij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 7029 if (n) *n = 0; 7030 if (ia) *ia = NULL; 7031 if (ja) *ja = NULL; 7032 } 7033 PetscFunctionReturn(0); 7034 } 7035 7036 #undef __FUNCT__ 7037 #define __FUNCT__ "MatRestoreColumnIJ" 7038 /*@C 7039 MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with 7040 MatGetColumnIJ(). 7041 7042 Collective on Mat 7043 7044 Input Parameters: 7045 + mat - the matrix 7046 . shift - 1 or zero indicating we want the indices starting at 0 or 1 7047 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 7048 symmetrized 7049 - inodecompressed - PETSC_TRUE or PETSC_FALSE indicating if the nonzero structure of the 7050 inodes or the nonzero elements is wanted. For BAIJ matrices the compressed version is 7051 always used. 7052 7053 Output Parameters: 7054 + n - size of (possibly compressed) matrix 7055 . ia - the column pointers 7056 . ja - the row indices 7057 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 7058 7059 Level: developer 7060 7061 .seealso: MatGetColumnIJ(), MatRestoreRowIJ() 7062 @*/ 7063 PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done) 7064 { 7065 PetscErrorCode ierr; 7066 7067 PetscFunctionBegin; 7068 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7069 PetscValidType(mat,1); 7070 if (ia) PetscValidIntPointer(ia,5); 7071 if (ja) PetscValidIntPointer(ja,6); 7072 PetscValidIntPointer(done,7); 7073 MatCheckPreallocated(mat,1); 7074 7075 if (!mat->ops->restorecolumnij) *done = PETSC_FALSE; 7076 else { 7077 *done = PETSC_TRUE; 7078 ierr = (*mat->ops->restorecolumnij)(mat,shift,symmetric,inodecompressed,n,ia,ja,done);CHKERRQ(ierr); 7079 if (n) *n = 0; 7080 if (ia) *ia = NULL; 7081 if (ja) *ja = NULL; 7082 } 7083 PetscFunctionReturn(0); 7084 } 7085 7086 #undef __FUNCT__ 7087 #define __FUNCT__ "MatColoringPatch" 7088 /*@C 7089 MatColoringPatch -Used inside matrix coloring routines that 7090 use MatGetRowIJ() and/or MatGetColumnIJ(). 7091 7092 Collective on Mat 7093 7094 Input Parameters: 7095 + mat - the matrix 7096 . ncolors - max color value 7097 . n - number of entries in colorarray 7098 - colorarray - array indicating color for each column 7099 7100 Output Parameters: 7101 . iscoloring - coloring generated using colorarray information 7102 7103 Level: developer 7104 7105 .seealso: MatGetRowIJ(), MatGetColumnIJ() 7106 7107 @*/ 7108 PetscErrorCode MatColoringPatch(Mat mat,PetscInt ncolors,PetscInt n,ISColoringValue colorarray[],ISColoring *iscoloring) 7109 { 7110 PetscErrorCode ierr; 7111 7112 PetscFunctionBegin; 7113 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7114 PetscValidType(mat,1); 7115 PetscValidIntPointer(colorarray,4); 7116 PetscValidPointer(iscoloring,5); 7117 MatCheckPreallocated(mat,1); 7118 7119 if (!mat->ops->coloringpatch) { 7120 ierr = ISColoringCreate(PetscObjectComm((PetscObject)mat),ncolors,n,colorarray,iscoloring);CHKERRQ(ierr); 7121 } else { 7122 ierr = (*mat->ops->coloringpatch)(mat,ncolors,n,colorarray,iscoloring);CHKERRQ(ierr); 7123 } 7124 PetscFunctionReturn(0); 7125 } 7126 7127 7128 #undef __FUNCT__ 7129 #define __FUNCT__ "MatSetUnfactored" 7130 /*@ 7131 MatSetUnfactored - Resets a factored matrix to be treated as unfactored. 7132 7133 Logically Collective on Mat 7134 7135 Input Parameter: 7136 . mat - the factored matrix to be reset 7137 7138 Notes: 7139 This routine should be used only with factored matrices formed by in-place 7140 factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE 7141 format). This option can save memory, for example, when solving nonlinear 7142 systems with a matrix-free Newton-Krylov method and a matrix-based, in-place 7143 ILU(0) preconditioner. 7144 7145 Note that one can specify in-place ILU(0) factorization by calling 7146 .vb 7147 PCType(pc,PCILU); 7148 PCFactorSeUseInPlace(pc); 7149 .ve 7150 or by using the options -pc_type ilu -pc_factor_in_place 7151 7152 In-place factorization ILU(0) can also be used as a local 7153 solver for the blocks within the block Jacobi or additive Schwarz 7154 methods (runtime option: -sub_pc_factor_in_place). See the discussion 7155 of these preconditioners in the <a href="../../docs/manual.pdf#ch_pc">PC chapter of the users manual</a> for details on setting 7156 local solver options. 7157 7158 Most users should employ the simplified KSP interface for linear solvers 7159 instead of working directly with matrix algebra routines such as this. 7160 See, e.g., KSPCreate(). 7161 7162 Level: developer 7163 7164 .seealso: PCFactorSetUseInPlace() 7165 7166 Concepts: matrices^unfactored 7167 7168 @*/ 7169 PetscErrorCode MatSetUnfactored(Mat mat) 7170 { 7171 PetscErrorCode ierr; 7172 7173 PetscFunctionBegin; 7174 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7175 PetscValidType(mat,1); 7176 MatCheckPreallocated(mat,1); 7177 mat->factortype = MAT_FACTOR_NONE; 7178 if (!mat->ops->setunfactored) PetscFunctionReturn(0); 7179 ierr = (*mat->ops->setunfactored)(mat);CHKERRQ(ierr); 7180 PetscFunctionReturn(0); 7181 } 7182 7183 /*MC 7184 MatDenseGetArrayF90 - Accesses a matrix array from Fortran90. 7185 7186 Synopsis: 7187 MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr) 7188 7189 Not collective 7190 7191 Input Parameter: 7192 . x - matrix 7193 7194 Output Parameters: 7195 + xx_v - the Fortran90 pointer to the array 7196 - ierr - error code 7197 7198 Example of Usage: 7199 .vb 7200 PetscScalar, pointer xx_v(:,:) 7201 .... 7202 call MatDenseGetArrayF90(x,xx_v,ierr) 7203 a = xx_v(3) 7204 call MatDenseRestoreArrayF90(x,xx_v,ierr) 7205 .ve 7206 7207 Level: advanced 7208 7209 .seealso: MatDenseRestoreArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJGetArrayF90() 7210 7211 Concepts: matrices^accessing array 7212 7213 M*/ 7214 7215 /*MC 7216 MatDenseRestoreArrayF90 - Restores a matrix array that has been 7217 accessed with MatDenseGetArrayF90(). 7218 7219 Synopsis: 7220 MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 7221 7222 Not collective 7223 7224 Input Parameters: 7225 + x - matrix 7226 - xx_v - the Fortran90 pointer to the array 7227 7228 Output Parameter: 7229 . ierr - error code 7230 7231 Example of Usage: 7232 .vb 7233 PetscScalar, pointer xx_v(:) 7234 .... 7235 call MatDenseGetArrayF90(x,xx_v,ierr) 7236 a = xx_v(3) 7237 call MatDenseRestoreArrayF90(x,xx_v,ierr) 7238 .ve 7239 7240 Level: advanced 7241 7242 .seealso: MatDenseGetArrayF90(), MatDenseGetArray(), MatDenseRestoreArray(), MatSeqAIJRestoreArrayF90() 7243 7244 M*/ 7245 7246 7247 /*MC 7248 MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran90. 7249 7250 Synopsis: 7251 MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr) 7252 7253 Not collective 7254 7255 Input Parameter: 7256 . x - matrix 7257 7258 Output Parameters: 7259 + xx_v - the Fortran90 pointer to the array 7260 - ierr - error code 7261 7262 Example of Usage: 7263 .vb 7264 PetscScalar, pointer xx_v(:,:) 7265 .... 7266 call MatSeqAIJGetArrayF90(x,xx_v,ierr) 7267 a = xx_v(3) 7268 call MatSeqAIJRestoreArrayF90(x,xx_v,ierr) 7269 .ve 7270 7271 Level: advanced 7272 7273 .seealso: MatSeqAIJRestoreArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseGetArrayF90() 7274 7275 Concepts: matrices^accessing array 7276 7277 M*/ 7278 7279 /*MC 7280 MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been 7281 accessed with MatSeqAIJGetArrayF90(). 7282 7283 Synopsis: 7284 MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 7285 7286 Not collective 7287 7288 Input Parameters: 7289 + x - matrix 7290 - xx_v - the Fortran90 pointer to the array 7291 7292 Output Parameter: 7293 . ierr - error code 7294 7295 Example of Usage: 7296 .vb 7297 PetscScalar, pointer xx_v(:) 7298 .... 7299 call MatSeqAIJGetArrayF90(x,xx_v,ierr) 7300 a = xx_v(3) 7301 call MatSeqAIJRestoreArrayF90(x,xx_v,ierr) 7302 .ve 7303 7304 Level: advanced 7305 7306 .seealso: MatSeqAIJGetArrayF90(), MatSeqAIJGetArray(), MatSeqAIJRestoreArray(), MatDenseRestoreArrayF90() 7307 7308 M*/ 7309 7310 7311 #undef __FUNCT__ 7312 #define __FUNCT__ "MatGetSubMatrix" 7313 /*@ 7314 MatGetSubMatrix - Gets a single submatrix on the same number of processors 7315 as the original matrix. 7316 7317 Collective on Mat 7318 7319 Input Parameters: 7320 + mat - the original matrix 7321 . isrow - parallel IS containing the rows this processor should obtain 7322 . iscol - parallel IS containing all columns you wish to keep. Each process should list the columns that will be in IT's "diagonal part" in the new matrix. 7323 - cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 7324 7325 Output Parameter: 7326 . newmat - the new submatrix, of the same type as the old 7327 7328 Level: advanced 7329 7330 Notes: 7331 The submatrix will be able to be multiplied with vectors using the same layout as iscol. 7332 7333 The rows in isrow will be sorted into the same order as the original matrix on each process. 7334 7335 The first time this is called you should use a cll of MAT_INITIAL_MATRIX, 7336 the MatGetSubMatrix() routine will create the newmat for you. Any additional calls 7337 to this routine with a mat of the same nonzero structure and with a call of MAT_REUSE_MATRIX 7338 will reuse the matrix generated the first time. You should call MatDestroy() on newmat when 7339 you are finished using it. 7340 7341 The communicator of the newly obtained matrix is ALWAYS the same as the communicator of 7342 the input matrix. 7343 7344 If iscol is NULL then all columns are obtained (not supported in Fortran). 7345 7346 Example usage: 7347 Consider the following 8x8 matrix with 34 non-zero values, that is 7348 assembled across 3 processors. Let's assume that proc0 owns 3 rows, 7349 proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown 7350 as follows: 7351 7352 .vb 7353 1 2 0 | 0 3 0 | 0 4 7354 Proc0 0 5 6 | 7 0 0 | 8 0 7355 9 0 10 | 11 0 0 | 12 0 7356 ------------------------------------- 7357 13 0 14 | 15 16 17 | 0 0 7358 Proc1 0 18 0 | 19 20 21 | 0 0 7359 0 0 0 | 22 23 0 | 24 0 7360 ------------------------------------- 7361 Proc2 25 26 27 | 0 0 28 | 29 0 7362 30 0 0 | 31 32 33 | 0 34 7363 .ve 7364 7365 Suppose isrow = [0 1 | 4 | 6 7] and iscol = [1 2 | 3 4 5 | 6]. The resulting submatrix is 7366 7367 .vb 7368 2 0 | 0 3 0 | 0 7369 Proc0 5 6 | 7 0 0 | 8 7370 ------------------------------- 7371 Proc1 18 0 | 19 20 21 | 0 7372 ------------------------------- 7373 Proc2 26 27 | 0 0 28 | 29 7374 0 0 | 31 32 33 | 0 7375 .ve 7376 7377 7378 Concepts: matrices^submatrices 7379 7380 .seealso: MatGetSubMatrices() 7381 @*/ 7382 PetscErrorCode MatGetSubMatrix(Mat mat,IS isrow,IS iscol,MatReuse cll,Mat *newmat) 7383 { 7384 PetscErrorCode ierr; 7385 PetscMPIInt size; 7386 Mat *local; 7387 IS iscoltmp; 7388 7389 PetscFunctionBegin; 7390 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7391 PetscValidHeaderSpecific(isrow,IS_CLASSID,2); 7392 if (iscol) PetscValidHeaderSpecific(iscol,IS_CLASSID,3); 7393 PetscValidPointer(newmat,5); 7394 if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat,MAT_CLASSID,5); 7395 PetscValidType(mat,1); 7396 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 7397 MatCheckPreallocated(mat,1); 7398 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 7399 7400 if (!iscol) { 7401 ierr = ISCreateStride(PetscObjectComm((PetscObject)mat),mat->cmap->n,mat->cmap->rstart,1,&iscoltmp);CHKERRQ(ierr); 7402 } else { 7403 iscoltmp = iscol; 7404 } 7405 7406 /* if original matrix is on just one processor then use submatrix generated */ 7407 if (mat->ops->getsubmatrices && !mat->ops->getsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) { 7408 ierr = MatGetSubMatrices(mat,1,&isrow,&iscoltmp,MAT_REUSE_MATRIX,&newmat);CHKERRQ(ierr); 7409 if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} 7410 PetscFunctionReturn(0); 7411 } else if (mat->ops->getsubmatrices && !mat->ops->getsubmatrix && size == 1) { 7412 ierr = MatGetSubMatrices(mat,1,&isrow,&iscoltmp,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 7413 *newmat = *local; 7414 ierr = PetscFree(local);CHKERRQ(ierr); 7415 if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} 7416 PetscFunctionReturn(0); 7417 } else if (!mat->ops->getsubmatrix) { 7418 /* Create a new matrix type that implements the operation using the full matrix */ 7419 switch (cll) { 7420 case MAT_INITIAL_MATRIX: 7421 ierr = MatCreateSubMatrix(mat,isrow,iscoltmp,newmat);CHKERRQ(ierr); 7422 break; 7423 case MAT_REUSE_MATRIX: 7424 ierr = MatSubMatrixUpdate(*newmat,mat,isrow,iscoltmp);CHKERRQ(ierr); 7425 break; 7426 default: SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX"); 7427 } 7428 if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} 7429 PetscFunctionReturn(0); 7430 } 7431 7432 if (!mat->ops->getsubmatrix) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 7433 ierr = (*mat->ops->getsubmatrix)(mat,isrow,iscoltmp,cll,newmat);CHKERRQ(ierr); 7434 if (!iscol) {ierr = ISDestroy(&iscoltmp);CHKERRQ(ierr);} 7435 if (*newmat && cll == MAT_INITIAL_MATRIX) {ierr = PetscObjectStateIncrease((PetscObject)*newmat);CHKERRQ(ierr);} 7436 PetscFunctionReturn(0); 7437 } 7438 7439 #undef __FUNCT__ 7440 #define __FUNCT__ "MatStashSetInitialSize" 7441 /*@ 7442 MatStashSetInitialSize - sets the sizes of the matrix stash, that is 7443 used during the assembly process to store values that belong to 7444 other processors. 7445 7446 Not Collective 7447 7448 Input Parameters: 7449 + mat - the matrix 7450 . size - the initial size of the stash. 7451 - bsize - the initial size of the block-stash(if used). 7452 7453 Options Database Keys: 7454 + -matstash_initial_size <size> or <size0,size1,...sizep-1> 7455 - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> 7456 7457 Level: intermediate 7458 7459 Notes: 7460 The block-stash is used for values set with MatSetValuesBlocked() while 7461 the stash is used for values set with MatSetValues() 7462 7463 Run with the option -info and look for output of the form 7464 MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs. 7465 to determine the appropriate value, MM, to use for size and 7466 MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs. 7467 to determine the value, BMM to use for bsize 7468 7469 Concepts: stash^setting matrix size 7470 Concepts: matrices^stash 7471 7472 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashGetInfo() 7473 7474 @*/ 7475 PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize) 7476 { 7477 PetscErrorCode ierr; 7478 7479 PetscFunctionBegin; 7480 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7481 PetscValidType(mat,1); 7482 ierr = MatStashSetInitialSize_Private(&mat->stash,size);CHKERRQ(ierr); 7483 ierr = MatStashSetInitialSize_Private(&mat->bstash,bsize);CHKERRQ(ierr); 7484 PetscFunctionReturn(0); 7485 } 7486 7487 #undef __FUNCT__ 7488 #define __FUNCT__ "MatInterpolateAdd" 7489 /*@ 7490 MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of 7491 the matrix 7492 7493 Neighbor-wise Collective on Mat 7494 7495 Input Parameters: 7496 + mat - the matrix 7497 . x,y - the vectors 7498 - w - where the result is stored 7499 7500 Level: intermediate 7501 7502 Notes: 7503 w may be the same vector as y. 7504 7505 This allows one to use either the restriction or interpolation (its transpose) 7506 matrix to do the interpolation 7507 7508 Concepts: interpolation 7509 7510 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 7511 7512 @*/ 7513 PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w) 7514 { 7515 PetscErrorCode ierr; 7516 PetscInt M,N,Ny; 7517 7518 PetscFunctionBegin; 7519 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 7520 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 7521 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 7522 PetscValidHeaderSpecific(w,VEC_CLASSID,4); 7523 PetscValidType(A,1); 7524 MatCheckPreallocated(A,1); 7525 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 7526 ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); 7527 if (M == Ny) { 7528 ierr = MatMultAdd(A,x,y,w);CHKERRQ(ierr); 7529 } else { 7530 ierr = MatMultTransposeAdd(A,x,y,w);CHKERRQ(ierr); 7531 } 7532 PetscFunctionReturn(0); 7533 } 7534 7535 #undef __FUNCT__ 7536 #define __FUNCT__ "MatInterpolate" 7537 /*@ 7538 MatInterpolate - y = A*x or A'*x depending on the shape of 7539 the matrix 7540 7541 Neighbor-wise Collective on Mat 7542 7543 Input Parameters: 7544 + mat - the matrix 7545 - x,y - the vectors 7546 7547 Level: intermediate 7548 7549 Notes: 7550 This allows one to use either the restriction or interpolation (its transpose) 7551 matrix to do the interpolation 7552 7553 Concepts: matrices^interpolation 7554 7555 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 7556 7557 @*/ 7558 PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y) 7559 { 7560 PetscErrorCode ierr; 7561 PetscInt M,N,Ny; 7562 7563 PetscFunctionBegin; 7564 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 7565 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 7566 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 7567 PetscValidType(A,1); 7568 MatCheckPreallocated(A,1); 7569 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 7570 ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); 7571 if (M == Ny) { 7572 ierr = MatMult(A,x,y);CHKERRQ(ierr); 7573 } else { 7574 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 7575 } 7576 PetscFunctionReturn(0); 7577 } 7578 7579 #undef __FUNCT__ 7580 #define __FUNCT__ "MatRestrict" 7581 /*@ 7582 MatRestrict - y = A*x or A'*x 7583 7584 Neighbor-wise Collective on Mat 7585 7586 Input Parameters: 7587 + mat - the matrix 7588 - x,y - the vectors 7589 7590 Level: intermediate 7591 7592 Notes: 7593 This allows one to use either the restriction or interpolation (its transpose) 7594 matrix to do the restriction 7595 7596 Concepts: matrices^restriction 7597 7598 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate() 7599 7600 @*/ 7601 PetscErrorCode MatRestrict(Mat A,Vec x,Vec y) 7602 { 7603 PetscErrorCode ierr; 7604 PetscInt M,N,Ny; 7605 7606 PetscFunctionBegin; 7607 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 7608 PetscValidHeaderSpecific(x,VEC_CLASSID,2); 7609 PetscValidHeaderSpecific(y,VEC_CLASSID,3); 7610 PetscValidType(A,1); 7611 MatCheckPreallocated(A,1); 7612 7613 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 7614 ierr = VecGetSize(y,&Ny);CHKERRQ(ierr); 7615 if (M == Ny) { 7616 ierr = MatMult(A,x,y);CHKERRQ(ierr); 7617 } else { 7618 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 7619 } 7620 PetscFunctionReturn(0); 7621 } 7622 7623 #undef __FUNCT__ 7624 #define __FUNCT__ "MatGetNullSpace" 7625 /*@ 7626 MatGetNullSpace - retrieves the null space to a matrix. 7627 7628 Logically Collective on Mat and MatNullSpace 7629 7630 Input Parameters: 7631 + mat - the matrix 7632 - nullsp - the null space object 7633 7634 Level: developer 7635 7636 Notes: 7637 This null space is used by solvers. Overwrites any previous null space that may have been attached 7638 7639 Concepts: null space^attaching to matrix 7640 7641 .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace() 7642 @*/ 7643 PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp) 7644 { 7645 PetscFunctionBegin; 7646 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7647 PetscValidType(mat,1); 7648 PetscValidPointer(nullsp,2); 7649 *nullsp = mat->nullsp; 7650 PetscFunctionReturn(0); 7651 } 7652 7653 #undef __FUNCT__ 7654 #define __FUNCT__ "MatSetNullSpace" 7655 /*@ 7656 MatSetNullSpace - attaches a null space to a matrix. 7657 This null space will be removed from the resulting vector whenever 7658 MatMult() is called 7659 7660 Logically Collective on Mat and MatNullSpace 7661 7662 Input Parameters: 7663 + mat - the matrix 7664 - nullsp - the null space object 7665 7666 Level: advanced 7667 7668 Notes: 7669 This null space is used by solvers. Overwrites any previous null space that may have been attached 7670 7671 Concepts: null space^attaching to matrix 7672 7673 .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace() 7674 @*/ 7675 PetscErrorCode MatSetNullSpace(Mat mat,MatNullSpace nullsp) 7676 { 7677 PetscErrorCode ierr; 7678 7679 PetscFunctionBegin; 7680 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7681 PetscValidType(mat,1); 7682 PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_CLASSID,2); 7683 MatCheckPreallocated(mat,1); 7684 ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); 7685 ierr = MatNullSpaceDestroy(&mat->nullsp);CHKERRQ(ierr); 7686 7687 mat->nullsp = nullsp; 7688 PetscFunctionReturn(0); 7689 } 7690 7691 #undef __FUNCT__ 7692 #define __FUNCT__ "MatSetNearNullSpace" 7693 /*@ 7694 MatSetNearNullSpace - attaches a null space to a matrix. 7695 This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix. 7696 7697 Logically Collective on Mat and MatNullSpace 7698 7699 Input Parameters: 7700 + mat - the matrix 7701 - nullsp - the null space object 7702 7703 Level: advanced 7704 7705 Notes: 7706 Overwrites any previous near null space that may have been attached 7707 7708 Concepts: null space^attaching to matrix 7709 7710 .seealso: MatCreate(), MatNullSpaceCreate(), MatSetNullSpace() 7711 @*/ 7712 PetscErrorCode MatSetNearNullSpace(Mat mat,MatNullSpace nullsp) 7713 { 7714 PetscErrorCode ierr; 7715 7716 PetscFunctionBegin; 7717 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7718 PetscValidType(mat,1); 7719 PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_CLASSID,2); 7720 MatCheckPreallocated(mat,1); 7721 ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); 7722 ierr = MatNullSpaceDestroy(&mat->nearnullsp);CHKERRQ(ierr); 7723 7724 mat->nearnullsp = nullsp; 7725 PetscFunctionReturn(0); 7726 } 7727 7728 #undef __FUNCT__ 7729 #define __FUNCT__ "MatGetNearNullSpace" 7730 /*@ 7731 MatGetNearNullSpace -Get null space attached with MatSetNearNullSpace() 7732 7733 Not Collective 7734 7735 Input Parameters: 7736 . mat - the matrix 7737 7738 Output Parameters: 7739 . nullsp - the null space object, NULL if not set 7740 7741 Level: developer 7742 7743 Concepts: null space^attaching to matrix 7744 7745 .seealso: MatSetNearNullSpace(), MatGetNullSpace() 7746 @*/ 7747 PetscErrorCode MatGetNearNullSpace(Mat mat,MatNullSpace *nullsp) 7748 { 7749 PetscFunctionBegin; 7750 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7751 PetscValidType(mat,1); 7752 PetscValidPointer(nullsp,2); 7753 MatCheckPreallocated(mat,1); 7754 *nullsp = mat->nearnullsp; 7755 PetscFunctionReturn(0); 7756 } 7757 7758 #undef __FUNCT__ 7759 #define __FUNCT__ "MatICCFactor" 7760 /*@C 7761 MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix. 7762 7763 Collective on Mat 7764 7765 Input Parameters: 7766 + mat - the matrix 7767 . row - row/column permutation 7768 . fill - expected fill factor >= 1.0 7769 - level - level of fill, for ICC(k) 7770 7771 Notes: 7772 Probably really in-place only when level of fill is zero, otherwise allocates 7773 new space to store factored matrix and deletes previous memory. 