1 2 /* 3 This is where the abstract matrix operations are defined 4 */ 5 6 #include "src/mat/matimpl.h" /*I "petscmat.h" I*/ 7 #include "vecimpl.h" 8 9 /* Logging support */ 10 PetscCookie MAT_COOKIE = 0, MATSNESMFCTX_COOKIE = 0; 11 PetscEvent MAT_Mult = 0, MAT_MultMatrixFree = 0, MAT_Mults = 0, MAT_MultConstrained = 0, MAT_MultAdd = 0, MAT_MultTranspose = 0; 12 PetscEvent MAT_MultTransposeConstrained = 0, MAT_MultTransposeAdd = 0, MAT_Solve = 0, MAT_Solves = 0, MAT_SolveAdd = 0, MAT_SolveTranspose = 0; 13 PetscEvent MAT_SolveTransposeAdd = 0, MAT_Relax = 0, MAT_ForwardSolve = 0, MAT_BackwardSolve = 0, MAT_LUFactor = 0, MAT_LUFactorSymbolic = 0; 14 PetscEvent MAT_LUFactorNumeric = 0, MAT_CholeskyFactor = 0, MAT_CholeskyFactorSymbolic = 0, MAT_CholeskyFactorNumeric = 0, MAT_ILUFactor = 0; 15 PetscEvent MAT_ILUFactorSymbolic = 0, MAT_ICCFactorSymbolic = 0, MAT_Copy = 0, MAT_Convert = 0, MAT_Scale = 0, MAT_AssemblyBegin = 0; 16 PetscEvent MAT_AssemblyEnd = 0, MAT_SetValues = 0, MAT_GetValues = 0, MAT_GetRow = 0, MAT_GetSubMatrices = 0, MAT_GetColoring = 0, MAT_GetOrdering = 0; 17 PetscEvent MAT_IncreaseOverlap = 0, MAT_Partitioning = 0, MAT_ZeroEntries = 0, MAT_Load = 0, MAT_View = 0, MAT_AXPY = 0, MAT_FDColoringCreate = 0; 18 PetscEvent MAT_FDColoringApply = 0,MAT_Transpose = 0,MAT_FDColoringFunction = 0; 19 PetscEvent MAT_MatMult = 0, MAT_MatMultSymbolic = 0, MAT_MatMultNumeric = 0; 20 PetscEvent MAT_PtAP = 0, MAT_PtAPSymbolic = 0, MAT_PtAPNumeric = 0; 21 PetscEvent MAT_MatMultTranspose = 0, MAT_MatMultTransposeSymbolic = 0, MAT_MatMultTransposeNumeric = 0; 22 23 /* nasty global values for MatSetValue() */ 24 PetscInt MatSetValue_Row = 0, MatSetValue_Column = 0; 25 PetscScalar MatSetValue_Value = 0.0; 26 27 #undef __FUNCT__ 28 #define __FUNCT__ "MatGetRow" 29 /*@C 30 MatGetRow - Gets a row of a matrix. You MUST call MatRestoreRow() 31 for each row that you get to ensure that your application does 32 not bleed memory. 33 34 Not Collective 35 36 Input Parameters: 37 + mat - the matrix 38 - row - the row to get 39 40 Output Parameters: 41 + ncols - if not NULL, the number of nonzeros in the row 42 . cols - if not NULL, the column numbers 43 - vals - if not NULL, the values 44 45 Notes: 46 This routine is provided for people who need to have direct access 47 to the structure of a matrix. We hope that we provide enough 48 high-level matrix routines that few users will need it. 49 50 MatGetRow() always returns 0-based column indices, regardless of 51 whether the internal representation is 0-based (default) or 1-based. 52 53 For better efficiency, set cols and/or vals to PETSC_NULL if you do 54 not wish to extract these quantities. 55 56 The user can only examine the values extracted with MatGetRow(); 57 the values cannot be altered. To change the matrix entries, one 58 must use MatSetValues(). 59 60 You can only have one call to MatGetRow() outstanding for a particular 61 matrix at a time, per processor. MatGetRow() can only obtained rows 62 associated with the given processor, it cannot get rows from the 63 other processors; for that we suggest using MatGetSubMatrices(), then 64 MatGetRow() on the submatrix. The row indix passed to MatGetRows() 65 is in the global number of rows. 66 67 Fortran Notes: 68 The calling sequence from Fortran is 69 .vb 70 MatGetRow(matrix,row,ncols,cols,values,ierr) 71 Mat matrix (input) 72 integer row (input) 73 integer ncols (output) 74 integer cols(maxcols) (output) 75 double precision (or double complex) values(maxcols) output 76 .ve 77 where maxcols >= maximum nonzeros in any row of the matrix. 78 79 80 Caution: 81 Do not try to change the contents of the output arrays (cols and vals). 82 In some cases, this may corrupt the matrix. 83 84 Level: advanced 85 86 Concepts: matrices^row access 87 88 .seealso: MatRestoreRow(), MatSetValues(), MatGetValues(), MatGetSubmatrices(), MatGetDiagonal() 89 @*/ 90 91 PetscErrorCode MatGetRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) 92 { 93 PetscErrorCode ierr; 94 PetscInt incols; 95 96 PetscFunctionBegin; 97 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 98 PetscValidType(mat,1); 99 MatPreallocated(mat); 100 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 101 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 102 if (!mat->ops->getrow) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 103 ierr = PetscLogEventBegin(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); 104 ierr = (*mat->ops->getrow)(mat,row,&incols,(PetscInt **)cols,(PetscScalar **)vals);CHKERRQ(ierr); 105 if (ncols) *ncols = incols; 106 ierr = PetscLogEventEnd(MAT_GetRow,mat,0,0,0);CHKERRQ(ierr); 107 PetscFunctionReturn(0); 108 } 109 110 #undef __FUNCT__ 111 #define __FUNCT__ "MatRestoreRow" 112 /*@C 113 MatRestoreRow - Frees any temporary space allocated by MatGetRow(). 114 115 Not Collective 116 117 Input Parameters: 118 + mat - the matrix 119 . row - the row to get 120 . ncols, cols - the number of nonzeros and their columns 121 - vals - if nonzero the column values 122 123 Notes: 124 This routine should be called after you have finished examining the entries. 125 126 Fortran Notes: 127 The calling sequence from Fortran is 128 .vb 129 MatRestoreRow(matrix,row,ncols,cols,values,ierr) 130 Mat matrix (input) 131 integer row (input) 132 integer ncols (output) 133 integer cols(maxcols) (output) 134 double precision (or double complex) values(maxcols) output 135 .ve 136 Where maxcols >= maximum nonzeros in any row of the matrix. 137 138 In Fortran MatRestoreRow() MUST be called after MatGetRow() 139 before another call to MatGetRow() can be made. 140 141 Level: advanced 142 143 .seealso: MatGetRow() 144 @*/ 145 PetscErrorCode MatRestoreRow(Mat mat,PetscInt row,PetscInt *ncols,const PetscInt *cols[],const PetscScalar *vals[]) 146 { 147 PetscErrorCode ierr; 148 149 PetscFunctionBegin; 150 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 151 PetscValidIntPointer(ncols,3); 152 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 153 if (!mat->ops->restorerow) PetscFunctionReturn(0); 154 ierr = (*mat->ops->restorerow)(mat,row,ncols,(PetscInt **)cols,(PetscScalar **)vals);CHKERRQ(ierr); 155 PetscFunctionReturn(0); 156 } 157 158 #undef __FUNCT__ 159 #define __FUNCT__ "MatView" 160 /*@C 161 MatView - Visualizes a matrix object. 162 163 Collective on Mat 164 165 Input Parameters: 166 + mat - the matrix 167 - viewer - visualization context 168 169 Notes: 170 The available visualization contexts include 171 + PETSC_VIEWER_STDOUT_SELF - standard output (default) 172 . PETSC_VIEWER_STDOUT_WORLD - synchronized standard 173 output where only the first processor opens 174 the file. All other processors send their 175 data to the first processor to print. 176 - PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure 177 178 The user can open alternative visualization contexts with 179 + PetscViewerASCIIOpen() - Outputs matrix to a specified file 180 . PetscViewerBinaryOpen() - Outputs matrix in binary to a 181 specified file; corresponding input uses MatLoad() 182 . PetscViewerDrawOpen() - Outputs nonzero matrix structure to 183 an X window display 184 - PetscViewerSocketOpen() - Outputs matrix to Socket viewer. 185 Currently only the sequential dense and AIJ 186 matrix types support the Socket viewer. 187 188 The user can call PetscViewerSetFormat() to specify the output 189 format of ASCII printed objects (when using PETSC_VIEWER_STDOUT_SELF, 190 PETSC_VIEWER_STDOUT_WORLD and PetscViewerASCIIOpen). Available formats include 191 + PETSC_VIEWER_ASCII_DEFAULT - default, prints matrix contents 192 . PETSC_VIEWER_ASCII_MATLAB - prints matrix contents in Matlab format 193 . PETSC_VIEWER_ASCII_DENSE - prints entire matrix including zeros 194 . PETSC_VIEWER_ASCII_COMMON - prints matrix contents, using a sparse 195 format common among all matrix types 196 . PETSC_VIEWER_ASCII_IMPL - prints matrix contents, using an implementation-specific 197 format (which is in many cases the same as the default) 198 . PETSC_VIEWER_ASCII_INFO - prints basic information about the matrix 199 size and structure (not the matrix entries) 200 . PETSC_VIEWER_ASCII_INFO_DETAIL - prints more detailed information about 201 the matrix structure 202 203 Options Database Keys: 204 + -mat_view_info - Prints info on matrix at conclusion of MatEndAssembly() 205 . -mat_view_info_detailed - Prints more detailed info 206 . -mat_view - Prints matrix in ASCII format 207 . -mat_view_matlab - Prints matrix in Matlab format 208 . -mat_view_draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). 209 . -display <name> - Sets display name (default is host) 210 . -draw_pause <sec> - Sets number of seconds to pause after display 211 . -mat_view_socket - Sends matrix to socket, can be accessed from Matlab (see users manual) 212 . -viewer_socket_machine <machine> 213 . -viewer_socket_port <port> 214 . -mat_view_binary - save matrix to file in binary format 215 - -viewer_binary_filename <name> 216 Level: beginner 217 218 Concepts: matrices^viewing 219 Concepts: matrices^plotting 220 Concepts: matrices^printing 221 222 .seealso: PetscViewerSetFormat(), PetscViewerASCIIOpen(), PetscViewerDrawOpen(), 223 PetscViewerSocketOpen(), PetscViewerBinaryOpen(), MatLoad() 224 @*/ 225 PetscErrorCode MatView(Mat mat,PetscViewer viewer) 226 { 227 PetscErrorCode ierr; 228 PetscInt rows,cols; 229 PetscTruth iascii; 230 char *cstr; 231 PetscViewerFormat format; 232 233 PetscFunctionBegin; 234 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 235 PetscValidType(mat,1); 236 MatPreallocated(mat); 237 if (!viewer) viewer = PETSC_VIEWER_STDOUT_(mat->comm); 238 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_COOKIE,2); 239 PetscCheckSameComm(mat,1,viewer,2); 240 if (!mat->assembled) SETERRQ(PETSC_ERR_ORDER,"Must call MatAssemblyBegin/End() before viewing matrix"); 241 242 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); 243 if (iascii) { 244 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 245 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 246 if (mat->prefix) { 247 ierr = PetscViewerASCIIPrintf(viewer,"Matrix Object:(%s)\n",mat->prefix);CHKERRQ(ierr); 248 } else { 249 ierr = PetscViewerASCIIPrintf(viewer,"Matrix Object:\n");CHKERRQ(ierr); 250 } 251 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 252 ierr = MatGetType(mat,&cstr);CHKERRQ(ierr); 253 ierr = MatGetSize(mat,&rows,&cols);CHKERRQ(ierr); 254 ierr = PetscViewerASCIIPrintf(viewer,"type=%s, rows=%D, cols=%D\n",cstr,rows,cols);CHKERRQ(ierr); 255 if (mat->ops->getinfo) { 256 MatInfo info; 257 ierr = MatGetInfo(mat,MAT_GLOBAL_SUM,&info);CHKERRQ(ierr); 258 ierr = PetscViewerASCIIPrintf(viewer,"total: nonzeros=%D, allocated nonzeros=%D\n", 259 (PetscInt)info.nz_used,(PetscInt)info.nz_allocated);CHKERRQ(ierr); 260 } 261 } 262 } 263 if (mat->ops->view) { 264 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 265 ierr = (*mat->ops->view)(mat,viewer);CHKERRQ(ierr); 266 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 267 } else if (!iascii) { 268 SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported",((PetscObject)viewer)->type_name); 269 } 270 if (iascii) { 271 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 272 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 273 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 274 } 275 } 276 PetscFunctionReturn(0); 277 } 278 279 #undef __FUNCT__ 280 #define __FUNCT__ "MatScaleSystem" 281 /*@C 282 MatScaleSystem - Scale a vector solution and right hand side to 283 match the scaling of a scaled matrix. 