7774 7775 Most users should employ the simplified KSP interface for linear solvers 7776 instead of working directly with matrix algebra routines such as this. 7777 See, e.g., KSPCreate(). 7778 7779 Level: developer 7780 7781 Concepts: matrices^incomplete Cholesky factorization 7782 Concepts: Cholesky factorization 7783 7784 .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 7785 7786 Developer Note: fortran interface is not autogenerated as the f90 7787 interface defintion cannot be generated correctly [due to MatFactorInfo] 7788 7789 @*/ 7790 PetscErrorCode MatICCFactor(Mat mat,IS row,const MatFactorInfo *info) 7791 { 7792 PetscErrorCode ierr; 7793 7794 PetscFunctionBegin; 7795 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7796 PetscValidType(mat,1); 7797 if (row) PetscValidHeaderSpecific(row,IS_CLASSID,2); 7798 PetscValidPointer(info,3); 7799 if (mat->rmap->N != mat->cmap->N) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONG,"matrix must be square"); 7800 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 7801 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 7802 if (!mat->ops->iccfactor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 7803 MatCheckPreallocated(mat,1); 7804 ierr = (*mat->ops->iccfactor)(mat,row,info);CHKERRQ(ierr); 7805 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 7806 PetscFunctionReturn(0); 7807 } 7808 7809 #undef __FUNCT__ 7810 #define __FUNCT__ "MatSetValuesAdifor" 7811 /*@ 7812 MatSetValuesAdifor - Sets values computed with automatic differentiation into a matrix. 7813 7814 Not Collective 7815 7816 Input Parameters: 7817 + mat - the matrix 7818 . nl - leading dimension of v 7819 - v - the values compute with ADIFOR 7820 7821 Level: developer 7822 7823 Notes: 7824 Must call MatSetColoring() before using this routine. Also this matrix must already 7825 have its nonzero pattern determined. 7826 7827 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 7828 MatSetValues(), MatSetColoring() 7829 @*/ 7830 PetscErrorCode MatSetValuesAdifor(Mat mat,PetscInt nl,void *v) 7831 { 7832 PetscErrorCode ierr; 7833 7834 PetscFunctionBegin; 7835 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7836 PetscValidType(mat,1); 7837 PetscValidPointer(v,3); 7838 7839 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 7840 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 7841 if (!mat->ops->setvaluesadifor) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 7842 ierr = (*mat->ops->setvaluesadifor)(mat,nl,v);CHKERRQ(ierr); 7843 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 7844 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 7845 PetscFunctionReturn(0); 7846 } 7847 7848 #undef __FUNCT__ 7849 #define __FUNCT__ "MatDiagonalScaleLocal" 7850 /*@ 7851 MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the 7852 ghosted ones. 7853 7854 Not Collective 7855 7856 Input Parameters: 7857 + mat - the matrix 7858 - diag = the diagonal values, including ghost ones 7859 7860 Level: developer 7861 7862 Notes: Works only for MPIAIJ and MPIBAIJ matrices 7863 7864 .seealso: MatDiagonalScale() 7865 @*/ 7866 PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag) 7867 { 7868 PetscErrorCode ierr; 7869 PetscMPIInt size; 7870 7871 PetscFunctionBegin; 7872 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7873 PetscValidHeaderSpecific(diag,VEC_CLASSID,2); 7874 PetscValidType(mat,1); 7875 7876 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 7877 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 7878 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);CHKERRQ(ierr); 7879 if (size == 1) { 7880 PetscInt n,m; 7881 ierr = VecGetSize(diag,&n);CHKERRQ(ierr); 7882 ierr = MatGetSize(mat,0,&m);CHKERRQ(ierr); 7883 if (m == n) { 7884 ierr = MatDiagonalScale(mat,0,diag);CHKERRQ(ierr); 7885 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions"); 7886 } else { 7887 ierr = PetscUseMethod(mat,"MatDiagonalScaleLocal_C",(Mat,Vec),(mat,diag));CHKERRQ(ierr); 7888 } 7889 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 7890 ierr = PetscObjectStateIncrease((PetscObject)mat);CHKERRQ(ierr); 7891 PetscFunctionReturn(0); 7892 } 7893 7894 #undef __FUNCT__ 7895 #define __FUNCT__ "MatGetInertia" 7896 /*@ 7897 MatGetInertia - Gets the inertia from a factored matrix 7898 7899 Collective on Mat 7900 7901 Input Parameter: 7902 . mat - the matrix 7903 7904 Output Parameters: 7905 + nneg - number of negative eigenvalues 7906 . nzero - number of zero eigenvalues 7907 - npos - number of positive eigenvalues 7908 7909 Level: advanced 7910 7911 Notes: Matrix must have been factored by MatCholeskyFactor() 7912 7913 7914 @*/ 7915 PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos) 7916 { 7917 PetscErrorCode ierr; 7918 7919 PetscFunctionBegin; 7920 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7921 PetscValidType(mat,1); 7922 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 7923 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled"); 7924 if (!mat->ops->getinertia) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 7925 ierr = (*mat->ops->getinertia)(mat,nneg,nzero,npos);CHKERRQ(ierr); 7926 PetscFunctionReturn(0); 7927 } 7928 7929 /* ----------------------------------------------------------------*/ 7930 #undef __FUNCT__ 7931 #define __FUNCT__ "MatSolves" 7932 /*@C 7933 MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors 7934 7935 Neighbor-wise Collective on Mat and Vecs 7936 7937 Input Parameters: 7938 + mat - the factored matrix 7939 - b - the right-hand-side vectors 7940 7941 Output Parameter: 7942 . x - the result vectors 7943 7944 Notes: 7945 The vectors b and x cannot be the same. I.e., one cannot 7946 call MatSolves(A,x,x). 7947 7948 Notes: 7949 Most users should employ the simplified KSP interface for linear solvers 7950 instead of working directly with matrix algebra routines such as this. 7951 See, e.g., KSPCreate(). 7952 7953 Level: developer 7954 7955 Concepts: matrices^triangular solves 7956 7957 .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve() 7958 @*/ 7959 PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x) 7960 { 7961 PetscErrorCode ierr; 7962 7963 PetscFunctionBegin; 7964 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 7965 PetscValidType(mat,1); 7966 if (x == b) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 7967 if (!mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 7968 if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(0); 7969 7970 if (!mat->ops->solves) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 7971 MatCheckPreallocated(mat,1); 7972 ierr = PetscLogEventBegin(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 7973 ierr = (*mat->ops->solves)(mat,b,x);CHKERRQ(ierr); 7974 ierr = PetscLogEventEnd(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 7975 PetscFunctionReturn(0); 7976 } 7977 7978 #undef __FUNCT__ 7979 #define __FUNCT__ "MatIsSymmetric" 7980 /*@ 7981 MatIsSymmetric - Test whether a matrix is symmetric 7982 7983 Collective on Mat 7984 7985 Input Parameter: 7986 + A - the matrix to test 7987 - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose) 7988 7989 Output Parameters: 7990 . flg - the result 7991 7992 Notes: For real numbers MatIsSymmetric() and MatIsHermitian() return identical results 7993 7994 Level: intermediate 7995 7996 Concepts: matrix^symmetry 7997 7998 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown() 7999 @*/ 8000 PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscBool *flg) 8001 { 8002 PetscErrorCode ierr; 8003 8004 PetscFunctionBegin; 8005 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8006 PetscValidPointer(flg,2); 8007 8008 if (!A->symmetric_set) { 8009 if (!A->ops->issymmetric) { 8010 MatType mattype; 8011 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 8012 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); 8013 } 8014 ierr = (*A->ops->issymmetric)(A,tol,flg);CHKERRQ(ierr); 8015 if (!tol) { 8016 A->symmetric_set = PETSC_TRUE; 8017 A->symmetric = *flg; 8018 if (A->symmetric) { 8019 A->structurally_symmetric_set = PETSC_TRUE; 8020 A->structurally_symmetric = PETSC_TRUE; 8021 } 8022 } 8023 } else if (A->symmetric) { 8024 *flg = PETSC_TRUE; 8025 } else if (!tol) { 8026 *flg = PETSC_FALSE; 8027 } else { 8028 if (!A->ops->issymmetric) { 8029 MatType mattype; 8030 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 8031 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); 8032 } 8033 ierr = (*A->ops->issymmetric)(A,tol,flg);CHKERRQ(ierr); 8034 } 8035 PetscFunctionReturn(0); 8036 } 8037 8038 #undef __FUNCT__ 8039 #define __FUNCT__ "MatIsHermitian" 8040 /*@ 8041 MatIsHermitian - Test whether a matrix is Hermitian 8042 8043 Collective on Mat 8044 8045 Input Parameter: 8046 + A - the matrix to test 8047 - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian) 8048 8049 Output Parameters: 8050 . flg - the result 8051 8052 Level: intermediate 8053 8054 Concepts: matrix^symmetry 8055 8056 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), 8057 MatIsSymmetricKnown(), MatIsSymmetric() 8058 @*/ 8059 PetscErrorCode MatIsHermitian(Mat A,PetscReal tol,PetscBool *flg) 8060 { 8061 PetscErrorCode ierr; 8062 8063 PetscFunctionBegin; 8064 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8065 PetscValidPointer(flg,2); 8066 8067 if (!A->hermitian_set) { 8068 if (!A->ops->ishermitian) { 8069 MatType mattype; 8070 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 8071 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype); 8072 } 8073 ierr = (*A->ops->ishermitian)(A,tol,flg);CHKERRQ(ierr); 8074 if (!tol) { 8075 A->hermitian_set = PETSC_TRUE; 8076 A->hermitian = *flg; 8077 if (A->hermitian) { 8078 A->structurally_symmetric_set = PETSC_TRUE; 8079 A->structurally_symmetric = PETSC_TRUE; 8080 } 8081 } 8082 } else if (A->hermitian) { 8083 *flg = PETSC_TRUE; 8084 } else if (!tol) { 8085 *flg = PETSC_FALSE; 8086 } else { 8087 if (!A->ops->ishermitian) { 8088 MatType mattype; 8089 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 8090 SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for hermitian",mattype); 8091 } 8092 ierr = (*A->ops->ishermitian)(A,tol,flg);CHKERRQ(ierr); 8093 } 8094 PetscFunctionReturn(0); 8095 } 8096 8097 #undef __FUNCT__ 8098 #define __FUNCT__ "MatIsSymmetricKnown" 8099 /*@ 8100 MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric. 8101 8102 Not Collective 8103 8104 Input Parameter: 8105 . A - the matrix to check 8106 8107 Output Parameters: 8108 + set - if the symmetric flag is set (this tells you if the next flag is valid) 8109 - flg - the result 8110 8111 Level: advanced 8112 8113 Concepts: matrix^symmetry 8114 8115 Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric() 8116 if you want it explicitly checked 8117 8118 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() 8119 @*/ 8120 PetscErrorCode MatIsSymmetricKnown(Mat A,PetscBool *set,PetscBool *flg) 8121 { 8122 PetscFunctionBegin; 8123 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8124 PetscValidPointer(set,2); 8125 PetscValidPointer(flg,3); 