284 285 Collective on Mat 286 287 Input Parameter: 288 + mat - the matrix 289 . x - solution vector (or PETSC_NULL) 290 - b - right hand side vector (or PETSC_NULL) 291 292 293 Notes: 294 For AIJ, BAIJ, and BDiag matrix formats, the matrices are not 295 internally scaled, so this does nothing. For MPIROWBS it 296 permutes and diagonally scales. 297 298 The KSP methods automatically call this routine when required 299 (via PCPreSolve()) so it is rarely used directly. 300 301 Level: Developer 302 303 Concepts: matrices^scaling 304 305 .seealso: MatUseScaledForm(), MatUnScaleSystem() 306 @*/ 307 PetscErrorCode MatScaleSystem(Mat mat,Vec x,Vec b) 308 { 309 PetscErrorCode ierr; 310 311 PetscFunctionBegin; 312 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 313 PetscValidType(mat,1); 314 MatPreallocated(mat); 315 if (x) {PetscValidHeaderSpecific(x,VEC_COOKIE,2);PetscCheckSameComm(mat,1,x,2);} 316 if (b) {PetscValidHeaderSpecific(b,VEC_COOKIE,3);PetscCheckSameComm(mat,1,b,3);} 317 318 if (mat->ops->scalesystem) { 319 ierr = (*mat->ops->scalesystem)(mat,x,b);CHKERRQ(ierr); 320 } 321 PetscFunctionReturn(0); 322 } 323 324 #undef __FUNCT__ 325 #define __FUNCT__ "MatUnScaleSystem" 326 /*@C 327 MatUnScaleSystem - Unscales a vector solution and right hand side to 328 match the original scaling of a scaled matrix. 329 330 Collective on Mat 331 332 Input Parameter: 333 + mat - the matrix 334 . x - solution vector (or PETSC_NULL) 335 - b - right hand side vector (or PETSC_NULL) 336 337 338 Notes: 339 For AIJ, BAIJ, and BDiag matrix formats, the matrices are not 340 internally scaled, so this does nothing. For MPIROWBS it 341 permutes and diagonally scales. 342 343 The KSP methods automatically call this routine when required 344 (via PCPreSolve()) so it is rarely used directly. 345 346 Level: Developer 347 348 .seealso: MatUseScaledForm(), MatScaleSystem() 349 @*/ 350 PetscErrorCode MatUnScaleSystem(Mat mat,Vec x,Vec b) 351 { 352 PetscErrorCode ierr; 353 354 PetscFunctionBegin; 355 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 356 PetscValidType(mat,1); 357 MatPreallocated(mat); 358 if (x) {PetscValidHeaderSpecific(x,VEC_COOKIE,2);PetscCheckSameComm(mat,1,x,2);} 359 if (b) {PetscValidHeaderSpecific(b,VEC_COOKIE,3);PetscCheckSameComm(mat,1,b,3);} 360 if (mat->ops->unscalesystem) { 361 ierr = (*mat->ops->unscalesystem)(mat,x,b);CHKERRQ(ierr); 362 } 363 PetscFunctionReturn(0); 364 } 365 366 #undef __FUNCT__ 367 #define __FUNCT__ "MatUseScaledForm" 368 /*@C 369 MatUseScaledForm - For matrix storage formats that scale the 370 matrix (for example MPIRowBS matrices are diagonally scaled on 371 assembly) indicates matrix operations (MatMult() etc) are 372 applied using the scaled matrix. 373 374 Collective on Mat 375 376 Input Parameter: 377 + mat - the matrix 378 - scaled - PETSC_TRUE for applying the scaled, PETSC_FALSE for 379 applying the original matrix 380 381 Notes: 382 For scaled matrix formats, applying the original, unscaled matrix 383 will be slightly more expensive 384 385 Level: Developer 386 387 .seealso: MatScaleSystem(), MatUnScaleSystem() 388 @*/ 389 PetscErrorCode MatUseScaledForm(Mat mat,PetscTruth scaled) 390 { 391 PetscErrorCode ierr; 392 393 PetscFunctionBegin; 394 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 395 PetscValidType(mat,1); 396 MatPreallocated(mat); 397 if (mat->ops->usescaledform) { 398 ierr = (*mat->ops->usescaledform)(mat,scaled);CHKERRQ(ierr); 399 } 400 PetscFunctionReturn(0); 401 } 402 403 #undef __FUNCT__ 404 #define __FUNCT__ "MatDestroy" 405 /*@C 406 MatDestroy - Frees space taken by a matrix. 407 408 Collective on Mat 409 410 Input Parameter: 411 . A - the matrix 412 413 Level: beginner 414 415 @*/ 416 PetscErrorCode MatDestroy(Mat A) 417 { 418 PetscErrorCode ierr; 419 420 PetscFunctionBegin; 421 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 422 PetscValidType(A,1); 423 MatPreallocated(A); 424 if (--A->refct > 0) PetscFunctionReturn(0); 425 426 /* if memory was published with AMS then destroy it */ 427 ierr = PetscObjectDepublish(A);CHKERRQ(ierr); 428 if (A->mapping) { 429 ierr = ISLocalToGlobalMappingDestroy(A->mapping);CHKERRQ(ierr); 430 } 431 if (A->bmapping) { 432 ierr = ISLocalToGlobalMappingDestroy(A->bmapping);CHKERRQ(ierr); 433 } 434 if (A->rmap) { 435 ierr = PetscMapDestroy(A->rmap);CHKERRQ(ierr); 436 } 437 if (A->cmap) { 438 ierr = PetscMapDestroy(A->cmap);CHKERRQ(ierr); 439 } 440 441 ierr = (*A->ops->destroy)(A);CHKERRQ(ierr); 442 PetscLogObjectDestroy(A); 443 PetscHeaderDestroy(A); 444 PetscFunctionReturn(0); 445 } 446 447 #undef __FUNCT__ 448 #define __FUNCT__ "MatValid" 449 /*@ 450 MatValid - Checks whether a matrix object is valid. 451 452 Collective on Mat 453 454 Input Parameter: 455 . m - the matrix to check 456 457 Output Parameter: 458 flg - flag indicating matrix status, either 459 PETSC_TRUE if matrix is valid, or PETSC_FALSE otherwise. 460 461 Level: developer 462 463 Concepts: matrices^validity 464 @*/ 465 PetscErrorCode MatValid(Mat m,PetscTruth *flg) 466 { 467 PetscFunctionBegin; 468 PetscValidIntPointer(flg,1); 469 if (!m) *flg = PETSC_FALSE; 470 else if (m->cookie != MAT_COOKIE) *flg = PETSC_FALSE; 471 else *flg = PETSC_TRUE; 472 PetscFunctionReturn(0); 473 } 474 475 #undef __FUNCT__ 476 #define __FUNCT__ "MatSetValues" 477 /*@ 478 MatSetValues - Inserts or adds a block of values into a matrix. 479 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 480 MUST be called after all calls to MatSetValues() have been completed. 481 482 Not Collective 483 484 Input Parameters: 485 + mat - the matrix 486 . v - a logically two-dimensional array of values 487 . m, idxm - the number of rows and their global indices 488 . n, idxn - the number of columns and their global indices 489 - addv - either ADD_VALUES or INSERT_VALUES, where 490 ADD_VALUES adds values to any existing entries, and 491 INSERT_VALUES replaces existing entries with new values 492 493 Notes: 494 By default the values, v, are row-oriented and unsorted. 495 See MatSetOption() for other options. 496 497 Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES 498 options cannot be mixed without intervening calls to the assembly 499 routines. 500 501 MatSetValues() uses 0-based row and column numbers in Fortran 502 as well as in C. 503 504 Negative indices may be passed in idxm and idxn, these rows and columns are 505 simply ignored. This allows easily inserting element stiffness matrices 506 with homogeneous Dirchlet boundary conditions that you don't want represented 507 in the matrix. 508 509 Efficiency Alert: 510 The routine MatSetValuesBlocked() may offer much better efficiency 511 for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). 512 513 Level: beginner 514 515 Concepts: matrices^putting entries in 516 517 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 518 InsertMode, INSERT_VALUES, ADD_VALUES 519 @*/ 520 PetscErrorCode MatSetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 521 { 522 PetscErrorCode ierr; 523 524 PetscFunctionBegin; 525 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 526 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 527 PetscValidType(mat,1); 528 MatPreallocated(mat); 529 PetscValidIntPointer(idxm,3); 530 PetscValidIntPointer(idxn,5); 531 PetscValidScalarPointer(v,6); 532 if (mat->insertmode == NOT_SET_VALUES) { 533 mat->insertmode = addv; 534 } 535 #if defined(PETSC_USE_BOPT_g) 536 else if (mat->insertmode != addv) { 537 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 538 } 539 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 540 #endif 541 542 if (mat->assembled) { 543 mat->was_assembled = PETSC_TRUE; 544 mat->assembled = PETSC_FALSE; 545 } 546 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 547 if (!mat->ops->setvalues) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 548 ierr = (*mat->ops->setvalues)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); 549 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 550 PetscFunctionReturn(0); 551 } 552 553 #undef __FUNCT__ 554 #define __FUNCT__ "MatSetValuesStencil" 555 /*@C 556 MatSetValuesStencil - Inserts or adds a block of values into a matrix. 557 Using structured grid indexing 558 559 Not Collective 560 561 Input Parameters: 562 + mat - the matrix 563 . v - a logically two-dimensional array of values 564 . m - number of rows being entered 565 . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered 566 . n - number of columns being entered 567 . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered 568 - addv - either ADD_VALUES or INSERT_VALUES, where 569 ADD_VALUES adds values to any existing entries, and 570 INSERT_VALUES replaces existing entries with new values 571 572 Notes: 573 By default the values, v, are row-oriented and unsorted. 574 See MatSetOption() for other options. 575 576 Calls to MatSetValuesStencil() with the INSERT_VALUES and ADD_VALUES 577 options cannot be mixed without intervening calls to the assembly 578 routines. 579 580 The grid coordinates are across the entire grid, not just the local portion 581 582 MatSetValuesStencil() uses 0-based row and column numbers in Fortran 583 as well as in C. 584 585 For setting/accessing vector values via array coordinates you can use the DAVecGetArray() routine 586 587 In order to use this routine you must either obtain the matrix with DAGetMatrix() 588 or call MatSetLocalToGlobalMapping() and MatSetStencil() first. 589 590 The columns and rows in the stencil passed in MUST be contained within the 591 ghost region of the given process as set with DACreateXXX() or MatSetStencil(). For example, 592 if you create a DA with an overlap of one grid level and on a particular process its first 593 local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the 594 first i index you can use in your column and row indices in MatSetStencil() is 5. 595 596 In Fortran idxm and idxn should be declared as 597 $ MatStencil idxm(4,m),idxn(4,n) 598 and the values inserted using 599 $ idxm(MatStencil_i,1) = i 600 $ idxm(MatStencil_j,1) = j 601 $ idxm(MatStencil_k,1) = k 602 $ idxm(MatStencil_c,1) = c 603 etc 604 605 Negative indices may be passed in idxm and idxn, these rows and columns are 606 simply ignored. This allows easily inserting element stiffness matrices 607 with homogeneous Dirchlet boundary conditions that you don't want represented 608 in the matrix. 609 610 Inspired by the structured grid interface to the HYPRE package 611 (http://www.llnl.gov/CASC/hypre) 612 613 Efficiency Alert: 614 The routine MatSetValuesBlockedStencil() may offer much better efficiency 615 for users of block sparse formats (MATSEQBAIJ and MATMPIBAIJ). 616 617 Level: beginner 618 619 Concepts: matrices^putting entries in 620 621 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 622 MatSetValues(), MatSetValuesBlockedStencil(), MatSetStencil(), DAGetMatrix(), DAVecGetArray(), MatStencil 623 @*/ 624 PetscErrorCode MatSetValuesStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) 625 { 626 PetscErrorCode ierr; 627 PetscInt j,i,jdxm[128],jdxn[256],dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; 628 PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); 629 630 PetscFunctionBegin; 631 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 632 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 633 PetscValidType(mat,1); 634 PetscValidIntPointer(idxm,3); 635 PetscValidIntPointer(idxn,5); 636 PetscValidScalarPointer(v,6); 637 638 if (m > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 128 rows at a time; trying to set %D",m); 639 if (n > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 256 columns at a time; trying to set %D",n); 640 641 for (i=0; i<m; i++) { 642 for (j=0; j<3-sdim; j++) dxm++; 643 if (*dxm++ < 0) tmp = PETSC_MIN_INT; 644 else tmp = dxm[-1] - starts[0]; 645 for (j=0; j<dim-1; j++) { 646 if (*dxm++ < 0 || tmp < 0) tmp = PETSC_MIN_INT; 647 else tmp = tmp*dims[j] + dxm[-1] - starts[j+1]; 648 } 649 if (mat->stencil.noc) dxm++; 650 jdxm[i] = tmp; 651 } 652 for (i=0; i<n; i++) { 653 for (j=0; j<3-sdim; j++) dxn++; 654 if (*dxn++ < 0) tmp = PETSC_MIN_INT; 655 else tmp = dxn[-1] - starts[0]; 656 for (j=0; j<dim-1; j++) { 657 if (*dxn++ < 0 || tmp < 0) tmp = PETSC_MIN_INT; 658 else tmp = tmp*dims[j] + dxn[-1] - starts[j+1]; 659 } 660 if (mat->stencil.noc) dxn++; 661 jdxn[i] = tmp; 662 } 663 ierr = MatSetValuesLocal(mat,m,jdxm,n,jdxn,v,addv);CHKERRQ(ierr); 664 PetscFunctionReturn(0); 665 } 666 667 #undef __FUNCT__ 668 #define __FUNCT__ "MatSetValuesBlockedStencil" 669 /*@C 670 MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix. 671 Using structured grid indexing 672 673 Not Collective 674 675 Input Parameters: 676 + mat - the matrix 677 . v - a logically two-dimensional array of values 678 . m - number of rows being entered 679 . idxm - grid coordinates for matrix rows being entered 680 . n - number of columns being entered 681 . idxn - grid coordinates for matrix columns being entered 682 - addv - either ADD_VALUES or INSERT_VALUES, where 683 ADD_VALUES adds values to any existing entries, and 684 INSERT_VALUES replaces existing entries with new values 685 686 Notes: 687 By default the values, v, are row-oriented and unsorted. 688 See MatSetOption() for other options. 689 690 Calls to MatSetValuesBlockedStencil() with the INSERT_VALUES and ADD_VALUES 691 options cannot be mixed without intervening calls to the assembly 692 routines. 693 694 The grid coordinates are across the entire grid, not just the local portion 695 696 MatSetValuesBlockedStencil() uses 0-based row and column numbers in Fortran 697 as well as in C. 698 699 For setting/accessing vector values via array coordinates you can use the DAVecGetArray() routine 700 701 In order to use this routine you must either obtain the matrix with DAGetMatrix() 702 or call MatSetLocalToGlobalMapping() and MatSetStencil() first. 703 704 The columns and rows in the stencil passed in MUST be contained within the 705 ghost region of the given process as set with DACreateXXX() or MatSetStencil(). For example, 706 if you create a DA with an overlap of one grid level and on a particular process its first 707 local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the 708 first i index you can use in your column and row indices in MatSetStencil() is 5. 709 710 In Fortran idxm and idxn should be declared as 711 $ MatStencil idxm(4,m),idxn(4,n) 712 and the values inserted using 713 $ idxm(MatStencil_i,1) = i 714 $ idxm(MatStencil_j,1) = j 715 $ idxm(MatStencil_k,1) = k 716 etc 717 718 Negative indices may be passed in idxm and idxn, these rows and columns are 719 simply ignored. This allows easily inserting element stiffness matrices 720 with homogeneous Dirchlet boundary conditions that you don't want represented 721 in the matrix. 722 723 Inspired by the structured grid interface to the HYPRE package 724 (http://www.llnl.gov/CASC/hypre) 725 726 Level: beginner 727 728 Concepts: matrices^putting entries in 729 730 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 731 MatSetValues(), MatSetValuesStencil(), MatSetStencil(), DAGetMatrix(), DAVecGetArray(), MatStencil 732 @*/ 733 PetscErrorCode MatSetValuesBlockedStencil(Mat mat,PetscInt m,const MatStencil idxm[],PetscInt n,const MatStencil idxn[],const PetscScalar v[],InsertMode addv) 734 { 735 PetscErrorCode ierr; 736 PetscInt j,i,jdxm[128],jdxn[256],dim = mat->stencil.dim,*dims = mat->stencil.dims+1,tmp; 737 PetscInt *starts = mat->stencil.starts,*dxm = (PetscInt*)idxm,*dxn = (PetscInt*)idxn,sdim = dim - (1 - (PetscInt)mat->stencil.noc); 738 739 PetscFunctionBegin; 740 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 741 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 742 PetscValidType(mat,1); 743 PetscValidIntPointer(idxm,3); 744 PetscValidIntPointer(idxn,5); 745 PetscValidScalarPointer(v,6); 746 747 if (m > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 128 rows at a time; trying to set %D",m); 748 if (n > 128) SETERRQ1(PETSC_ERR_SUP,"Can only set 256 columns at a time; trying to set %D",n); 749 750 for (i=0; i<m; i++) { 751 for (j=0; j<3-sdim; j++) dxm++; 752 tmp = *dxm++ - starts[0]; 753 for (j=0; j<sdim-1; j++) { 754 tmp = tmp*dims[j] + *dxm++ - starts[j+1]; 755 } 756 dxm++; 757 jdxm[i] = tmp; 758 } 759 for (i=0; i<n; i++) { 760 for (j=0; j<3-sdim; j++) dxn++; 761 tmp = *dxn++ - starts[0]; 762 for (j=0; j<sdim-1; j++) { 763 tmp = tmp*dims[j] + *dxn++ - starts[j+1]; 764 } 765 dxn++; 766 jdxn[i] = tmp; 767 } 768 ierr = MatSetValuesBlockedLocal(mat,m,jdxm,n,jdxn,v,addv);CHKERRQ(ierr); 769 PetscFunctionReturn(0); 770 } 771 772 #undef __FUNCT__ 773 #define __FUNCT__ "MatSetStencil" 774 /*@ 775 MatSetStencil - Sets the grid information for setting values into a matrix via 776 MatSetValuesStencil() 777 778 Not Collective 779 780 Input Parameters: 781 + mat - the matrix 782 . dim - dimension of the grid 1, 2, or 3 783 . dims - number of grid points in x, y, and z direction, including ghost points on your processor 784 . starts - starting point of ghost nodes on your processor in x, y, and z direction 785 - dof - number of degrees of freedom per node 786 787 788 Inspired by the structured grid interface to the HYPRE package 789 (www.llnl.gov/CASC/hyper) 790 791 For matrices generated with DAGetMatrix() this routine is automatically called and so not needed by the 792 user. 793 794 Level: beginner 795 796 Concepts: matrices^putting entries in 797 798 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal() 799 MatSetValues(), MatSetValuesBlockedStencil(), MatSetValuesStencil() 800 @*/ 801 PetscErrorCode MatSetStencil(Mat mat,PetscInt dim,const PetscInt dims[],const PetscInt starts[],PetscInt dof) 802 { 803 PetscInt i; 804 805 PetscFunctionBegin; 806 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 807 PetscValidIntPointer(dims,3); 808 PetscValidIntPointer(starts,4); 809 810 mat->stencil.dim = dim + (dof > 1); 811 for (i=0; i<dim; i++) { 812 mat->stencil.dims[i] = dims[dim-i-1]; /* copy the values in backwards */ 813 mat->stencil.starts[i] = starts[dim-i-1]; 814 } 815 mat->stencil.dims[dim] = dof; 816 mat->stencil.starts[dim] = 0; 817 mat->stencil.noc = (PetscTruth)(dof == 1); 818 PetscFunctionReturn(0); 819 } 820 821 #undef __FUNCT__ 822 #define __FUNCT__ "MatSetValuesBlocked" 823 /*@ 824 MatSetValuesBlocked - Inserts or adds a block of values into a matrix. 825 826 Not Collective 827 828 Input Parameters: 829 + mat - the matrix 830 . v - a logically two-dimensional array of values 831 . m, idxm - the number of block rows and their global block indices 832 . n, idxn - the number of block columns and their global block indices 833 - addv - either ADD_VALUES or INSERT_VALUES, where 834 ADD_VALUES adds values to any existing entries, and 835 INSERT_VALUES replaces existing entries with new values 836 837 Notes: 838 By default the values, v, are row-oriented and unsorted. So the layout of 839 v is the same as for MatSetValues(). See MatSetOption() for other options. 840 841 Calls to MatSetValuesBlocked() with the INSERT_VALUES and ADD_VALUES 842 options cannot be mixed without intervening calls to the assembly 843 routines. 844 845 MatSetValuesBlocked() uses 0-based row and column numbers in Fortran 846 as well as in C. 847 848 Negative indices may be passed in idxm and idxn, these rows and columns are 849 simply ignored. This allows easily inserting element stiffness matrices 850 with homogeneous Dirchlet boundary conditions that you don't want represented 851 in the matrix. 852 853 Each time an entry is set within a sparse matrix via MatSetValues(), 854 internal searching must be done to determine where to place the the 855 data in the matrix storage space. By instead inserting blocks of 856 entries via MatSetValuesBlocked(), the overhead of matrix assembly is 857 reduced. 858 859 Restrictions: 860 MatSetValuesBlocked() is currently supported only for the BAIJ and SBAIJ formats 861 862 Level: intermediate 863 864 Concepts: matrices^putting entries in blocked 865 866 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal() 867 @*/ 868 PetscErrorCode MatSetValuesBlocked(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],const PetscScalar v[],InsertMode addv) 869 { 870 PetscErrorCode ierr; 871 872 PetscFunctionBegin; 873 if (!m || !n) PetscFunctionReturn(0); /* no values to insert */ 874 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 875 PetscValidType(mat,1); 876 MatPreallocated(mat); 877 PetscValidIntPointer(idxm,3); 878 PetscValidIntPointer(idxn,5); 879 PetscValidScalarPointer(v,6); 880 if (mat->insertmode == NOT_SET_VALUES) { 881 mat->insertmode = addv; 882 } 883 #if defined(PETSC_USE_BOPT_g) 884 else if (mat->insertmode != addv) { 885 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 886 } 887 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 888 #endif 889 890 if (mat->assembled) { 891 mat->was_assembled = PETSC_TRUE; 892 mat->assembled = PETSC_FALSE; 893 } 894 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 895 if (!mat->ops->setvaluesblocked) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 896 ierr = (*mat->ops->setvaluesblocked)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr); 897 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 898 PetscFunctionReturn(0); 899 } 900 901 #undef __FUNCT__ 902 #define __FUNCT__ "MatGetValues" 903 /*@ 904 MatGetValues - Gets a block of values from a matrix. 905 906 Not Collective; currently only returns a local block 907 908 Input Parameters: 909 + mat - the matrix 910 . v - a logically two-dimensional array for storing the values 911 . m, idxm - the number of rows and their global indices 912 - n, idxn - the number of columns and their global indices 913 914 Notes: 915 The user must allocate space (m*n PetscScalars) for the values, v. 916 The values, v, are then returned in a row-oriented format, 917 analogous to that used by default in MatSetValues(). 918 919 MatGetValues() uses 0-based row and column numbers in 920 Fortran as well as in C. 921 922 MatGetValues() requires that the matrix has been assembled 923 with MatAssemblyBegin()/MatAssemblyEnd(). Thus, calls to 924 MatSetValues() and MatGetValues() CANNOT be made in succession 925 without intermediate matrix assembly. 926 927 Level: advanced 928 929 Concepts: matrices^accessing values 930 931 .seealso: MatGetRow(), MatGetSubmatrices(), MatSetValues() 932 @*/ 933 PetscErrorCode MatGetValues(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[]) 934 { 935 PetscErrorCode ierr; 936 937 PetscFunctionBegin; 938 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 939 PetscValidType(mat,1); 940 MatPreallocated(mat); 941 PetscValidIntPointer(idxm,3); 942 PetscValidIntPointer(idxn,5); 943 PetscValidScalarPointer(v,6); 944 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 945 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 946 if (!mat->ops->getvalues) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 947 948 ierr = PetscLogEventBegin(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); 949 ierr = (*mat->ops->getvalues)(mat,m,idxm,n,idxn,v);CHKERRQ(ierr); 950 ierr = PetscLogEventEnd(MAT_GetValues,mat,0,0,0);CHKERRQ(ierr); 951 PetscFunctionReturn(0); 952 } 953 954 #undef __FUNCT__ 955 #define __FUNCT__ "MatSetLocalToGlobalMapping" 956 /*@ 957 MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by 958 the routine MatSetValuesLocal() to allow users to insert matrix entries 959 using a local (per-processor) numbering. 