8126 if (A->symmetric_set) { 8127 *set = PETSC_TRUE; 8128 *flg = A->symmetric; 8129 } else { 8130 *set = PETSC_FALSE; 8131 } 8132 PetscFunctionReturn(0); 8133 } 8134 8135 #undef __FUNCT__ 8136 #define __FUNCT__ "MatIsHermitianKnown" 8137 /*@ 8138 MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian. 8139 8140 Not Collective 8141 8142 Input Parameter: 8143 . A - the matrix to check 8144 8145 Output Parameters: 8146 + set - if the hermitian flag is set (this tells you if the next flag is valid) 8147 - flg - the result 8148 8149 Level: advanced 8150 8151 Concepts: matrix^symmetry 8152 8153 Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian() 8154 if you want it explicitly checked 8155 8156 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() 8157 @*/ 8158 PetscErrorCode MatIsHermitianKnown(Mat A,PetscBool *set,PetscBool *flg) 8159 { 8160 PetscFunctionBegin; 8161 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8162 PetscValidPointer(set,2); 8163 PetscValidPointer(flg,3); 8164 if (A->hermitian_set) { 8165 *set = PETSC_TRUE; 8166 *flg = A->hermitian; 8167 } else { 8168 *set = PETSC_FALSE; 8169 } 8170 PetscFunctionReturn(0); 8171 } 8172 8173 #undef __FUNCT__ 8174 #define __FUNCT__ "MatIsStructurallySymmetric" 8175 /*@ 8176 MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric 8177 8178 Collective on Mat 8179 8180 Input Parameter: 8181 . A - the matrix to test 8182 8183 Output Parameters: 8184 . flg - the result 8185 8186 Level: intermediate 8187 8188 Concepts: matrix^symmetry 8189 8190 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption() 8191 @*/ 8192 PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscBool *flg) 8193 { 8194 PetscErrorCode ierr; 8195 8196 PetscFunctionBegin; 8197 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8198 PetscValidPointer(flg,2); 8199 if (!A->structurally_symmetric_set) { 8200 if (!A->ops->isstructurallysymmetric) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric"); 8201 ierr = (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);CHKERRQ(ierr); 8202 8203 A->structurally_symmetric_set = PETSC_TRUE; 8204 } 8205 *flg = A->structurally_symmetric; 8206 PetscFunctionReturn(0); 8207 } 8208 8209 #undef __FUNCT__ 8210 #define __FUNCT__ "MatStashGetInfo" 8211 extern PetscErrorCode MatStashGetInfo_Private(MatStash*,PetscInt*,PetscInt*); 8212 /*@ 8213 MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need 8214 to be communicated to other processors during the MatAssemblyBegin/End() process 8215 8216 Not collective 8217 8218 Input Parameter: 8219 . vec - the vector 8220 8221 Output Parameters: 8222 + nstash - the size of the stash 8223 . reallocs - the number of additional mallocs incurred. 8224 . bnstash - the size of the block stash 8225 - breallocs - the number of additional mallocs incurred.in the block stash 8226 8227 Level: advanced 8228 8229 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize() 8230 8231 @*/ 8232 PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *breallocs) 8233 { 8234 PetscErrorCode ierr; 8235 8236 PetscFunctionBegin; 8237 ierr = MatStashGetInfo_Private(&mat->stash,nstash,reallocs);CHKERRQ(ierr); 8238 ierr = MatStashGetInfo_Private(&mat->bstash,bnstash,breallocs);CHKERRQ(ierr); 8239 PetscFunctionReturn(0); 8240 } 8241 8242 #undef __FUNCT__ 8243 #define __FUNCT__ "MatGetVecs" 8244 /*@C 8245 MatGetVecs - Get vector(s) compatible with the matrix, i.e. with the same 8246 parallel layout 8247 8248 Collective on Mat 8249 8250 Input Parameter: 8251 . mat - the matrix 8252 8253 Output Parameter: 8254 + right - (optional) vector that the matrix can be multiplied against 8255 - left - (optional) vector that the matrix vector product can be stored in 8256 8257 Level: advanced 8258 8259 .seealso: MatCreate() 8260 @*/ 8261 PetscErrorCode MatGetVecs(Mat mat,Vec *right,Vec *left) 8262 { 8263 PetscErrorCode ierr; 8264 8265 PetscFunctionBegin; 8266 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 8267 PetscValidType(mat,1); 8268 MatCheckPreallocated(mat,1); 8269 if (mat->ops->getvecs) { 8270 ierr = (*mat->ops->getvecs)(mat,right,left);CHKERRQ(ierr); 8271 } else { 8272 PetscMPIInt size; 8273 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size);CHKERRQ(ierr); 8274 if (right) { 8275 ierr = VecCreate(PetscObjectComm((PetscObject)mat),right);CHKERRQ(ierr); 8276 ierr = VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 8277 ierr = VecSetBlockSize(*right,mat->rmap->bs);CHKERRQ(ierr); 8278 ierr = VecSetType(*right,VECSTANDARD);CHKERRQ(ierr); 8279 ierr = PetscLayoutReference(mat->cmap,&(*right)->map);CHKERRQ(ierr); 8280 } 8281 if (left) { 8282 ierr = VecCreate(PetscObjectComm((PetscObject)mat),left);CHKERRQ(ierr); 8283 ierr = VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);CHKERRQ(ierr); 8284 ierr = VecSetBlockSize(*left,mat->rmap->bs);CHKERRQ(ierr); 8285 ierr = VecSetType(*left,VECSTANDARD);CHKERRQ(ierr); 8286 ierr = PetscLayoutReference(mat->rmap,&(*left)->map);CHKERRQ(ierr); 8287 } 8288 } 8289 PetscFunctionReturn(0); 8290 } 8291 8292 #undef __FUNCT__ 8293 #define __FUNCT__ "MatFactorInfoInitialize" 8294 /*@C 8295 MatFactorInfoInitialize - Initializes a MatFactorInfo data structure 8296 with default values. 8297 8298 Not Collective 8299 8300 Input Parameters: 8301 . info - the MatFactorInfo data structure 8302 8303 8304 Notes: The solvers are generally used through the KSP and PC objects, for example 8305 PCLU, PCILU, PCCHOLESKY, PCICC 8306 8307 Level: developer 8308 8309 .seealso: MatFactorInfo 8310 8311 Developer Note: fortran interface is not autogenerated as the f90 8312 interface defintion cannot be generated correctly [due to MatFactorInfo] 8313 8314 @*/ 8315 8316 PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info) 8317 { 8318 PetscErrorCode ierr; 8319 8320 PetscFunctionBegin; 8321 ierr = PetscMemzero(info,sizeof(MatFactorInfo));CHKERRQ(ierr); 8322 PetscFunctionReturn(0); 8323 } 8324 8325 #undef __FUNCT__ 8326 #define __FUNCT__ "MatPtAP" 8327 /*@ 8328 MatPtAP - Creates the matrix product C = P^T * A * P 8329 8330 Neighbor-wise Collective on Mat 8331 8332 Input Parameters: 8333 + A - the matrix 8334 . P - the projection matrix 8335 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 8336 - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)) 8337 8338 Output Parameters: 8339 . C - the product matrix 8340 8341 Notes: 8342 C will be created and must be destroyed by the user with MatDestroy(). 8343 8344 This routine is currently only implemented for pairs of AIJ matrices and classes 8345 which inherit from AIJ. 8346 8347 Level: intermediate 8348 8349 .seealso: MatPtAPSymbolic(), MatPtAPNumeric(), MatMatMult(), MatRARt() 8350 @*/ 8351 PetscErrorCode MatPtAP(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C) 8352 { 8353 PetscErrorCode ierr; 8354 PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); 8355 PetscErrorCode (*fP)(Mat,Mat,MatReuse,PetscReal,Mat*); 8356 PetscErrorCode (*ptap)(Mat,Mat,MatReuse,PetscReal,Mat*)=NULL; 8357 PetscBool viatranspose=PETSC_FALSE,viamatmatmatmult=PETSC_FALSE; 8358 8359 PetscFunctionBegin; 8360 ierr = PetscOptionsGetBool(((PetscObject)A)->prefix,"-matptap_viatranspose",&viatranspose,NULL);CHKERRQ(ierr); 8361 ierr = PetscOptionsGetBool(((PetscObject)A)->prefix,"-matptap_viamatmatmatmult",&viamatmatmatmult,NULL);CHKERRQ(ierr); 8362 8363 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8364 PetscValidType(A,1); 8365 MatCheckPreallocated(A,1); 8366 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8367 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8368 PetscValidHeaderSpecific(P,MAT_CLASSID,2); 8369 PetscValidType(P,2); 8370 MatCheckPreallocated(P,2); 8371 if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8372 if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8373 8374 if (P->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); 8375 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8376 8377 if (scall == MAT_REUSE_MATRIX) { 8378 PetscValidPointer(*C,5); 8379 PetscValidHeaderSpecific(*C,MAT_CLASSID,5); 8380 if (viatranspose || viamatmatmatmult) { 8381 Mat Pt; 8382 ierr = MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);CHKERRQ(ierr); 8383 if (viamatmatmatmult) { 8384 ierr = MatMatMatMult(Pt,A,P,scall,fill,C);CHKERRQ(ierr); 8385 } else { 8386 Mat AP; 8387 ierr = MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);CHKERRQ(ierr); 8388 ierr = MatMatMult(Pt,AP,scall,fill,C);CHKERRQ(ierr); 8389 ierr = MatDestroy(&AP);CHKERRQ(ierr); 8390 } 8391 ierr = MatDestroy(&Pt);CHKERRQ(ierr); 8392 } else { 8393 ierr = PetscLogEventBegin(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); 8394 ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); 8395 ierr = (*(*C)->ops->ptapnumeric)(A,P,*C);CHKERRQ(ierr); 8396 ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); 8397 ierr = PetscLogEventEnd(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); 8398 } 8399 PetscFunctionReturn(0); 8400 } 8401 8402 if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; 8403 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8404 8405 fA = A->ops->ptap; 8406 fP = P->ops->ptap; 8407 if (fP == fA) { 8408 if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatPtAP not supported for A of type %s",((PetscObject)A)->type_name); 8409 ptap = fA; 8410 } else { 8411 /* dispatch based on the type of A and P from their PetscObject's PetscFunctionLists. */ 8412 char ptapname[256]; 8413 ierr = PetscStrcpy(ptapname,"MatPtAP_");CHKERRQ(ierr); 8414 ierr = PetscStrcat(ptapname,((PetscObject)A)->type_name);CHKERRQ(ierr); 8415 ierr = PetscStrcat(ptapname,"_");CHKERRQ(ierr); 8416 ierr = PetscStrcat(ptapname,((PetscObject)P)->type_name);CHKERRQ(ierr); 8417 ierr = PetscStrcat(ptapname,"_C");CHKERRQ(ierr); /* e.g., ptapname = "MatPtAP_seqdense_seqaij_C" */ 8418 ierr = PetscObjectQueryFunction((PetscObject)P,ptapname,&ptap);CHKERRQ(ierr); 8419 if (!ptap) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatPtAP requires A, %s, to be compatible with P, %s",((PetscObject)A)->type_name,((PetscObject)P)->type_name); 8420 } 8421 8422 if (viatranspose || viamatmatmatmult) { 8423 Mat Pt; 8424 ierr = MatTranspose(P,MAT_INITIAL_MATRIX,&Pt);CHKERRQ(ierr); 8425 if (viamatmatmatmult) { 8426 ierr = MatMatMatMult(Pt,A,P,scall,fill,C);CHKERRQ(ierr); 8427 ierr = PetscInfo(*C,"MatPtAP via MatMatMatMult\n");CHKERRQ(ierr); 8428 } else { 8429 Mat AP; 8430 ierr = MatMatMult(A,P,MAT_INITIAL_MATRIX,fill,&AP);CHKERRQ(ierr); 8431 ierr = MatMatMult(Pt,AP,scall,fill,C);CHKERRQ(ierr); 8432 ierr = MatDestroy(&AP);CHKERRQ(ierr); 8433 ierr = PetscInfo(*C,"MatPtAP via MatTranspose and MatMatMult\n");CHKERRQ(ierr); 8434 } 8435 ierr = MatDestroy(&Pt);CHKERRQ(ierr); 8436 } else { 8437 ierr = PetscLogEventBegin(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); 8438 ierr = (*ptap)(A,P,scall,fill,C);CHKERRQ(ierr); 8439 ierr = PetscLogEventEnd(MAT_PtAP,A,P,0,0);CHKERRQ(ierr); 8440 } 8441 PetscFunctionReturn(0); 8442 } 8443 8444 #undef __FUNCT__ 8445 #define __FUNCT__ "MatPtAPNumeric" 8446 /*@ 8447 MatPtAPNumeric - Computes the matrix product C = P^T * A * P 8448 8449 Neighbor-wise Collective on Mat 8450 8451 Input Parameters: 8452 + A - the matrix 8453 - P - the projection matrix 8454 8455 Output Parameters: 8456 . C - the product matrix 8457 8458 Notes: 8459 C must have been created by calling MatPtAPSymbolic and must be destroyed by 8460 the user using MatDeatroy(). 