960 961 Not Collective 962 963 Input Parameters: 964 + x - the matrix 965 - mapping - mapping created with ISLocalToGlobalMappingCreate() 966 or ISLocalToGlobalMappingCreateIS() 967 968 Level: intermediate 969 970 Concepts: matrices^local to global mapping 971 Concepts: local to global mapping^for matrices 972 973 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesLocal() 974 @*/ 975 PetscErrorCode MatSetLocalToGlobalMapping(Mat x,ISLocalToGlobalMapping mapping) 976 { 977 PetscErrorCode ierr; 978 PetscFunctionBegin; 979 PetscValidHeaderSpecific(x,MAT_COOKIE,1); 980 PetscValidType(x,1); 981 MatPreallocated(x); 982 PetscValidHeaderSpecific(mapping,IS_LTOGM_COOKIE,2); 983 if (x->mapping) { 984 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Mapping already set for matrix"); 985 } 986 987 if (x->ops->setlocaltoglobalmapping) { 988 ierr = (*x->ops->setlocaltoglobalmapping)(x,mapping);CHKERRQ(ierr); 989 } else { 990 x->mapping = mapping; 991 ierr = PetscObjectReference((PetscObject)mapping);CHKERRQ(ierr); 992 } 993 PetscFunctionReturn(0); 994 } 995 996 #undef __FUNCT__ 997 #define __FUNCT__ "MatSetLocalToGlobalMappingBlock" 998 /*@ 999 MatSetLocalToGlobalMappingBlock - Sets a local-to-global numbering for use 1000 by the routine MatSetValuesBlockedLocal() to allow users to insert matrix 1001 entries using a local (per-processor) numbering. 1002 1003 Not Collective 1004 1005 Input Parameters: 1006 + x - the matrix 1007 - mapping - mapping created with ISLocalToGlobalMappingCreate() or 1008 ISLocalToGlobalMappingCreateIS() 1009 1010 Level: intermediate 1011 1012 Concepts: matrices^local to global mapping blocked 1013 Concepts: local to global mapping^for matrices, blocked 1014 1015 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetValuesBlockedLocal(), 1016 MatSetValuesBlocked(), MatSetValuesLocal() 1017 @*/ 1018 PetscErrorCode MatSetLocalToGlobalMappingBlock(Mat x,ISLocalToGlobalMapping mapping) 1019 { 1020 PetscErrorCode ierr; 1021 PetscFunctionBegin; 1022 PetscValidHeaderSpecific(x,MAT_COOKIE,1); 1023 PetscValidType(x,1); 1024 MatPreallocated(x); 1025 PetscValidHeaderSpecific(mapping,IS_LTOGM_COOKIE,2); 1026 if (x->bmapping) { 1027 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Mapping already set for matrix"); 1028 } 1029 1030 x->bmapping = mapping; 1031 ierr = PetscObjectReference((PetscObject)mapping);CHKERRQ(ierr); 1032 PetscFunctionReturn(0); 1033 } 1034 1035 #undef __FUNCT__ 1036 #define __FUNCT__ "MatSetValuesLocal" 1037 /*@ 1038 MatSetValuesLocal - Inserts or adds values into certain locations of a matrix, 1039 using a local ordering of the nodes. 1040 1041 Not Collective 1042 1043 Input Parameters: 1044 + x - the matrix 1045 . nrow, irow - number of rows and their local indices 1046 . ncol, icol - number of columns and their local indices 1047 . y - a logically two-dimensional array of values 1048 - addv - either INSERT_VALUES or ADD_VALUES, where 1049 ADD_VALUES adds values to any existing entries, and 1050 INSERT_VALUES replaces existing entries with new values 1051 1052 Notes: 1053 Before calling MatSetValuesLocal(), the user must first set the 1054 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 1055 1056 Calls to MatSetValuesLocal() with the INSERT_VALUES and ADD_VALUES 1057 options cannot be mixed without intervening calls to the assembly 1058 routines. 1059 1060 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 1061 MUST be called after all calls to MatSetValuesLocal() have been completed. 1062 1063 Level: intermediate 1064 1065 Concepts: matrices^putting entries in with local numbering 1066 1067 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValues(), MatSetLocalToGlobalMapping(), 1068 MatSetValueLocal() 1069 @*/ 1070 PetscErrorCode MatSetValuesLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) 1071 { 1072 PetscErrorCode ierr; 1073 PetscInt irowm[2048],icolm[2048]; 1074 1075 PetscFunctionBegin; 1076 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1077 PetscValidType(mat,1); 1078 MatPreallocated(mat); 1079 PetscValidIntPointer(irow,3); 1080 PetscValidIntPointer(icol,5); 1081 PetscValidScalarPointer(y,6); 1082 1083 if (mat->insertmode == NOT_SET_VALUES) { 1084 mat->insertmode = addv; 1085 } 1086 #if defined(PETSC_USE_BOPT_g) 1087 else if (mat->insertmode != addv) { 1088 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 1089 } 1090 if (!mat->ops->setvalueslocal && (nrow > 2048 || ncol > 2048)) { 1091 SETERRQ2(PETSC_ERR_SUP,"Number column/row indices must be <= 2048: are %D %D",nrow,ncol); 1092 } 1093 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1094 #endif 1095 1096 if (mat->assembled) { 1097 mat->was_assembled = PETSC_TRUE; 1098 mat->assembled = PETSC_FALSE; 1099 } 1100 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1101 if (!mat->ops->setvalueslocal) { 1102 ierr = ISLocalToGlobalMappingApply(mat->mapping,nrow,irow,irowm);CHKERRQ(ierr); 1103 ierr = ISLocalToGlobalMappingApply(mat->mapping,ncol,icol,icolm);CHKERRQ(ierr); 1104 ierr = (*mat->ops->setvalues)(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); 1105 } else { 1106 ierr = (*mat->ops->setvalueslocal)(mat,nrow,irow,ncol,icol,y,addv);CHKERRQ(ierr); 1107 } 1108 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1109 PetscFunctionReturn(0); 1110 } 1111 1112 #undef __FUNCT__ 1113 #define __FUNCT__ "MatSetValuesBlockedLocal" 1114 /*@ 1115 MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix, 1116 using a local ordering of the nodes a block at a time. 1117 1118 Not Collective 1119 1120 Input Parameters: 1121 + x - the matrix 1122 . nrow, irow - number of rows and their local indices 1123 . ncol, icol - number of columns and their local indices 1124 . y - a logically two-dimensional array of values 1125 - addv - either INSERT_VALUES or ADD_VALUES, where 1126 ADD_VALUES adds values to any existing entries, and 1127 INSERT_VALUES replaces existing entries with new values 1128 1129 Notes: 1130 Before calling MatSetValuesBlockedLocal(), the user must first set the 1131 local-to-global mapping by calling MatSetLocalToGlobalMappingBlock(), 1132 where the mapping MUST be set for matrix blocks, not for matrix elements. 1133 1134 Calls to MatSetValuesBlockedLocal() with the INSERT_VALUES and ADD_VALUES 1135 options cannot be mixed without intervening calls to the assembly 1136 routines. 1137 1138 These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd() 1139 MUST be called after all calls to MatSetValuesBlockedLocal() have been completed. 1140 1141 Level: intermediate 1142 1143 Concepts: matrices^putting blocked values in with local numbering 1144 1145 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesLocal(), MatSetLocalToGlobalMappingBlock(), MatSetValuesBlocked() 1146 @*/ 1147 PetscErrorCode MatSetValuesBlockedLocal(Mat mat,PetscInt nrow,const PetscInt irow[],PetscInt ncol,const PetscInt icol[],const PetscScalar y[],InsertMode addv) 1148 { 1149 PetscErrorCode ierr; 1150 PetscInt irowm[2048],icolm[2048]; 1151 1152 PetscFunctionBegin; 1153 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1154 PetscValidType(mat,1); 1155 MatPreallocated(mat); 1156 PetscValidIntPointer(irow,3); 1157 PetscValidIntPointer(icol,5); 1158 PetscValidScalarPointer(y,6); 1159 if (mat->insertmode == NOT_SET_VALUES) { 1160 mat->insertmode = addv; 1161 } 1162 #if defined(PETSC_USE_BOPT_g) 1163 else if (mat->insertmode != addv) { 1164 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values"); 1165 } 1166 if (!mat->bmapping) { 1167 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Local to global never set with MatSetLocalToGlobalMappingBlock()"); 1168 } 1169 if (nrow > 2048 || ncol > 2048) { 1170 SETERRQ2(PETSC_ERR_SUP,"Number column/row indices must be <= 2048: are %D %D",nrow,ncol); 1171 } 1172 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1173 #endif 1174 1175 if (mat->assembled) { 1176 mat->was_assembled = PETSC_TRUE; 1177 mat->assembled = PETSC_FALSE; 1178 } 1179 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1180 ierr = ISLocalToGlobalMappingApply(mat->bmapping,nrow,irow,irowm);CHKERRQ(ierr); 1181 ierr = ISLocalToGlobalMappingApply(mat->bmapping,ncol,icol,icolm);CHKERRQ(ierr); 1182 ierr = (*mat->ops->setvaluesblocked)(mat,nrow,irowm,ncol,icolm,y,addv);CHKERRQ(ierr); 1183 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 1184 PetscFunctionReturn(0); 1185 } 1186 1187 /* --------------------------------------------------------*/ 1188 #undef __FUNCT__ 1189 #define __FUNCT__ "MatMult" 1190 /*@ 1191 MatMult - Computes the matrix-vector product, y = Ax. 1192 1193 Collective on Mat and Vec 1194 1195 Input Parameters: 1196 + mat - the matrix 1197 - x - the vector to be multiplied 1198 1199 Output Parameters: 1200 . y - the result 1201 1202 Notes: 1203 The vectors x and y cannot be the same. I.e., one cannot 1204 call MatMult(A,y,y). 1205 1206 Level: beginner 1207 1208 Concepts: matrix-vector product 1209 1210 .seealso: MatMultTranspose(), MatMultAdd(), MatMultTransposeAdd() 1211 @*/ 1212 PetscErrorCode MatMult(Mat mat,Vec x,Vec y) 1213 { 1214 PetscErrorCode ierr; 1215 1216 PetscFunctionBegin; 1217 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1218 PetscValidType(mat,1); 1219 MatPreallocated(mat); 1220 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 1221 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 1222 1223 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1224 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1225 if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 1226 #ifndef PETSC_HAVE_CONSTRAINTS 1227 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 1228 if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); 1229 if (mat->m != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->m,y->n); 1230 #endif 1231 1232 if (mat->nullsp) { 1233 ierr = MatNullSpaceRemove(mat->nullsp,x,&x);CHKERRQ(ierr); 1234 } 1235 1236 ierr = PetscLogEventBegin(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); 1237 ierr = (*mat->ops->mult)(mat,x,y);CHKERRQ(ierr); 1238 ierr = PetscLogEventEnd(MAT_Mult,mat,x,y,0);CHKERRQ(ierr); 1239 1240 if (mat->nullsp) { 1241 ierr = MatNullSpaceRemove(mat->nullsp,y,PETSC_NULL);CHKERRQ(ierr); 1242 } 1243 ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); 1244 PetscFunctionReturn(0); 1245 } 1246 1247 #undef __FUNCT__ 1248 #define __FUNCT__ "MatMultTranspose" 1249 /*@ 1250 MatMultTranspose - Computes matrix transpose times a vector. 1251 1252 Collective on Mat and Vec 1253 1254 Input Parameters: 1255 + mat - the matrix 1256 - x - the vector to be multilplied 1257 1258 Output Parameters: 1259 . y - the result 1260 1261 Notes: 1262 The vectors x and y cannot be the same. I.e., one cannot 1263 call MatMultTranspose(A,y,y). 1264 1265 Level: beginner 1266 1267 Concepts: matrix vector product^transpose 1268 1269 .seealso: MatMult(), MatMultAdd(), MatMultTransposeAdd() 1270 @*/ 1271 PetscErrorCode MatMultTranspose(Mat mat,Vec x,Vec y) 1272 { 1273 PetscErrorCode ierr; 1274 PetscTruth flg1, flg2; 1275 1276 PetscFunctionBegin; 1277 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1278 PetscValidType(mat,1); 1279 MatPreallocated(mat); 1280 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 1281 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 1282 1283 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1284 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1285 if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 1286 #ifndef PETSC_HAVE_CONSTRAINTS 1287 if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); 1288 if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->N,y->N); 1289 #endif 1290 1291 if (!mat->ops->multtranspose) SETERRQ(PETSC_ERR_SUP, "Operation not supported"); 1292 ierr = PetscLogEventBegin(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); 1293 if (!mat->ops->multtranspose) SETERRQ(PETSC_ERR_SUP,"This matrix type does not have a multiply tranpose defined"); 1294 1295 ierr = PetscTypeCompare((PetscObject)mat,MATSEQSBAIJ,&flg1); 1296 ierr = PetscTypeCompare((PetscObject)mat,MATMPISBAIJ,&flg2); 1297 if (flg1 || flg2) { /* mat is in sbaij format */ 1298 ierr = (*mat->ops->mult)(mat,x,y);CHKERRQ(ierr); 1299 } else { 1300 ierr = (*mat->ops->multtranspose)(mat,x,y);CHKERRQ(ierr); 1301 } 1302 ierr = PetscLogEventEnd(MAT_MultTranspose,mat,x,y,0);CHKERRQ(ierr); 1303 ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); 1304 PetscFunctionReturn(0); 1305 } 1306 1307 #undef __FUNCT__ 1308 #define __FUNCT__ "MatMultAdd" 1309 /*@ 1310 MatMultAdd - Computes v3 = v2 + A * v1. 