8461 8462 This routine is currently only implemented for pairs of AIJ matrices and classes 8463 which inherit from AIJ. C will be of type MATAIJ. 8464 8465 Level: intermediate 8466 8467 .seealso: MatPtAP(), MatPtAPSymbolic(), MatMatMultNumeric() 8468 @*/ 8469 PetscErrorCode MatPtAPNumeric(Mat A,Mat P,Mat C) 8470 { 8471 PetscErrorCode ierr; 8472 8473 PetscFunctionBegin; 8474 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8475 PetscValidType(A,1); 8476 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8477 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8478 PetscValidHeaderSpecific(P,MAT_CLASSID,2); 8479 PetscValidType(P,2); 8480 MatCheckPreallocated(P,2); 8481 if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8482 if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8483 PetscValidHeaderSpecific(C,MAT_CLASSID,3); 8484 PetscValidType(C,3); 8485 MatCheckPreallocated(C,3); 8486 if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8487 if (P->cmap->N!=C->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->rmap->N); 8488 if (P->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); 8489 if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); 8490 if (P->cmap->N!=C->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->cmap->N,C->cmap->N); 8491 MatCheckPreallocated(A,1); 8492 8493 ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); 8494 ierr = (*C->ops->ptapnumeric)(A,P,C);CHKERRQ(ierr); 8495 ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr); 8496 PetscFunctionReturn(0); 8497 } 8498 8499 #undef __FUNCT__ 8500 #define __FUNCT__ "MatPtAPSymbolic" 8501 /*@ 8502 MatPtAPSymbolic - Creates the (i,j) structure of the matrix product C = P^T * A * P 8503 8504 Neighbor-wise Collective on Mat 8505 8506 Input Parameters: 8507 + A - the matrix 8508 - P - the projection matrix 8509 8510 Output Parameters: 8511 . C - the (i,j) structure of the product matrix 8512 8513 Notes: 8514 C will be created and must be destroyed by the user with MatDestroy(). 8515 8516 This routine is currently only implemented for pairs of SeqAIJ matrices and classes 8517 which inherit from SeqAIJ. C will be of type MATSEQAIJ. The product is computed using 8518 this (i,j) structure by calling MatPtAPNumeric(). 8519 8520 Level: intermediate 8521 8522 .seealso: MatPtAP(), MatPtAPNumeric(), MatMatMultSymbolic() 8523 @*/ 8524 PetscErrorCode MatPtAPSymbolic(Mat A,Mat P,PetscReal fill,Mat *C) 8525 { 8526 PetscErrorCode ierr; 8527 8528 PetscFunctionBegin; 8529 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8530 PetscValidType(A,1); 8531 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8532 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8533 if (fill <1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8534 PetscValidHeaderSpecific(P,MAT_CLASSID,2); 8535 PetscValidType(P,2); 8536 MatCheckPreallocated(P,2); 8537 if (!P->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8538 if (P->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8539 PetscValidPointer(C,3); 8540 8541 if (P->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",P->rmap->N,A->cmap->N); 8542 if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); 8543 MatCheckPreallocated(A,1); 8544 ierr = PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); 8545 ierr = (*A->ops->ptapsymbolic)(A,P,fill,C);CHKERRQ(ierr); 8546 ierr = PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr); 8547 8548 /* ierr = MatSetBlockSize(*C,A->rmap->bs);CHKERRQ(ierr); NO! this is not always true -ma */ 8549 PetscFunctionReturn(0); 8550 } 8551 8552 #undef __FUNCT__ 8553 #define __FUNCT__ "MatRARt" 8554 /*@ 8555 MatRARt - Creates the matrix product C = R * A * R^T 8556 8557 Neighbor-wise Collective on Mat 8558 8559 Input Parameters: 8560 + A - the matrix 8561 . R - the projection matrix 8562 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 8563 - fill - expected fill as ratio of nnz(C)/nnz(A) 8564 8565 Output Parameters: 8566 . C - the product matrix 8567 8568 Notes: 8569 C will be created and must be destroyed by the user with MatDestroy(). 8570 8571 This routine is currently only implemented for pairs of AIJ matrices and classes 8572 which inherit from AIJ. 8573 8574 Level: intermediate 8575 8576 .seealso: MatRARtSymbolic(), MatRARtNumeric(), MatMatMult(), MatPtAP() 8577 @*/ 8578 PetscErrorCode MatRARt(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C) 8579 { 8580 PetscErrorCode ierr; 8581 8582 PetscFunctionBegin; 8583 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8584 PetscValidType(A,1); 8585 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8586 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8587 PetscValidHeaderSpecific(R,MAT_CLASSID,2); 8588 PetscValidType(R,2); 8589 MatCheckPreallocated(R,2); 8590 if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8591 if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8592 PetscValidPointer(C,3); 8593 if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)R),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); 8594 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8595 MatCheckPreallocated(A,1); 8596 8597 if (!A->ops->rart) { 8598 MatType mattype; 8599 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 8600 SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix of type <%s> does not support RARt",mattype); 8601 } 8602 ierr = PetscLogEventBegin(MAT_RARt,A,R,0,0);CHKERRQ(ierr); 8603 ierr = (*A->ops->rart)(A,R,scall,fill,C);CHKERRQ(ierr); 8604 ierr = PetscLogEventEnd(MAT_RARt,A,R,0,0);CHKERRQ(ierr); 8605 PetscFunctionReturn(0); 8606 } 8607 8608 #undef __FUNCT__ 8609 #define __FUNCT__ "MatRARtNumeric" 8610 /*@ 8611 MatRARtNumeric - Computes the matrix product C = R * A * R^T 8612 8613 Neighbor-wise Collective on Mat 8614 8615 Input Parameters: 8616 + A - the matrix 8617 - R - the projection matrix 8618 8619 Output Parameters: 8620 . C - the product matrix 8621 8622 Notes: 8623 C must have been created by calling MatRARtSymbolic and must be destroyed by 8624 the user using MatDeatroy(). 8625 8626 This routine is currently only implemented for pairs of AIJ matrices and classes 8627 which inherit from AIJ. C will be of type MATAIJ. 8628 8629 Level: intermediate 8630 8631 .seealso: MatRARt(), MatRARtSymbolic(), MatMatMultNumeric() 8632 @*/ 8633 PetscErrorCode MatRARtNumeric(Mat A,Mat R,Mat C) 8634 { 8635 PetscErrorCode ierr; 8636 8637 PetscFunctionBegin; 8638 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8639 PetscValidType(A,1); 8640 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8641 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8642 PetscValidHeaderSpecific(R,MAT_CLASSID,2); 8643 PetscValidType(R,2); 8644 MatCheckPreallocated(R,2); 8645 if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8646 if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8647 PetscValidHeaderSpecific(C,MAT_CLASSID,3); 8648 PetscValidType(C,3); 8649 MatCheckPreallocated(C,3); 8650 if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8651 if (R->rmap->N!=C->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->rmap->N); 8652 if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); 8653 if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); 8654 if (R->rmap->N!=C->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->rmap->N,C->cmap->N); 8655 MatCheckPreallocated(A,1); 8656 8657 ierr = PetscLogEventBegin(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); 8658 ierr = (*A->ops->rartnumeric)(A,R,C);CHKERRQ(ierr); 8659 ierr = PetscLogEventEnd(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); 8660 PetscFunctionReturn(0); 8661 } 8662 8663 #undef __FUNCT__ 8664 #define __FUNCT__ "MatRARtSymbolic" 8665 /*@ 8666 MatRARtSymbolic - Creates the (i,j) structure of the matrix product C = R * A * R^T 8667 8668 Neighbor-wise Collective on Mat 8669 8670 Input Parameters: 8671 + A - the matrix 8672 - R - the projection matrix 8673 8674 Output Parameters: 8675 . C - the (i,j) structure of the product matrix 8676 8677 Notes: 8678 C will be created and must be destroyed by the user with MatDestroy(). 8679 8680 This routine is currently only implemented for pairs of SeqAIJ matrices and classes 8681 which inherit from SeqAIJ. C will be of type MATSEQAIJ. The product is computed using 8682 this (i,j) structure by calling MatRARtNumeric(). 8683 8684 Level: intermediate 8685 8686 .seealso: MatRARt(), MatRARtNumeric(), MatMatMultSymbolic() 8687 @*/ 8688 PetscErrorCode MatRARtSymbolic(Mat A,Mat R,PetscReal fill,Mat *C) 8689 { 8690 PetscErrorCode ierr; 8691 8692 PetscFunctionBegin; 8693 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8694 PetscValidType(A,1); 8695 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8696 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8697 if (fill <1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8698 PetscValidHeaderSpecific(R,MAT_CLASSID,2); 8699 PetscValidType(R,2); 8700 MatCheckPreallocated(R,2); 8701 if (!R->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8702 if (R->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8703 PetscValidPointer(C,3); 8704 8705 if (R->cmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",R->cmap->N,A->rmap->N); 8706 if (A->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix 'A' must be square, %D != %D",A->rmap->N,A->cmap->N); 8707 MatCheckPreallocated(A,1); 8708 ierr = PetscLogEventBegin(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); 8709 ierr = (*A->ops->rartsymbolic)(A,R,fill,C);CHKERRQ(ierr); 8710 ierr = PetscLogEventEnd(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); 8711 8712 ierr = MatSetBlockSize(*C,A->rmap->bs);CHKERRQ(ierr); 8713 PetscFunctionReturn(0); 8714 } 8715 8716 #undef __FUNCT__ 8717 #define __FUNCT__ "MatMatMult" 8718 /*@ 8719 MatMatMult - Performs Matrix-Matrix Multiplication C=A*B. 8720 8721 Neighbor-wise Collective on Mat 8722 8723 Input Parameters: 8724 + A - the left matrix 8725 . B - the right matrix 8726 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 8727 - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate 8728 if the result is a dense matrix this is irrelevent 8729 8730 Output Parameters: 8731 . C - the product matrix 8732 8733 Notes: 8734 Unless scall is MAT_REUSE_MATRIX C will be created. 8735 8736 MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call 8737 8738 To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value 8739 actually needed. 