1311 1312 Collective on Mat and Vec 1313 1314 Input Parameters: 1315 + mat - the matrix 1316 - v1, v2 - the vectors 1317 1318 Output Parameters: 1319 . v3 - the result 1320 1321 Notes: 1322 The vectors v1 and v3 cannot be the same. I.e., one cannot 1323 call MatMultAdd(A,v1,v2,v1). 1324 1325 Level: beginner 1326 1327 Concepts: matrix vector product^addition 1328 1329 .seealso: MatMultTranspose(), MatMult(), MatMultTransposeAdd() 1330 @*/ 1331 PetscErrorCode MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3) 1332 { 1333 PetscErrorCode ierr; 1334 1335 PetscFunctionBegin; 1336 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1337 PetscValidType(mat,1); 1338 MatPreallocated(mat); 1339 PetscValidHeaderSpecific(v1,VEC_COOKIE,2); 1340 PetscValidHeaderSpecific(v2,VEC_COOKIE,3); 1341 PetscValidHeaderSpecific(v3,VEC_COOKIE,4); 1342 1343 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1344 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1345 if (mat->N != v1->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->N,v1->N); 1346 if (mat->M != v2->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->M,v2->N); 1347 if (mat->M != v3->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->M,v3->N); 1348 if (mat->m != v3->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: local dim %D %D",mat->m,v3->n); 1349 if (mat->m != v2->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: local dim %D %D",mat->m,v2->n); 1350 if (v1 == v3) SETERRQ(PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 1351 1352 ierr = PetscLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); 1353 ierr = (*mat->ops->multadd)(mat,v1,v2,v3);CHKERRQ(ierr); 1354 ierr = PetscLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);CHKERRQ(ierr); 1355 ierr = PetscObjectIncreaseState((PetscObject)v3);CHKERRQ(ierr); 1356 PetscFunctionReturn(0); 1357 } 1358 1359 #undef __FUNCT__ 1360 #define __FUNCT__ "MatMultTransposeAdd" 1361 /*@ 1362 MatMultTransposeAdd - Computes v3 = v2 + A' * v1. 1363 1364 Collective on Mat and Vec 1365 1366 Input Parameters: 1367 + mat - the matrix 1368 - v1, v2 - the vectors 1369 1370 Output Parameters: 1371 . v3 - the result 1372 1373 Notes: 1374 The vectors v1 and v3 cannot be the same. I.e., one cannot 1375 call MatMultTransposeAdd(A,v1,v2,v1). 1376 1377 Level: beginner 1378 1379 Concepts: matrix vector product^transpose and addition 1380 1381 .seealso: MatMultTranspose(), MatMultAdd(), MatMult() 1382 @*/ 1383 PetscErrorCode MatMultTransposeAdd(Mat mat,Vec v1,Vec v2,Vec v3) 1384 { 1385 PetscErrorCode ierr; 1386 1387 PetscFunctionBegin; 1388 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1389 PetscValidType(mat,1); 1390 MatPreallocated(mat); 1391 PetscValidHeaderSpecific(v1,VEC_COOKIE,2); 1392 PetscValidHeaderSpecific(v2,VEC_COOKIE,3); 1393 PetscValidHeaderSpecific(v3,VEC_COOKIE,4); 1394 1395 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1396 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1397 if (!mat->ops->multtransposeadd) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1398 if (v1 == v3) SETERRQ(PETSC_ERR_ARG_IDN,"v1 and v3 must be different vectors"); 1399 if (mat->M != v1->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v1: global dim %D %D",mat->M,v1->N); 1400 if (mat->N != v2->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %D %D",mat->N,v2->N); 1401 if (mat->N != v3->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %D %D",mat->N,v3->N); 1402 1403 ierr = PetscLogEventBegin(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 1404 ierr = (*mat->ops->multtransposeadd)(mat,v1,v2,v3);CHKERRQ(ierr); 1405 ierr = PetscLogEventEnd(MAT_MultTransposeAdd,mat,v1,v2,v3);CHKERRQ(ierr); 1406 ierr = PetscObjectIncreaseState((PetscObject)v3);CHKERRQ(ierr); 1407 PetscFunctionReturn(0); 1408 } 1409 1410 #undef __FUNCT__ 1411 #define __FUNCT__ "MatMultConstrained" 1412 /*@ 1413 MatMultConstrained - The inner multiplication routine for a 1414 constrained matrix P^T A P. 1415 1416 Collective on Mat and Vec 1417 1418 Input Parameters: 1419 + mat - the matrix 1420 - x - the vector to be multilplied 1421 1422 Output Parameters: 1423 . y - the result 1424 1425 Notes: 1426 The vectors x and y cannot be the same. I.e., one cannot 1427 call MatMult(A,y,y). 1428 1429 Level: beginner 1430 1431 .keywords: matrix, multiply, matrix-vector product, constraint 1432 .seealso: MatMult(), MatMultTrans(), MatMultAdd(), MatMultTransAdd() 1433 @*/ 1434 PetscErrorCode MatMultConstrained(Mat mat,Vec x,Vec y) 1435 { 1436 PetscErrorCode ierr; 1437 1438 PetscFunctionBegin; 1439 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1440 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 1441 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 1442 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1443 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1444 if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 1445 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 1446 if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); 1447 if (mat->m != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: local dim %D %D",mat->m,y->n); 1448 1449 ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 1450 ierr = (*mat->ops->multconstrained)(mat,x,y);CHKERRQ(ierr); 1451 ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 1452 ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); 1453 1454 PetscFunctionReturn(0); 1455 } 1456 1457 #undef __FUNCT__ 1458 #define __FUNCT__ "MatMultTransposeConstrained" 1459 /*@ 1460 MatMultTransposeConstrained - The inner multiplication routine for a 1461 constrained matrix P^T A^T P. 1462 1463 Collective on Mat and Vec 1464 1465 Input Parameters: 1466 + mat - the matrix 1467 - x - the vector to be multilplied 1468 1469 Output Parameters: 1470 . y - the result 1471 1472 Notes: 1473 The vectors x and y cannot be the same. I.e., one cannot 1474 call MatMult(A,y,y). 1475 1476 Level: beginner 1477 1478 .keywords: matrix, multiply, matrix-vector product, constraint 1479 .seealso: MatMult(), MatMultTrans(), MatMultAdd(), MatMultTransAdd() 1480 @*/ 1481 PetscErrorCode MatMultTransposeConstrained(Mat mat,Vec x,Vec y) 1482 { 1483 PetscErrorCode ierr; 1484 1485 PetscFunctionBegin; 1486 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1487 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 1488 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 1489 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1490 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1491 if (x == y) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"x and y must be different vectors"); 1492 if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 1493 if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); 1494 1495 ierr = PetscLogEventBegin(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 1496 ierr = (*mat->ops->multtransposeconstrained)(mat,x,y);CHKERRQ(ierr); 1497 ierr = PetscLogEventEnd(MAT_MultConstrained,mat,x,y,0);CHKERRQ(ierr); 1498 ierr = PetscObjectIncreaseState((PetscObject)y);CHKERRQ(ierr); 1499 1500 PetscFunctionReturn(0); 1501 } 1502 /* ------------------------------------------------------------*/ 1503 #undef __FUNCT__ 1504 #define __FUNCT__ "MatGetInfo" 1505 /*@C 1506 MatGetInfo - Returns information about matrix storage (number of 1507 nonzeros, memory, etc.). 1508 1509 Collective on Mat if MAT_GLOBAL_MAX or MAT_GLOBAL_SUM is used 1510 as the flag 1511 1512 Input Parameters: 1513 . mat - the matrix 1514 1515 Output Parameters: 1516 + flag - flag indicating the type of parameters to be returned 1517 (MAT_LOCAL - local matrix, MAT_GLOBAL_MAX - maximum over all processors, 1518 MAT_GLOBAL_SUM - sum over all processors) 1519 - info - matrix information context 1520 1521 Notes: 1522 The MatInfo context contains a variety of matrix data, including 1523 number of nonzeros allocated and used, number of mallocs during 1524 matrix assembly, etc. Additional information for factored matrices 1525 is provided (such as the fill ratio, number of mallocs during 1526 factorization, etc.). Much of this info is printed to STDOUT 1527 when using the runtime options 1528 $ -log_info -mat_view_info 1529 1530 Example for C/C++ Users: 1531 See the file ${PETSC_DIR}/include/petscmat.h for a complete list of 1532 data within the MatInfo context. For example, 1533 .vb 1534 MatInfo info; 1535 Mat A; 1536 double mal, nz_a, nz_u; 1537 1538 MatGetInfo(A,MAT_LOCAL,&info); 1539 mal = info.mallocs; 1540 nz_a = info.nz_allocated; 1541 .ve 1542 1543 Example for Fortran Users: 1544 Fortran users should declare info as a double precision 1545 array of dimension MAT_INFO_SIZE, and then extract the parameters 1546 of interest. See the file ${PETSC_DIR}/include/finclude/petscmat.h 1547 a complete list of parameter names. 1548 .vb 1549 double precision info(MAT_INFO_SIZE) 1550 double precision mal, nz_a 1551 Mat A 1552 integer ierr 1553 1554 call MatGetInfo(A,MAT_LOCAL,info,ierr) 1555 mal = info(MAT_INFO_MALLOCS) 1556 nz_a = info(MAT_INFO_NZ_ALLOCATED) 1557 .ve 1558 1559 Level: intermediate 1560 1561 Concepts: matrices^getting information on 1562 1563 @*/ 1564 PetscErrorCode MatGetInfo(Mat mat,MatInfoType flag,MatInfo *info) 1565 { 1566 PetscErrorCode ierr; 1567 1568 PetscFunctionBegin; 1569 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1570 PetscValidType(mat,1); 1571 MatPreallocated(mat); 1572 PetscValidPointer(info,3); 1573 if (!mat->ops->getinfo) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1574 ierr = (*mat->ops->getinfo)(mat,flag,info);CHKERRQ(ierr); 1575 PetscFunctionReturn(0); 1576 } 1577 1578 /* ----------------------------------------------------------*/ 1579 #undef __FUNCT__ 1580 #define __FUNCT__ "MatILUDTFactor" 1581 /*@C 1582 MatILUDTFactor - Performs a drop tolerance ILU factorization. 1583 1584 Collective on Mat 1585 1586 Input Parameters: 1587 + mat - the matrix 1588 . info - information about the factorization to be done 1589 . row - row permutation 1590 - col - column permutation 1591 1592 Output Parameters: 1593 . fact - the factored matrix 1594 1595 Level: developer 1596 1597 Notes: 1598 Most users should employ the simplified KSP interface for linear solvers 1599 instead of working directly with matrix algebra routines such as this. 1600 See, e.g., KSPCreate(). 1601 1602 This is currently only supported for the SeqAIJ matrix format using code 1603 from Yousef Saad's SPARSEKIT2 package (translated to C with f2c) and/or 1604 Matlab. SPARSEKIT2 is copyrighted by Yousef Saad with the GNU copyright 1605 and thus can be distributed with PETSc. 