8740 8741 If you have many matrices with the same non-zero structure to multiply, you 8742 should either 8743 $ 1) use MAT_REUSE_MATRIX in all calls but the first or 8744 $ 2) call MatMatMultSymbolic() once and then MatMatMultNumeric() for each product needed 8745 8746 Level: intermediate 8747 8748 .seealso: MatMatMultSymbolic(), MatMatMultNumeric(), MatTransposeMatMult(), MatMatTransposeMult(), MatPtAP() 8749 @*/ 8750 PetscErrorCode MatMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 8751 { 8752 PetscErrorCode ierr; 8753 PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); 8754 PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); 8755 PetscErrorCode (*mult)(Mat,Mat,MatReuse,PetscReal,Mat*)=NULL; 8756 8757 PetscFunctionBegin; 8758 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8759 PetscValidType(A,1); 8760 MatCheckPreallocated(A,1); 8761 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8762 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8763 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 8764 PetscValidType(B,2); 8765 MatCheckPreallocated(B,2); 8766 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8767 if (B->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8768 PetscValidPointer(C,3); 8769 if (B->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); 8770 if (scall == MAT_REUSE_MATRIX) { 8771 PetscValidPointer(*C,5); 8772 PetscValidHeaderSpecific(*C,MAT_CLASSID,5); 8773 ierr = PetscLogEventBegin(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); 8774 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 8775 ierr = (*(*C)->ops->matmultnumeric)(A,B,*C);CHKERRQ(ierr); 8776 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 8777 ierr = PetscLogEventEnd(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); 8778 PetscFunctionReturn(0); 8779 } 8780 if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; 8781 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 8782 8783 fA = A->ops->matmult; 8784 fB = B->ops->matmult; 8785 if (fB == fA) { 8786 if (!fB) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatMatMult not supported for B of type %s",((PetscObject)B)->type_name); 8787 mult = fB; 8788 } else { 8789 /* dispatch based on the type of A and B from their PetscObject's PetscFunctionLists. */ 8790 char multname[256]; 8791 ierr = PetscStrcpy(multname,"MatMatMult_");CHKERRQ(ierr); 8792 ierr = PetscStrcat(multname,((PetscObject)A)->type_name);CHKERRQ(ierr); 8793 ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); 8794 ierr = PetscStrcat(multname,((PetscObject)B)->type_name);CHKERRQ(ierr); 8795 ierr = PetscStrcat(multname,"_C");CHKERRQ(ierr); /* e.g., multname = "MatMatMult_seqdense_seqaij_C" */ 8796 ierr = PetscObjectQueryFunction((PetscObject)B,multname,&mult);CHKERRQ(ierr); 8797 if (!mult) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatMatMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); 8798 } 8799 ierr = PetscLogEventBegin(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); 8800 ierr = (*mult)(A,B,scall,fill,C);CHKERRQ(ierr); 8801 ierr = PetscLogEventEnd(MAT_MatMult,A,B,0,0);CHKERRQ(ierr); 8802 PetscFunctionReturn(0); 8803 } 8804 8805 #undef __FUNCT__ 8806 #define __FUNCT__ "MatMatMultSymbolic" 8807 /*@ 8808 MatMatMultSymbolic - Performs construction, preallocation, and computes the ij structure 8809 of the matrix-matrix product C=A*B. Call this routine before calling MatMatMultNumeric(). 8810 8811 Neighbor-wise Collective on Mat 8812 8813 Input Parameters: 8814 + A - the left matrix 8815 . B - the right matrix 8816 - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if you do not have a good estimate, 8817 if C is a dense matrix this is irrelevent 8818 8819 Output Parameters: 8820 . C - the product matrix 8821 8822 Notes: 8823 Unless scall is MAT_REUSE_MATRIX C will be created. 8824 8825 To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value 8826 actually needed. 8827 8828 This routine is currently implemented for 8829 - pairs of AIJ matrices and classes which inherit from AIJ, C will be of type AIJ 8830 - pairs of AIJ (A) and Dense (B) matrix, C will be of type Dense. 8831 - pairs of Dense (A) and AIJ (B) matrix, C will be of type Dense. 8832 8833 Level: intermediate 8834 8835 Developers Note: There are ways to estimate the number of nonzeros in the resulting product, see for example, http://arxiv.org/abs/1006.4173 8836 We should incorporate them into PETSc. 8837 8838 .seealso: MatMatMult(), MatMatMultNumeric() 8839 @*/ 8840 PetscErrorCode MatMatMultSymbolic(Mat A,Mat B,PetscReal fill,Mat *C) 8841 { 8842 PetscErrorCode ierr; 8843 PetscErrorCode (*Asymbolic)(Mat,Mat,PetscReal,Mat*); 8844 PetscErrorCode (*Bsymbolic)(Mat,Mat,PetscReal,Mat*); 8845 PetscErrorCode (*symbolic)(Mat,Mat,PetscReal,Mat*)=NULL; 8846 8847 PetscFunctionBegin; 8848 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8849 PetscValidType(A,1); 8850 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8851 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8852 8853 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 8854 PetscValidType(B,2); 8855 MatCheckPreallocated(B,2); 8856 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8857 if (B->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8858 PetscValidPointer(C,3); 8859 8860 if (B->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); 8861 if (fill == PETSC_DEFAULT) fill = 2.0; 8862 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); 8863 MatCheckPreallocated(A,1); 8864 8865 Asymbolic = A->ops->matmultsymbolic; 8866 Bsymbolic = B->ops->matmultsymbolic; 8867 if (Asymbolic == Bsymbolic) { 8868 if (!Bsymbolic) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"C=A*B not implemented for B of type %s",((PetscObject)B)->type_name); 8869 symbolic = Bsymbolic; 8870 } else { /* dispatch based on the type of A and B */ 8871 char symbolicname[256]; 8872 ierr = PetscStrcpy(symbolicname,"MatMatMultSymbolic_");CHKERRQ(ierr); 8873 ierr = PetscStrcat(symbolicname,((PetscObject)A)->type_name);CHKERRQ(ierr); 8874 ierr = PetscStrcat(symbolicname,"_");CHKERRQ(ierr); 8875 ierr = PetscStrcat(symbolicname,((PetscObject)B)->type_name);CHKERRQ(ierr); 8876 ierr = PetscStrcat(symbolicname,"_C");CHKERRQ(ierr); 8877 ierr = PetscObjectQueryFunction((PetscObject)B,symbolicname,&symbolic);CHKERRQ(ierr); 8878 if (!symbolic) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatMatMultSymbolic requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); 8879 } 8880 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 8881 ierr = (*symbolic)(A,B,fill,C);CHKERRQ(ierr); 8882 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 8883 PetscFunctionReturn(0); 8884 } 8885 8886 #undef __FUNCT__ 8887 #define __FUNCT__ "MatMatMultNumeric" 8888 /*@ 8889 MatMatMultNumeric - Performs the numeric matrix-matrix product. 8890 Call this routine after first calling MatMatMultSymbolic(). 8891 8892 Neighbor-wise Collective on Mat 8893 8894 Input Parameters: 8895 + A - the left matrix 8896 - B - the right matrix 8897 8898 Output Parameters: 8899 . C - the product matrix, which was created by from MatMatMultSymbolic() or a call to MatMatMult(). 8900 8901 Notes: 8902 C must have been created with MatMatMultSymbolic(). 8903 8904 This routine is currently implemented for 8905 - pairs of AIJ matrices and classes which inherit from AIJ, C will be of type MATAIJ. 8906 - pairs of AIJ (A) and Dense (B) matrix, C will be of type Dense. 8907 - pairs of Dense (A) and AIJ (B) matrix, C will be of type Dense. 8908 8909 Level: intermediate 8910 8911 .seealso: MatMatMult(), MatMatMultSymbolic() 8912 @*/ 8913 PetscErrorCode MatMatMultNumeric(Mat A,Mat B,Mat C) 8914 { 8915 PetscErrorCode ierr; 8916 8917 PetscFunctionBegin; 8918 ierr = MatMatMult(A,B,MAT_REUSE_MATRIX,0.0,&C);CHKERRQ(ierr); 8919 PetscFunctionReturn(0); 8920 } 8921 8922 #undef __FUNCT__ 8923 #define __FUNCT__ "MatMatTransposeMult" 8924 /*@ 8925 MatMatTransposeMult - Performs Matrix-Matrix Multiplication C=A*B^T. 8926 8927 Neighbor-wise Collective on Mat 8928 8929 Input Parameters: 8930 + A - the left matrix 8931 . B - the right matrix 8932 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 8933 - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known 8934 8935 Output Parameters: 8936 . C - the product matrix 8937 8938 Notes: 8939 C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy(). 8940 8941 MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call 8942 8943 To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value 8944 actually needed. 8945 8946 This routine is currently only implemented for pairs of SeqAIJ matrices. C will be of type MATSEQAIJ. 8947 8948 Level: intermediate 8949 8950 .seealso: MatMatTransposeMultSymbolic(), MatMatTransposeMultNumeric(), MatMatMult(), MatTransposeMatMult() MatPtAP() 8951 @*/ 8952 PetscErrorCode MatMatTransposeMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 8953 { 8954 PetscErrorCode ierr; 8955 PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); 8956 PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); 8957 8958 PetscFunctionBegin; 8959 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 8960 PetscValidType(A,1); 8961 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8962 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8963 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 8964 PetscValidType(B,2); 8965 MatCheckPreallocated(B,2); 8966 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 8967 if (B->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 8968 PetscValidPointer(C,3); 8969 if (B->cmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, AN %D != BN %D",A->cmap->N,B->cmap->N); 8970 if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; 8971 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); 8972 MatCheckPreallocated(A,1); 8973 8974 fA = A->ops->mattransposemult; 8975 if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatMatTransposeMult not supported for A of type %s",((PetscObject)A)->type_name); 8976 fB = B->ops->mattransposemult; 8977 if (!fB) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatMatTransposeMult not supported for B of type %s",((PetscObject)B)->type_name); 8978 if (fB!=fA) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatMatTransposeMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); 8979 8980 ierr = PetscLogEventBegin(MAT_MatTransposeMult,A,B,0,0);CHKERRQ(ierr); 8981 if (scall == MAT_INITIAL_MATRIX) { 8982 ierr = PetscLogEventBegin(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 8983 ierr = (*A->ops->mattransposemultsymbolic)(A,B,fill,C);CHKERRQ(ierr); 8984 ierr = PetscLogEventEnd(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 8985 } 8986 ierr = PetscLogEventBegin(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 8987 ierr = (*A->ops->mattransposemultnumeric)(A,B,*C);CHKERRQ(ierr); 8988 ierr = PetscLogEventEnd(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 8989 ierr = PetscLogEventEnd(MAT_MatTransposeMult,A,B,0,0);CHKERRQ(ierr); 8990 PetscFunctionReturn(0); 8991 } 8992 8993 #undef __FUNCT__ 8994 #define __FUNCT__ "MatTransposeMatMult" 8995 /*@ 8996 MatTransposeMatMult - Performs Matrix-Matrix Multiplication C=A^T*B. 8997 8998 Neighbor-wise Collective on Mat 8999 9000 Input Parameters: 9001 + A - the left matrix 9002 . B - the right matrix 9003 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 9004 - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use PETSC_DEFAULT if not known 9005 9006 Output Parameters: 9007 . C - the product matrix 9008 9009 Notes: 9010 C will be created if MAT_INITIAL_MATRIX and must be destroyed by the user with MatDestroy(). 9011 9012 MAT_REUSE_MATRIX can only be used if the matrices A and B have the same nonzero pattern as in the previous call 9013 9014 To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value 9015 actually needed. 9016 9017 This routine is currently implemented for pairs of AIJ matrices and pairs of SeqDense matrices and classes 9018 which inherit from SeqAIJ. C will be of same type as the input matrices. 