1606 1607 Concepts: matrices^ILUDT factorization 1608 1609 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 1610 @*/ 1611 PetscErrorCode MatILUDTFactor(Mat mat,MatFactorInfo *info,IS row,IS col,Mat *fact) 1612 { 1613 PetscErrorCode ierr; 1614 1615 PetscFunctionBegin; 1616 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1617 PetscValidType(mat,1); 1618 MatPreallocated(mat); 1619 PetscValidPointer(info,2); 1620 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,3); 1621 if (col) PetscValidHeaderSpecific(col,IS_COOKIE,4); 1622 PetscValidPointer(fact,5); 1623 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1624 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1625 if (!mat->ops->iludtfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1626 1627 ierr = PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 1628 ierr = (*mat->ops->iludtfactor)(mat,info,row,col,fact);CHKERRQ(ierr); 1629 ierr = PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 1630 ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); 1631 1632 PetscFunctionReturn(0); 1633 } 1634 1635 #undef __FUNCT__ 1636 #define __FUNCT__ "MatLUFactor" 1637 /*@ 1638 MatLUFactor - Performs in-place LU factorization of matrix. 1639 1640 Collective on Mat 1641 1642 Input Parameters: 1643 + mat - the matrix 1644 . row - row permutation 1645 . col - column permutation 1646 - info - options for factorization, includes 1647 $ fill - expected fill as ratio of original fill. 1648 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 1649 $ Run with the option -log_info to determine an optimal value to use 1650 1651 Notes: 1652 Most users should employ the simplified KSP interface for linear solvers 1653 instead of working directly with matrix algebra routines such as this. 1654 See, e.g., KSPCreate(). 1655 1656 This changes the state of the matrix to a factored matrix; it cannot be used 1657 for example with MatSetValues() unless one first calls MatSetUnfactored(). 1658 1659 Level: developer 1660 1661 Concepts: matrices^LU factorization 1662 1663 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), 1664 MatGetOrdering(), MatSetUnfactored(), MatFactorInfo 1665 1666 @*/ 1667 PetscErrorCode MatLUFactor(Mat mat,IS row,IS col,MatFactorInfo *info) 1668 { 1669 PetscErrorCode ierr; 1670 1671 PetscFunctionBegin; 1672 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1673 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); 1674 if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); 1675 PetscValidPointer(info,4); 1676 PetscValidType(mat,1); 1677 MatPreallocated(mat); 1678 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1679 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1680 if (!mat->ops->lufactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1681 1682 ierr = PetscLogEventBegin(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); 1683 ierr = (*mat->ops->lufactor)(mat,row,col,info);CHKERRQ(ierr); 1684 ierr = PetscLogEventEnd(MAT_LUFactor,mat,row,col,0);CHKERRQ(ierr); 1685 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 1686 PetscFunctionReturn(0); 1687 } 1688 1689 #undef __FUNCT__ 1690 #define __FUNCT__ "MatILUFactor" 1691 /*@ 1692 MatILUFactor - Performs in-place ILU factorization of matrix. 1693 1694 Collective on Mat 1695 1696 Input Parameters: 1697 + mat - the matrix 1698 . row - row permutation 1699 . col - column permutation 1700 - info - structure containing 1701 $ levels - number of levels of fill. 1702 $ expected fill - as ratio of original fill. 1703 $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices 1704 missing diagonal entries) 1705 1706 Notes: 1707 Probably really in-place only when level of fill is zero, otherwise allocates 1708 new space to store factored matrix and deletes previous memory. 1709 1710 Most users should employ the simplified KSP interface for linear solvers 1711 instead of working directly with matrix algebra routines such as this. 1712 See, e.g., KSPCreate(). 1713 1714 Level: developer 1715 1716 Concepts: matrices^ILU factorization 1717 1718 .seealso: MatILUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 1719 @*/ 1720 PetscErrorCode MatILUFactor(Mat mat,IS row,IS col,MatFactorInfo *info) 1721 { 1722 PetscErrorCode ierr; 1723 1724 PetscFunctionBegin; 1725 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1726 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); 1727 if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); 1728 PetscValidPointer(info,4); 1729 PetscValidType(mat,1); 1730 MatPreallocated(mat); 1731 if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"matrix must be square"); 1732 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1733 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1734 if (!mat->ops->ilufactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1735 1736 ierr = PetscLogEventBegin(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 1737 ierr = (*mat->ops->ilufactor)(mat,row,col,info);CHKERRQ(ierr); 1738 ierr = PetscLogEventEnd(MAT_ILUFactor,mat,row,col,0);CHKERRQ(ierr); 1739 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 1740 PetscFunctionReturn(0); 1741 } 1742 1743 #undef __FUNCT__ 1744 #define __FUNCT__ "MatLUFactorSymbolic" 1745 /*@ 1746 MatLUFactorSymbolic - Performs symbolic LU factorization of matrix. 1747 Call this routine before calling MatLUFactorNumeric(). 1748 1749 Collective on Mat 1750 1751 Input Parameters: 1752 + mat - the matrix 1753 . row, col - row and column permutations 1754 - info - options for factorization, includes 1755 $ fill - expected fill as ratio of original fill. 1756 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 1757 $ Run with the option -log_info to determine an optimal value to use 1758 1759 Output Parameter: 1760 . fact - new matrix that has been symbolically factored 1761 1762 Notes: 1763 See the users manual for additional information about 1764 choosing the fill factor for better efficiency. 1765 1766 Most users should employ the simplified KSP interface for linear solvers 1767 instead of working directly with matrix algebra routines such as this. 1768 See, e.g., KSPCreate(). 1769 1770 Level: developer 1771 1772 Concepts: matrices^LU symbolic factorization 1773 1774 .seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 1775 @*/ 1776 PetscErrorCode MatLUFactorSymbolic(Mat mat,IS row,IS col,MatFactorInfo *info,Mat *fact) 1777 { 1778 PetscErrorCode ierr; 1779 1780 PetscFunctionBegin; 1781 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1782 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); 1783 if (col) PetscValidHeaderSpecific(col,IS_COOKIE,3); 1784 PetscValidPointer(info,4); 1785 PetscValidType(mat,1); 1786 MatPreallocated(mat); 1787 PetscValidPointer(fact,5); 1788 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1789 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1790 if (!mat->ops->lufactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic LU",mat->type_name); 1791 1792 ierr = PetscLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 1793 ierr = (*mat->ops->lufactorsymbolic)(mat,row,col,info,fact);CHKERRQ(ierr); 1794 ierr = PetscLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 1795 ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); 1796 PetscFunctionReturn(0); 1797 } 1798 1799 #undef __FUNCT__ 1800 #define __FUNCT__ "MatLUFactorNumeric" 1801 /*@ 1802 MatLUFactorNumeric - Performs numeric LU factorization of a matrix. 1803 Call this routine after first calling MatLUFactorSymbolic(). 1804 1805 Collective on Mat 1806 1807 Input Parameters: 1808 + mat - the matrix 1809 - fact - the matrix generated for the factor, from MatLUFactorSymbolic() 1810 1811 Notes: 1812 See MatLUFactor() for in-place factorization. See 1813 MatCholeskyFactorNumeric() for the symmetric, positive definite case. 1814 1815 Most users should employ the simplified KSP interface for linear solvers 1816 instead of working directly with matrix algebra routines such as this. 1817 See, e.g., KSPCreate(). 1818 1819 Level: developer 1820 1821 Concepts: matrices^LU numeric factorization 1822 1823 .seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor() 1824 @*/ 1825 PetscErrorCode MatLUFactorNumeric(Mat mat,Mat *fact) 1826 { 1827 PetscErrorCode ierr; 1828 1829 PetscFunctionBegin; 1830 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1831 PetscValidType(mat,1); 1832 MatPreallocated(mat); 1833 PetscValidPointer(fact,2); 1834 PetscValidHeaderSpecific(*fact,MAT_COOKIE,2); 1835 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1836 if (mat->M != (*fact)->M || mat->N != (*fact)->N) { 1837 SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat mat,Mat *fact: global dimensions are different %D should = %D %D should = %D", 1838 mat->M,(*fact)->M,mat->N,(*fact)->N); 1839 } 1840 if (!(*fact)->ops->lufactornumeric) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1841 1842 ierr = PetscLogEventBegin(MAT_LUFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); 1843 ierr = (*(*fact)->ops->lufactornumeric)(mat,fact);CHKERRQ(ierr); 1844 ierr = PetscLogEventEnd(MAT_LUFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); 1845 1846 ierr = MatView_Private(*fact);CHKERRQ(ierr); 1847 ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); 1848 PetscFunctionReturn(0); 1849 } 1850 1851 #undef __FUNCT__ 1852 #define __FUNCT__ "MatCholeskyFactor" 1853 /*@ 1854 MatCholeskyFactor - Performs in-place Cholesky factorization of a 1855 symmetric matrix. 1856 1857 Collective on Mat 1858 1859 Input Parameters: 1860 + mat - the matrix 1861 . perm - row and column permutations 1862 - f - expected fill as ratio of original fill 1863 1864 Notes: 1865 See MatLUFactor() for the nonsymmetric case. See also 1866 MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric(). 1867 1868 Most users should employ the simplified KSP interface for linear solvers 1869 instead of working directly with matrix algebra routines such as this. 1870 See, e.g., KSPCreate(). 1871 1872 Level: developer 1873 1874 Concepts: matrices^Cholesky factorization 1875 1876 .seealso: MatLUFactor(), MatCholeskyFactorSymbolic(), MatCholeskyFactorNumeric() 1877 MatGetOrdering() 1878 1879 @*/ 1880 PetscErrorCode MatCholeskyFactor(Mat mat,IS perm,MatFactorInfo *info) 1881 { 1882 PetscErrorCode ierr; 1883 1884 PetscFunctionBegin; 1885 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1886 PetscValidType(mat,1); 1887 MatPreallocated(mat); 1888 PetscValidHeaderSpecific(perm,IS_COOKIE,2); 1889 PetscValidPointer(info,3); 1890 if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"Matrix must be square"); 1891 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1892 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1893 if (!mat->ops->choleskyfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1894 1895 ierr = PetscLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); 1896 ierr = (*mat->ops->choleskyfactor)(mat,perm,info);CHKERRQ(ierr); 1897 ierr = PetscLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);CHKERRQ(ierr); 1898 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 1899 PetscFunctionReturn(0); 1900 } 1901 1902 #undef __FUNCT__ 1903 #define __FUNCT__ "MatCholeskyFactorSymbolic" 1904 /*@ 1905 MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization 1906 of a symmetric matrix. 1907 1908 Collective on Mat 1909 1910 Input Parameters: 1911 + mat - the matrix 1912 . perm - row and column permutations 1913 - info - options for factorization, includes 1914 $ fill - expected fill as ratio of original fill. 1915 $ dtcol - pivot tolerance (0 no pivot, 1 full column pivoting) 1916 $ Run with the option -log_info to determine an optimal value to use 1917 1918 Output Parameter: 1919 . fact - the factored matrix 1920 1921 Notes: 1922 See MatLUFactorSymbolic() for the nonsymmetric case. See also 1923 MatCholeskyFactor() and MatCholeskyFactorNumeric(). 