9019 9020 Level: intermediate 9021 9022 .seealso: MatTransposeMatMultSymbolic(), MatTransposeMatMultNumeric(), MatMatMult(), MatMatTransposeMult(), MatPtAP() 9023 @*/ 9024 PetscErrorCode MatTransposeMatMult(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 9025 { 9026 PetscErrorCode ierr; 9027 PetscErrorCode (*fA)(Mat,Mat,MatReuse,PetscReal,Mat*); 9028 PetscErrorCode (*fB)(Mat,Mat,MatReuse,PetscReal,Mat*); 9029 PetscErrorCode (*transposematmult)(Mat,Mat,MatReuse,PetscReal,Mat*) = NULL; 9030 9031 PetscFunctionBegin; 9032 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 9033 PetscValidType(A,1); 9034 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9035 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9036 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 9037 PetscValidType(B,2); 9038 MatCheckPreallocated(B,2); 9039 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9040 if (B->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9041 PetscValidPointer(C,3); 9042 if (B->rmap->N!=A->rmap->N) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->rmap->N); 9043 if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; 9044 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be > 1.0",fill); 9045 MatCheckPreallocated(A,1); 9046 9047 fA = A->ops->transposematmult; 9048 fB = B->ops->transposematmult; 9049 if (fB==fA) { 9050 if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatTransposeMatMult not supported for A of type %s",((PetscObject)A)->type_name); 9051 transposematmult = fA; 9052 } else { 9053 /* dispatch based on the type of A and B from their PetscObject's PetscFunctionLists. */ 9054 char multname[256]; 9055 ierr = PetscStrcpy(multname,"MatTransposeMatMult_");CHKERRQ(ierr); 9056 ierr = PetscStrcat(multname,((PetscObject)A)->type_name);CHKERRQ(ierr); 9057 ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); 9058 ierr = PetscStrcat(multname,((PetscObject)B)->type_name);CHKERRQ(ierr); 9059 ierr = PetscStrcat(multname,"_C");CHKERRQ(ierr); /* e.g., multname = "MatMatMult_seqdense_seqaij_C" */ 9060 ierr = PetscObjectQueryFunction((PetscObject)B,multname,&transposematmult);CHKERRQ(ierr); 9061 if (!transposematmult) SETERRQ2(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatTransposeMatMult requires A, %s, to be compatible with B, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name); 9062 } 9063 ierr = PetscLogEventBegin(MAT_TransposeMatMult,A,B,0,0);CHKERRQ(ierr); 9064 ierr = (*transposematmult)(A,B,scall,fill,C);CHKERRQ(ierr); 9065 ierr = PetscLogEventEnd(MAT_TransposeMatMult,A,B,0,0);CHKERRQ(ierr); 9066 PetscFunctionReturn(0); 9067 } 9068 9069 #undef __FUNCT__ 9070 #define __FUNCT__ "MatMatMatMult" 9071 /*@ 9072 MatMatMatMult - Performs Matrix-Matrix-Matrix Multiplication D=A*B*C. 9073 9074 Neighbor-wise Collective on Mat 9075 9076 Input Parameters: 9077 + A - the left matrix 9078 . B - the middle matrix 9079 . C - the right matrix 9080 . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 9081 - fill - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use PETSC_DEFAULT if you do not have a good estimate 9082 if the result is a dense matrix this is irrelevent 9083 9084 Output Parameters: 9085 . D - the product matrix 9086 9087 Notes: 9088 Unless scall is MAT_REUSE_MATRIX D will be created. 9089 9090 MAT_REUSE_MATRIX can only be used if the matrices A, B and C have the same nonzero pattern as in the previous call 9091 9092 To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value 9093 actually needed. 9094 9095 If you have many matrices with the same non-zero structure to multiply, you 9096 should either 9097 $ 1) use MAT_REUSE_MATRIX in all calls but the first or 9098 $ 2) call MatMatMatMultSymbolic() once and then MatMatMatMultNumeric() for each product needed 9099 9100 Level: intermediate 9101 9102 .seealso: MatMatMult, MatPtAP() 9103 @*/ 9104 PetscErrorCode MatMatMatMult(Mat A,Mat B,Mat C,MatReuse scall,PetscReal fill,Mat *D) 9105 { 9106 PetscErrorCode ierr; 9107 PetscErrorCode (*fA)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); 9108 PetscErrorCode (*fB)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); 9109 PetscErrorCode (*fC)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*); 9110 PetscErrorCode (*mult)(Mat,Mat,Mat,MatReuse,PetscReal,Mat*)=NULL; 9111 9112 PetscFunctionBegin; 9113 PetscValidHeaderSpecific(A,MAT_CLASSID,1); 9114 PetscValidType(A,1); 9115 MatCheckPreallocated(A,1); 9116 if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9117 if (A->factortype) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9118 PetscValidHeaderSpecific(B,MAT_CLASSID,2); 9119 PetscValidType(B,2); 9120 MatCheckPreallocated(B,2); 9121 if (!B->assembled) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9122 if (B->factortype) SETERRQ(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9123 PetscValidHeaderSpecific(C,MAT_CLASSID,3); 9124 PetscValidPointer(C,3); 9125 MatCheckPreallocated(C,3); 9126 if (!C->assembled) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9127 if (C->factortype) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9128 if (B->rmap->N!=A->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",B->rmap->N,A->cmap->N); 9129 if (C->rmap->N!=B->cmap->N) SETERRQ2(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_SIZ,"Matrix dimensions are incompatible, %D != %D",C->rmap->N,B->cmap->N); 9130 if (scall == MAT_REUSE_MATRIX) { 9131 PetscValidPointer(*D,6); 9132 PetscValidHeaderSpecific(*D,MAT_CLASSID,6); 9133 ierr = PetscLogEventBegin(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); 9134 ierr = (*(*D)->ops->matmatmult)(A,B,C,scall,fill,D);CHKERRQ(ierr); 9135 ierr = PetscLogEventEnd(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); 9136 PetscFunctionReturn(0); 9137 } 9138 if (fill == PETSC_DEFAULT || fill == PETSC_DECIDE) fill = 2.0; 9139 if (fill < 1.0) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Expected fill=%G must be >= 1.0",fill); 9140 9141 fA = A->ops->matmatmult; 9142 fB = B->ops->matmatmult; 9143 fC = C->ops->matmatmult; 9144 if (fA == fB && fA == fC) { 9145 if (!fA) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"MatMatMatMult not supported for A of type %s",((PetscObject)A)->type_name); 9146 mult = fA; 9147 } else { 9148 /* dispatch based on the type of A, B and C from their PetscObject's PetscFunctionLists. */ 9149 char multname[256]; 9150 ierr = PetscStrcpy(multname,"MatMatMatMult_");CHKERRQ(ierr); 9151 ierr = PetscStrcat(multname,((PetscObject)A)->type_name);CHKERRQ(ierr); 9152 ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); 9153 ierr = PetscStrcat(multname,((PetscObject)B)->type_name);CHKERRQ(ierr); 9154 ierr = PetscStrcat(multname,"_");CHKERRQ(ierr); 9155 ierr = PetscStrcat(multname,((PetscObject)C)->type_name);CHKERRQ(ierr); 9156 ierr = PetscStrcat(multname,"_C");CHKERRQ(ierr); 9157 ierr = PetscObjectQueryFunction((PetscObject)B,multname,&mult);CHKERRQ(ierr); 9158 if (!mult) SETERRQ3(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_INCOMP,"MatMatMatMult requires A, %s, to be compatible with B, %s, C, %s",((PetscObject)A)->type_name,((PetscObject)B)->type_name,((PetscObject)C)->type_name); 9159 } 9160 ierr = PetscLogEventBegin(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); 9161 ierr = (*mult)(A,B,C,scall,fill,D);CHKERRQ(ierr); 9162 ierr = PetscLogEventEnd(MAT_MatMatMult,A,B,0,0);CHKERRQ(ierr); 9163 PetscFunctionReturn(0); 9164 } 9165 9166 #undef __FUNCT__ 9167 #define __FUNCT__ "MatGetRedundantMatrix" 9168 /*@C 9169 MatGetRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators. 9170 9171 Collective on Mat 9172 9173 Input Parameters: 9174 + mat - the matrix 9175 . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices) 9176 . subcomm - MPI communicator split from the communicator where mat resides in (or MPI_COMM_NULL if nsubcomm is used) 9177 - reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 9178 9179 Output Parameter: 9180 . matredundant - redundant matrix 9181 9182 Notes: 9183 MAT_REUSE_MATRIX can only be used when the nonzero structure of the 9184 original matrix has not changed from that last call to MatGetRedundantMatrix(). 9185 9186 This routine creates the duplicated matrices in subcommunicators; you should NOT create them before 9187 calling it. 9188 9189 Only MPIAIJ matrix is supported. 9190 9191 Level: advanced 9192 9193 Concepts: subcommunicator 9194 Concepts: duplicate matrix 9195 9196 .seealso: MatDestroy() 9197 @*/ 9198 PetscErrorCode MatGetRedundantMatrix(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,MatReuse reuse,Mat *matredundant) 9199 { 9200 PetscErrorCode ierr; 9201 9202 PetscFunctionBegin; 9203 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 9204 if (nsubcomm && reuse == MAT_REUSE_MATRIX) { 9205 PetscValidPointer(*matredundant,5); 9206 PetscValidHeaderSpecific(*matredundant,MAT_CLASSID,5); 9207 } 9208 if (!mat->ops->getredundantmatrix) SETERRQ1(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Mat type %s",((PetscObject)mat)->type_name); 9209 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9210 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9211 MatCheckPreallocated(mat,1); 9212 9213 ierr = PetscLogEventBegin(MAT_GetRedundantMatrix,mat,0,0,0);CHKERRQ(ierr); 9214 ierr = (*mat->ops->getredundantmatrix)(mat,nsubcomm,subcomm,reuse,matredundant);CHKERRQ(ierr); 9215 ierr = PetscLogEventEnd(MAT_GetRedundantMatrix,mat,0,0,0);CHKERRQ(ierr); 9216 PetscFunctionReturn(0); 9217 } 9218 9219 #undef __FUNCT__ 9220 #define __FUNCT__ "MatGetMultiProcBlock" 9221 /*@C 9222 MatGetMultiProcBlock - Create multiple [bjacobi] 'parallel submatrices' from 9223 a given 'mat' object. Each submatrix can span multiple procs. 9224 9225 Collective on Mat 9226 9227 Input Parameters: 9228 + mat - the matrix 9229 . subcomm - the subcommunicator obtained by com_split(comm) 9230 - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 9231 9232 Output Parameter: 9233 . subMat - 'parallel submatrices each spans a given subcomm 9234 9235 Notes: 9236 The submatrix partition across processors is dictated by 'subComm' a 9237 communicator obtained by com_split(comm). The comm_split 9238 is not restriced to be grouped with consecutive original ranks. 9239 9240 Due the comm_split() usage, the parallel layout of the submatrices 9241 map directly to the layout of the original matrix [wrt the local 9242 row,col partitioning]. So the original 'DiagonalMat' naturally maps 9243 into the 'DiagonalMat' of the subMat, hence it is used directly from 9244 the subMat. However the offDiagMat looses some columns - and this is 9245 reconstructed with MatSetValues() 9246 9247 Level: advanced 9248 9249 Concepts: subcommunicator 9250 Concepts: submatrices 9251 9252 .seealso: MatGetSubMatrices() 9253 @*/ 9254 PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall,Mat *subMat) 9255 { 9256 PetscErrorCode ierr; 9257 PetscMPIInt commsize,subCommSize; 9258 9259 PetscFunctionBegin; 9260 ierr = MPI_Comm_size(PetscObjectComm((PetscObject)mat),&commsize);CHKERRQ(ierr); 9261 ierr = MPI_Comm_size(subComm,&subCommSize);CHKERRQ(ierr); 9262 if (subCommSize > commsize) SETERRQ2(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_OUTOFRANGE,"CommSize %D < SubCommZize %D",commsize,subCommSize); 9263 9264 ierr = PetscLogEventBegin(MAT_GetMultiProcBlock,mat,0,0,0);CHKERRQ(ierr); 9265 ierr = (*mat->ops->getmultiprocblock)(mat,subComm,scall,subMat);CHKERRQ(ierr); 9266 ierr = PetscLogEventEnd(MAT_GetMultiProcBlock,mat,0,0,0);CHKERRQ(ierr); 9267 PetscFunctionReturn(0); 9268 } 9269 9270 #undef __FUNCT__ 9271 #define __FUNCT__ "MatGetLocalSubMatrix" 9272 /*@ 9273 MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering 9274 9275 Not Collective 9276 9277 Input Arguments: 9278 mat - matrix to extract local submatrix from 9279 isrow - local row indices for submatrix 9280 iscol - local column indices for submatrix 9281 9282 Output Arguments: 9283 submat - the submatrix 9284 9285 Level: intermediate 9286 9287 Notes: 9288 The submat should be returned with MatRestoreLocalSubMatrix(). 