1924 1925 Most users should employ the simplified KSP interface for linear solvers 1926 instead of working directly with matrix algebra routines such as this. 1927 See, e.g., KSPCreate(). 1928 1929 Level: developer 1930 1931 Concepts: matrices^Cholesky symbolic factorization 1932 1933 .seealso: MatLUFactorSymbolic(), MatCholeskyFactor(), MatCholeskyFactorNumeric() 1934 MatGetOrdering() 1935 1936 @*/ 1937 PetscErrorCode MatCholeskyFactorSymbolic(Mat mat,IS perm,MatFactorInfo *info,Mat *fact) 1938 { 1939 PetscErrorCode ierr; 1940 1941 PetscFunctionBegin; 1942 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1943 PetscValidType(mat,1); 1944 MatPreallocated(mat); 1945 if (perm) PetscValidHeaderSpecific(perm,IS_COOKIE,2); 1946 PetscValidPointer(info,3); 1947 PetscValidPointer(fact,4); 1948 if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"Matrix must be square"); 1949 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1950 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 1951 if (!mat->ops->choleskyfactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1952 1953 ierr = PetscLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 1954 ierr = (*mat->ops->choleskyfactorsymbolic)(mat,perm,info,fact);CHKERRQ(ierr); 1955 ierr = PetscLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 1956 ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); 1957 PetscFunctionReturn(0); 1958 } 1959 1960 #undef __FUNCT__ 1961 #define __FUNCT__ "MatCholeskyFactorNumeric" 1962 /*@ 1963 MatCholeskyFactorNumeric - Performs numeric Cholesky factorization 1964 of a symmetric matrix. Call this routine after first calling 1965 MatCholeskyFactorSymbolic(). 1966 1967 Collective on Mat 1968 1969 Input Parameter: 1970 . mat - the initial matrix 1971 1972 Output Parameter: 1973 . fact - the factored matrix 1974 1975 Notes: 1976 Most users should employ the simplified KSP interface for linear solvers 1977 instead of working directly with matrix algebra routines such as this. 1978 See, e.g., KSPCreate(). 1979 1980 Level: developer 1981 1982 Concepts: matrices^Cholesky numeric factorization 1983 1984 .seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor(), MatLUFactorNumeric() 1985 @*/ 1986 PetscErrorCode MatCholeskyFactorNumeric(Mat mat,Mat *fact) 1987 { 1988 PetscErrorCode ierr; 1989 1990 PetscFunctionBegin; 1991 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 1992 PetscValidType(mat,1); 1993 MatPreallocated(mat); 1994 PetscValidPointer(fact,2); 1995 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 1996 if (!(*fact)->ops->choleskyfactornumeric) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 1997 if (mat->M != (*fact)->M || mat->N != (*fact)->N) { 1998 SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat mat,Mat *fact: global dim %D should = %D %D should = %D", 1999 mat->M,(*fact)->M,mat->N,(*fact)->N); 2000 } 2001 2002 ierr = PetscLogEventBegin(MAT_CholeskyFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); 2003 ierr = (*(*fact)->ops->choleskyfactornumeric)(mat,fact);CHKERRQ(ierr); 2004 ierr = PetscLogEventEnd(MAT_CholeskyFactorNumeric,mat,*fact,0,0);CHKERRQ(ierr); 2005 ierr = PetscObjectIncreaseState((PetscObject)*fact);CHKERRQ(ierr); 2006 PetscFunctionReturn(0); 2007 } 2008 2009 /* ----------------------------------------------------------------*/ 2010 #undef __FUNCT__ 2011 #define __FUNCT__ "MatSolve" 2012 /*@ 2013 MatSolve - Solves A x = b, given a factored matrix. 2014 2015 Collective on Mat and Vec 2016 2017 Input Parameters: 2018 + mat - the factored matrix 2019 - b - the right-hand-side vector 2020 2021 Output Parameter: 2022 . x - the result vector 2023 2024 Notes: 2025 The vectors b and x cannot be the same. I.e., one cannot 2026 call MatSolve(A,x,x). 2027 2028 Notes: 2029 Most users should employ the simplified KSP interface for linear solvers 2030 instead of working directly with matrix algebra routines such as this. 2031 See, e.g., KSPCreate(). 2032 2033 Level: developer 2034 2035 Concepts: matrices^triangular solves 2036 2037 .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd() 2038 @*/ 2039 PetscErrorCode MatSolve(Mat mat,Vec b,Vec x) 2040 { 2041 PetscErrorCode ierr; 2042 2043 PetscFunctionBegin; 2044 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2045 PetscValidType(mat,1); 2046 MatPreallocated(mat); 2047 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2048 PetscValidHeaderSpecific(x,VEC_COOKIE,3); 2049 PetscCheckSameComm(mat,1,b,2); 2050 PetscCheckSameComm(mat,1,x,3); 2051 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2052 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2053 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2054 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2055 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2056 if (!mat->M && !mat->N) PetscFunctionReturn(0); 2057 2058 if (!mat->ops->solve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2059 ierr = PetscLogEventBegin(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); 2060 ierr = (*mat->ops->solve)(mat,b,x);CHKERRQ(ierr); 2061 ierr = PetscLogEventEnd(MAT_Solve,mat,b,x,0);CHKERRQ(ierr); 2062 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2063 PetscFunctionReturn(0); 2064 } 2065 2066 #undef __FUNCT__ 2067 #define __FUNCT__ "MatForwardSolve" 2068 /* @ 2069 MatForwardSolve - Solves L x = b, given a factored matrix, A = LU. 2070 2071 Collective on Mat and Vec 2072 2073 Input Parameters: 2074 + mat - the factored matrix 2075 - b - the right-hand-side vector 2076 2077 Output Parameter: 2078 . x - the result vector 2079 2080 Notes: 2081 MatSolve() should be used for most applications, as it performs 2082 a forward solve followed by a backward solve. 2083 2084 The vectors b and x cannot be the same. I.e., one cannot 2085 call MatForwardSolve(A,x,x). 2086 2087 Most users should employ the simplified KSP interface for linear solvers 2088 instead of working directly with matrix algebra routines such as this. 2089 See, e.g., KSPCreate(). 2090 2091 Level: developer 2092 2093 Concepts: matrices^forward solves 2094 2095 .seealso: MatSolve(), MatBackwardSolve() 2096 @ */ 2097 PetscErrorCode MatForwardSolve(Mat mat,Vec b,Vec x) 2098 { 2099 PetscErrorCode ierr; 2100 2101 PetscFunctionBegin; 2102 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2103 PetscValidType(mat,1); 2104 MatPreallocated(mat); 2105 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2106 PetscValidHeaderSpecific(x,VEC_COOKIE,3); 2107 PetscCheckSameComm(mat,1,b,2); 2108 PetscCheckSameComm(mat,1,x,3); 2109 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2110 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2111 if (!mat->ops->forwardsolve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2112 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2113 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2114 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2115 2116 ierr = PetscLogEventBegin(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); 2117 ierr = (*mat->ops->forwardsolve)(mat,b,x);CHKERRQ(ierr); 2118 ierr = PetscLogEventEnd(MAT_ForwardSolve,mat,b,x,0);CHKERRQ(ierr); 2119 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2120 PetscFunctionReturn(0); 2121 } 2122 2123 #undef __FUNCT__ 2124 #define __FUNCT__ "MatBackwardSolve" 2125 /* @ 2126 MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU. 2127 2128 Collective on Mat and Vec 2129 2130 Input Parameters: 2131 + mat - the factored matrix 2132 - b - the right-hand-side vector 2133 2134 Output Parameter: 2135 . x - the result vector 2136 2137 Notes: 2138 MatSolve() should be used for most applications, as it performs 2139 a forward solve followed by a backward solve. 2140 2141 The vectors b and x cannot be the same. I.e., one cannot 2142 call MatBackwardSolve(A,x,x). 2143 2144 Most users should employ the simplified KSP interface for linear solvers 2145 instead of working directly with matrix algebra routines such as this. 2146 See, e.g., KSPCreate(). 2147 2148 Level: developer 2149 2150 Concepts: matrices^backward solves 2151 2152 .seealso: MatSolve(), MatForwardSolve() 2153 @ */ 2154 PetscErrorCode MatBackwardSolve(Mat mat,Vec b,Vec x) 2155 { 2156 PetscErrorCode ierr; 2157 2158 PetscFunctionBegin; 2159 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2160 PetscValidType(mat,1); 2161 MatPreallocated(mat); 2162 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2163 PetscValidHeaderSpecific(x,VEC_COOKIE,3); 2164 PetscCheckSameComm(mat,1,b,2); 2165 PetscCheckSameComm(mat,1,x,3); 2166 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2167 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2168 if (!mat->ops->backwardsolve) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2169 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2170 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2171 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2172 2173 ierr = PetscLogEventBegin(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); 2174 ierr = (*mat->ops->backwardsolve)(mat,b,x);CHKERRQ(ierr); 2175 ierr = PetscLogEventEnd(MAT_BackwardSolve,mat,b,x,0);CHKERRQ(ierr); 2176 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2177 PetscFunctionReturn(0); 2178 } 2179 2180 #undef __FUNCT__ 2181 #define __FUNCT__ "MatSolveAdd" 2182 /*@ 2183 MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix. 2184 2185 Collective on Mat and Vec 2186 2187 Input Parameters: 2188 + mat - the factored matrix 2189 . b - the right-hand-side vector 2190 - y - the vector to be added to 2191 2192 Output Parameter: 2193 . x - the result vector 2194 2195 Notes: 2196 The vectors b and x cannot be the same. I.e., one cannot 2197 call MatSolveAdd(A,x,y,x). 2198 2199 Most users should employ the simplified KSP interface for linear solvers 2200 instead of working directly with matrix algebra routines such as this. 2201 See, e.g., KSPCreate(). 2202 2203 Level: developer 2204 2205 Concepts: matrices^triangular solves 2206 2207 .seealso: MatSolve(), MatSolveTranspose(), MatSolveTransposeAdd() 2208 @*/ 2209 PetscErrorCode MatSolveAdd(Mat mat,Vec b,Vec y,Vec x) 2210 { 2211 PetscScalar one = 1.0; 2212 Vec tmp; 2213 PetscErrorCode ierr; 2214 2215 PetscFunctionBegin; 2216 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2217 PetscValidType(mat,1); 2218 MatPreallocated(mat); 2219 PetscValidHeaderSpecific(y,VEC_COOKIE,2); 2220 PetscValidHeaderSpecific(b,VEC_COOKIE,3); 2221 PetscValidHeaderSpecific(x,VEC_COOKIE,4); 2222 PetscCheckSameComm(mat,1,b,2); 2223 PetscCheckSameComm(mat,1,y,2); 2224 PetscCheckSameComm(mat,1,x,3); 2225 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2226 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2227 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2228 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2229 if (mat->M != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->M,y->N); 2230 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2231 if (x->n != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->n,y->n); 2232 2233 ierr = PetscLogEventBegin(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); 2234 if (mat->ops->solveadd) { 2235 ierr = (*mat->ops->solveadd)(mat,b,y,x);CHKERRQ(ierr); 2236 } else { 2237 /* do the solve then the add manually */ 2238 if (x != y) { 2239 ierr = MatSolve(mat,b,x);CHKERRQ(ierr); 2240 ierr = VecAXPY(&one,y,x);CHKERRQ(ierr); 2241 } else { 2242 ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); 2243 PetscLogObjectParent(mat,tmp); 2244 ierr = VecCopy(x,tmp);CHKERRQ(ierr); 2245 ierr = MatSolve(mat,b,x);CHKERRQ(ierr); 2246 ierr = VecAXPY(&one,tmp,x);CHKERRQ(ierr); 2247 ierr = VecDestroy(tmp);CHKERRQ(ierr); 2248 } 2249 } 2250 ierr = PetscLogEventEnd(MAT_SolveAdd,mat,b,x,y);CHKERRQ(ierr); 2251 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2252 PetscFunctionReturn(0); 2253 } 2254 2255 #undef __FUNCT__ 2256 #define __FUNCT__ "MatSolveTranspose" 2257 /*@ 2258 MatSolveTranspose - Solves A' x = b, given a factored matrix. 