9289 9290 Depending on the format of mat, the returned submat may not implement MatMult(). Its communicator may be 9291 the same as mat, it may be PETSC_COMM_SELF, or some other subcomm of mat's. 9292 9293 The submat always implements MatSetValuesLocal(). If isrow and iscol have the same block size, then 9294 MatSetValuesBlockedLocal() will also be implemented. 9295 9296 .seealso: MatRestoreLocalSubMatrix(), MatCreateLocalRef() 9297 @*/ 9298 PetscErrorCode MatGetLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat) 9299 { 9300 PetscErrorCode ierr; 9301 9302 PetscFunctionBegin; 9303 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 9304 PetscValidHeaderSpecific(isrow,IS_CLASSID,2); 9305 PetscValidHeaderSpecific(iscol,IS_CLASSID,3); 9306 PetscCheckSameComm(isrow,2,iscol,3); 9307 PetscValidPointer(submat,4); 9308 9309 if (mat->ops->getlocalsubmatrix) { 9310 ierr = (*mat->ops->getlocalsubmatrix)(mat,isrow,iscol,submat);CHKERRQ(ierr); 9311 } else { 9312 ierr = MatCreateLocalRef(mat,isrow,iscol,submat);CHKERRQ(ierr); 9313 } 9314 PetscFunctionReturn(0); 9315 } 9316 9317 #undef __FUNCT__ 9318 #define __FUNCT__ "MatRestoreLocalSubMatrix" 9319 /*@ 9320 MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering 9321 9322 Not Collective 9323 9324 Input Arguments: 9325 mat - matrix to extract local submatrix from 9326 isrow - local row indices for submatrix 9327 iscol - local column indices for submatrix 9328 submat - the submatrix 9329 9330 Level: intermediate 9331 9332 .seealso: MatGetLocalSubMatrix() 9333 @*/ 9334 PetscErrorCode MatRestoreLocalSubMatrix(Mat mat,IS isrow,IS iscol,Mat *submat) 9335 { 9336 PetscErrorCode ierr; 9337 9338 PetscFunctionBegin; 9339 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 9340 PetscValidHeaderSpecific(isrow,IS_CLASSID,2); 9341 PetscValidHeaderSpecific(iscol,IS_CLASSID,3); 9342 PetscCheckSameComm(isrow,2,iscol,3); 9343 PetscValidPointer(submat,4); 9344 if (*submat) { 9345 PetscValidHeaderSpecific(*submat,MAT_CLASSID,4); 9346 } 9347 9348 if (mat->ops->restorelocalsubmatrix) { 9349 ierr = (*mat->ops->restorelocalsubmatrix)(mat,isrow,iscol,submat);CHKERRQ(ierr); 9350 } else { 9351 ierr = MatDestroy(submat);CHKERRQ(ierr); 9352 } 9353 *submat = NULL; 9354 PetscFunctionReturn(0); 9355 } 9356 9357 /* --------------------------------------------------------*/ 9358 #undef __FUNCT__ 9359 #define __FUNCT__ "MatFindZeroDiagonals" 9360 /*@ 9361 MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no entry in the matrix 9362 9363 Collective on Mat 9364 9365 Input Parameter: 9366 . mat - the matrix 9367 9368 Output Parameter: 9369 . is - if any rows have zero diagonals this contains the list of them 9370 9371 Level: developer 9372 9373 Concepts: matrix-vector product 9374 9375 .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 9376 @*/ 9377 PetscErrorCode MatFindZeroDiagonals(Mat mat,IS *is) 9378 { 9379 PetscErrorCode ierr; 9380 9381 PetscFunctionBegin; 9382 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 9383 PetscValidType(mat,1); 9384 if (!mat->assembled) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9385 if (mat->factortype) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9386 9387 if (!mat->ops->findzerodiagonals) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"This matrix type does not have a find zero diagonals defined"); 9388 ierr = (*mat->ops->findzerodiagonals)(mat,is);CHKERRQ(ierr); 9389 PetscFunctionReturn(0); 9390 } 9391 9392 #undef __FUNCT__ 9393 #define __FUNCT__ "MatInvertBlockDiagonal" 9394 /*@C 9395 MatInvertBlockDiagonal - Inverts the block diagonal entries. 9396 9397 Collective on Mat 9398 9399 Input Parameters: 9400 . mat - the matrix 9401 9402 Output Parameters: 9403 . values - the block inverses in column major order (FORTRAN-like) 9404 9405 Note: 9406 This routine is not available from Fortran. 9407 9408 Level: advanced 9409 @*/ 9410 PetscErrorCode MatInvertBlockDiagonal(Mat mat,const PetscScalar **values) 9411 { 9412 PetscErrorCode ierr; 9413 9414 PetscFunctionBegin; 9415 PetscValidHeaderSpecific(mat,MAT_CLASSID,1); 9416 if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 9417 if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 9418 if (!mat->ops->invertblockdiagonal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported"); 9419 ierr = (*mat->ops->invertblockdiagonal)(mat,values);CHKERRQ(ierr); 9420 PetscFunctionReturn(0); 9421 } 9422 9423 #undef __FUNCT__ 9424 #define __FUNCT__ "MatTransposeColoringDestroy" 9425 /*@C 9426 MatTransposeColoringDestroy - Destroys a coloring context for matrix product C=A*B^T that was created 9427 via MatTransposeColoringCreate(). 9428 9429 Collective on MatTransposeColoring 9430 9431 Input Parameter: 9432 . c - coloring context 9433 9434 Level: intermediate 9435 9436 .seealso: MatTransposeColoringCreate() 9437 @*/ 9438 PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c) 9439 { 9440 PetscErrorCode ierr; 9441 MatTransposeColoring matcolor=*c; 9442 9443 PetscFunctionBegin; 9444 if (!matcolor) PetscFunctionReturn(0); 9445 if (--((PetscObject)matcolor)->refct > 0) {matcolor = 0; PetscFunctionReturn(0);} 9446 9447 ierr = PetscFree3(matcolor->ncolumns,matcolor->nrows,matcolor->colorforrow);CHKERRQ(ierr); 9448 ierr = PetscFree(matcolor->rows);CHKERRQ(ierr); 9449 ierr = PetscFree(matcolor->den2sp);CHKERRQ(ierr); 9450 ierr = PetscFree(matcolor->colorforcol);CHKERRQ(ierr); 9451 ierr = PetscFree(matcolor->columns);CHKERRQ(ierr); 9452 if (matcolor->brows>0) { 9453 ierr = PetscFree(matcolor->lstart);CHKERRQ(ierr); 9454 } 9455 ierr = PetscHeaderDestroy(c);CHKERRQ(ierr); 9456 PetscFunctionReturn(0); 9457 } 9458 9459 #undef __FUNCT__ 9460 #define __FUNCT__ "MatTransColoringApplySpToDen" 9461 /*@C 9462 MatTransColoringApplySpToDen - Given a symbolic matrix product C=A*B^T for which 9463 a MatTransposeColoring context has been created, computes a dense B^T by Apply 9464 MatTransposeColoring to sparse B. 9465 9466 Collective on MatTransposeColoring 9467 9468 Input Parameters: 9469 + B - sparse matrix B 9470 . Btdense - symbolic dense matrix B^T 9471 - coloring - coloring context created with MatTransposeColoringCreate() 9472 9473 Output Parameter: 9474 . Btdense - dense matrix B^T 9475 9476 Options Database Keys: 9477 + -mat_transpose_coloring_view - Activates basic viewing or coloring 9478 . -mat_transpose_coloring_view_draw - Activates drawing of coloring 9479 - -mat_transpose_coloring_view_info - Activates viewing of coloring info 9480 9481 Level: intermediate 9482 9483 .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy() 9484 9485 .keywords: coloring 9486 @*/ 9487 PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring,Mat B,Mat Btdense) 9488 { 9489 PetscErrorCode ierr; 9490 9491 PetscFunctionBegin; 9492 PetscValidHeaderSpecific(B,MAT_CLASSID,1); 9493 PetscValidHeaderSpecific(Btdense,MAT_CLASSID,2); 9494 PetscValidHeaderSpecific(coloring,MAT_TRANSPOSECOLORING_CLASSID,3); 9495 9496 if (!B->ops->transcoloringapplysptoden) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)B)->type_name); 9497 ierr = (B->ops->transcoloringapplysptoden)(coloring,B,Btdense);CHKERRQ(ierr); 9498 PetscFunctionReturn(0); 9499 } 9500 9501 #undef __FUNCT__ 9502 #define __FUNCT__ "MatTransColoringApplyDenToSp" 9503 /*@C 9504 MatTransColoringApplyDenToSp - Given a symbolic matrix product Csp=A*B^T for which 9505 a MatTransposeColoring context has been created and a dense matrix Cden=A*Btdense 9506 in which Btdens is obtained from MatTransColoringApplySpToDen(), recover sparse matrix 9507 Csp from Cden. 9508 9509 Collective on MatTransposeColoring 9510 9511 Input Parameters: 9512 + coloring - coloring context created with MatTransposeColoringCreate() 9513 - Cden - matrix product of a sparse matrix and a dense matrix Btdense 9514 9515 Output Parameter: 9516 . Csp - sparse matrix 9517 9518 Options Database Keys: 9519 + -mat_multtranspose_coloring_view - Activates basic viewing or coloring 9520 . -mat_multtranspose_coloring_view_draw - Activates drawing of coloring 9521 - -mat_multtranspose_coloring_view_info - Activates viewing of coloring info 9522 9523 Level: intermediate 9524 9525 .seealso: MatTransposeColoringCreate(), MatTransposeColoringDestroy(), MatTransColoringApplySpToDen() 9526 9527 .keywords: coloring 9528 @*/ 9529 PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring,Mat Cden,Mat Csp) 9530 { 9531 PetscErrorCode ierr; 9532 9533 PetscFunctionBegin; 9534 PetscValidHeaderSpecific(matcoloring,MAT_TRANSPOSECOLORING_CLASSID,1); 9535 PetscValidHeaderSpecific(Cden,MAT_CLASSID,2); 9536 PetscValidHeaderSpecific(Csp,MAT_CLASSID,3); 9537 9538 if (!Csp->ops->transcoloringapplydentosp) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)Csp)->type_name); 9539 ierr = (Csp->ops->transcoloringapplydentosp)(matcoloring,Cden,Csp);CHKERRQ(ierr); 9540 PetscFunctionReturn(0); 9541 } 9542 9543 #undef __FUNCT__ 9544 #define __FUNCT__ "MatTransposeColoringCreate" 9545 /*@C 9546 MatTransposeColoringCreate - Creates a matrix coloring context for matrix product C=A*B^T. 9547 9548 Collective on Mat 9549 9550 Input Parameters: 9551 + mat - the matrix product C 9552 - iscoloring - the coloring of the matrix; usually obtained with MatGetColoring() or DMCreateColoring() 9553 9554 Output Parameter: 9555 . color - the new coloring context 9556 9557 Level: intermediate 9558 9559 .seealso: MatTransposeColoringDestroy(), MatTransposeColoringSetFromOptions(), MatTransColoringApplySpToDen(), 9560 MatTransColoringApplyDenToSp(), MatTransposeColoringView(), 9561 @*/ 9562 PetscErrorCode MatTransposeColoringCreate(Mat mat,ISColoring iscoloring,MatTransposeColoring *color) 9563 { 9564 MatTransposeColoring c; 9565 MPI_Comm comm; 9566 PetscErrorCode ierr; 9567 9568 PetscFunctionBegin; 9569 ierr = PetscLogEventBegin(MAT_TransposeColoringCreate,mat,0,0,0);CHKERRQ(ierr); 9570 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 9571 ierr = PetscHeaderCreate(c,_p_MatTransposeColoring,int,MAT_TRANSPOSECOLORING_CLASSID,"MatTransposeColoring","Matrix product C=A*B^T via coloring","Mat",comm,MatTransposeColoringDestroy,0);CHKERRQ(ierr); 9572 9573 c->ctype = iscoloring->ctype; 9574 if (mat->ops->transposecoloringcreate) { 9575 ierr = (*mat->ops->transposecoloringcreate)(mat,iscoloring,c);CHKERRQ(ierr); 9576 } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"Code not yet written for this matrix type"); 9577 9578 *color = c; 9579 ierr = PetscLogEventEnd(MAT_TransposeColoringCreate,mat,0,0,0);CHKERRQ(ierr); 9580 PetscFunctionReturn(0); 9581 } 9582