2259 2260 Collective on Mat and Vec 2261 2262 Input Parameters: 2263 + mat - the factored matrix 2264 - b - the right-hand-side vector 2265 2266 Output Parameter: 2267 . x - the result vector 2268 2269 Notes: 2270 The vectors b and x cannot be the same. I.e., one cannot 2271 call MatSolveTranspose(A,x,x). 2272 2273 Most users should employ the simplified KSP interface for linear solvers 2274 instead of working directly with matrix algebra routines such as this. 2275 See, e.g., KSPCreate(). 2276 2277 Level: developer 2278 2279 Concepts: matrices^triangular solves 2280 2281 .seealso: MatSolve(), MatSolveAdd(), MatSolveTransposeAdd() 2282 @*/ 2283 PetscErrorCode MatSolveTranspose(Mat mat,Vec b,Vec x) 2284 { 2285 PetscErrorCode ierr; 2286 2287 PetscFunctionBegin; 2288 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2289 PetscValidType(mat,1); 2290 MatPreallocated(mat); 2291 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2292 PetscValidHeaderSpecific(x,VEC_COOKIE,3); 2293 PetscCheckSameComm(mat,1,b,2); 2294 PetscCheckSameComm(mat,1,x,3); 2295 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2296 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2297 if (!mat->ops->solvetranspose) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s",mat->type_name); 2298 if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); 2299 if (mat->N != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->N,b->N); 2300 2301 ierr = PetscLogEventBegin(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); 2302 ierr = (*mat->ops->solvetranspose)(mat,b,x);CHKERRQ(ierr); 2303 ierr = PetscLogEventEnd(MAT_SolveTranspose,mat,b,x,0);CHKERRQ(ierr); 2304 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2305 PetscFunctionReturn(0); 2306 } 2307 2308 #undef __FUNCT__ 2309 #define __FUNCT__ "MatSolveTransposeAdd" 2310 /*@ 2311 MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a 2312 factored matrix. 2313 2314 Collective on Mat and Vec 2315 2316 Input Parameters: 2317 + mat - the factored matrix 2318 . b - the right-hand-side vector 2319 - y - the vector to be added to 2320 2321 Output Parameter: 2322 . x - the result vector 2323 2324 Notes: 2325 The vectors b and x cannot be the same. I.e., one cannot 2326 call MatSolveTransposeAdd(A,x,y,x). 2327 2328 Most users should employ the simplified KSP interface for linear solvers 2329 instead of working directly with matrix algebra routines such as this. 2330 See, e.g., KSPCreate(). 2331 2332 Level: developer 2333 2334 Concepts: matrices^triangular solves 2335 2336 .seealso: MatSolve(), MatSolveAdd(), MatSolveTranspose() 2337 @*/ 2338 PetscErrorCode MatSolveTransposeAdd(Mat mat,Vec b,Vec y,Vec x) 2339 { 2340 PetscScalar one = 1.0; 2341 PetscErrorCode ierr; 2342 Vec tmp; 2343 2344 PetscFunctionBegin; 2345 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2346 PetscValidType(mat,1); 2347 MatPreallocated(mat); 2348 PetscValidHeaderSpecific(y,VEC_COOKIE,2); 2349 PetscValidHeaderSpecific(b,VEC_COOKIE,3); 2350 PetscValidHeaderSpecific(x,VEC_COOKIE,4); 2351 PetscCheckSameComm(mat,1,b,2); 2352 PetscCheckSameComm(mat,1,y,3); 2353 PetscCheckSameComm(mat,1,x,4); 2354 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 2355 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 2356 if (mat->M != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->M,x->N); 2357 if (mat->N != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->N,b->N); 2358 if (mat->N != y->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec y: global dim %D %D",mat->N,y->N); 2359 if (x->n != y->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Vec x,Vec y: local dim %D %D",x->n,y->n); 2360 2361 ierr = PetscLogEventBegin(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); 2362 if (mat->ops->solvetransposeadd) { 2363 ierr = (*mat->ops->solvetransposeadd)(mat,b,y,x);CHKERRQ(ierr); 2364 } else { 2365 /* do the solve then the add manually */ 2366 if (x != y) { 2367 ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); 2368 ierr = VecAXPY(&one,y,x);CHKERRQ(ierr); 2369 } else { 2370 ierr = VecDuplicate(x,&tmp);CHKERRQ(ierr); 2371 PetscLogObjectParent(mat,tmp); 2372 ierr = VecCopy(x,tmp);CHKERRQ(ierr); 2373 ierr = MatSolveTranspose(mat,b,x);CHKERRQ(ierr); 2374 ierr = VecAXPY(&one,tmp,x);CHKERRQ(ierr); 2375 ierr = VecDestroy(tmp);CHKERRQ(ierr); 2376 } 2377 } 2378 ierr = PetscLogEventEnd(MAT_SolveTransposeAdd,mat,b,x,y);CHKERRQ(ierr); 2379 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2380 PetscFunctionReturn(0); 2381 } 2382 /* ----------------------------------------------------------------*/ 2383 2384 #undef __FUNCT__ 2385 #define __FUNCT__ "MatRelax" 2386 /*@ 2387 MatRelax - Computes relaxation (SOR, Gauss-Seidel) sweeps. 2388 2389 Collective on Mat and Vec 2390 2391 Input Parameters: 2392 + mat - the matrix 2393 . b - the right hand side 2394 . omega - the relaxation factor 2395 . flag - flag indicating the type of SOR (see below) 2396 . shift - diagonal shift 2397 - its - the number of iterations 2398 - lits - the number of local iterations 2399 2400 Output Parameters: 2401 . x - the solution (can contain an initial guess) 2402 2403 SOR Flags: 2404 . SOR_FORWARD_SWEEP - forward SOR 2405 . SOR_BACKWARD_SWEEP - backward SOR 2406 . SOR_SYMMETRIC_SWEEP - SSOR (symmetric SOR) 2407 . SOR_LOCAL_FORWARD_SWEEP - local forward SOR 2408 . SOR_LOCAL_BACKWARD_SWEEP - local forward SOR 2409 . SOR_LOCAL_SYMMETRIC_SWEEP - local SSOR 2410 . SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies 2411 upper/lower triangular part of matrix to 2412 vector (with omega) 2413 . SOR_ZERO_INITIAL_GUESS - zero initial guess 2414 2415 Notes: 2416 SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and 2417 SOR_LOCAL_SYMMETRIC_SWEEP perform seperate independent smoothings 2418 on each processor. 2419 2420 Application programmers will not generally use MatRelax() directly, 2421 but instead will employ the KSP/PC interface. 2422 2423 Notes for Advanced Users: 2424 The flags are implemented as bitwise inclusive or operations. 2425 For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP) 2426 to specify a zero initial guess for SSOR. 2427 2428 Most users should employ the simplified KSP interface for linear solvers 2429 instead of working directly with matrix algebra routines such as this. 2430 See, e.g., KSPCreate(). 2431 2432 See also, MatPBRelax(). This routine will automatically call the point block 2433 version if the point version is not available. 2434 2435 Level: developer 2436 2437 Concepts: matrices^relaxation 2438 Concepts: matrices^SOR 2439 Concepts: matrices^Gauss-Seidel 2440 2441 @*/ 2442 PetscErrorCode MatRelax(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) 2443 { 2444 PetscErrorCode ierr; 2445 2446 PetscFunctionBegin; 2447 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2448 PetscValidType(mat,1); 2449 MatPreallocated(mat); 2450 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2451 PetscValidHeaderSpecific(x,VEC_COOKIE,8); 2452 PetscCheckSameComm(mat,1,b,2); 2453 PetscCheckSameComm(mat,1,x,8); 2454 if (!mat->ops->relax && !mat->ops->pbrelax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2455 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2456 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2457 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2458 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2459 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2460 2461 ierr = PetscLogEventBegin(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); 2462 if (mat->ops->relax) { 2463 ierr =(*mat->ops->relax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); 2464 } else { 2465 ierr =(*mat->ops->pbrelax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); 2466 } 2467 ierr = PetscLogEventEnd(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); 2468 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2469 PetscFunctionReturn(0); 2470 } 2471 2472 #undef __FUNCT__ 2473 #define __FUNCT__ "MatPBRelax" 2474 /*@ 2475 MatPBRelax - Computes relaxation (SOR, Gauss-Seidel) sweeps. 2476 2477 Collective on Mat and Vec 2478 2479 See MatRelax() for usage 2480 2481 For multi-component PDEs where the Jacobian is stored in a point block format 2482 (with the PETSc BAIJ matrix formats) the relaxation is done one point block at 2483 a time. That is, the small (for example, 4 by 4) blocks along the diagonal are solved 2484 simultaneously (that is a 4 by 4 linear solve is done) to update all the values at a point. 2485 2486 Level: developer 2487 2488 @*/ 2489 PetscErrorCode MatPBRelax(Mat mat,Vec b,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec x) 2490 { 2491 PetscErrorCode ierr; 2492 2493 PetscFunctionBegin; 2494 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2495 PetscValidType(mat,1); 2496 MatPreallocated(mat); 2497 PetscValidHeaderSpecific(b,VEC_COOKIE,2); 2498 PetscValidHeaderSpecific(x,VEC_COOKIE,8); 2499 PetscCheckSameComm(mat,1,b,2); 2500 PetscCheckSameComm(mat,1,x,8); 2501 if (!mat->ops->pbrelax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2502 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2503 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2504 if (mat->N != x->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec x: global dim %D %D",mat->N,x->N); 2505 if (mat->M != b->N) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: global dim %D %D",mat->M,b->N); 2506 if (mat->m != b->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"Mat mat,Vec b: local dim %D %D",mat->m,b->n); 2507 2508 ierr = PetscLogEventBegin(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); 2509 ierr =(*mat->ops->pbrelax)(mat,b,omega,flag,shift,its,lits,x);CHKERRQ(ierr); 2510 ierr = PetscLogEventEnd(MAT_Relax,mat,b,x,0);CHKERRQ(ierr); 2511 ierr = PetscObjectIncreaseState((PetscObject)x);CHKERRQ(ierr); 2512 PetscFunctionReturn(0); 2513 } 2514 2515 #undef __FUNCT__ 2516 #define __FUNCT__ "MatCopy_Basic" 2517 /* 2518 Default matrix copy routine. 2519 */ 2520 PetscErrorCode MatCopy_Basic(Mat A,Mat B,MatStructure str) 2521 { 2522 PetscErrorCode ierr; 2523 PetscInt i,rstart,rend,nz; 2524 const PetscInt *cwork; 2525 const PetscScalar *vwork; 2526 2527 PetscFunctionBegin; 2528 if (B->assembled) { 2529 ierr = MatZeroEntries(B);CHKERRQ(ierr); 2530 } 2531 ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); 2532 for (i=rstart; i<rend; i++) { 2533 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2534 ierr = MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 2535 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2536 } 2537 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2538 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2539 ierr = PetscObjectIncreaseState((PetscObject)B);CHKERRQ(ierr); 2540 PetscFunctionReturn(0); 2541 } 2542 2543 #undef __FUNCT__ 2544 #define __FUNCT__ "MatCopy" 2545 /*@C 2546 MatCopy - Copys a matrix to another matrix. 2547 2548 Collective on Mat 2549 2550 Input Parameters: 2551 + A - the matrix 2552 - str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN 2553 2554 Output Parameter: 2555 . B - where the copy is put 2556 2557 Notes: 2558 If you use SAME_NONZERO_PATTERN then the two matrices had better have the 2559 same nonzero pattern or the routine will crash. 2560 2561 MatCopy() copies the matrix entries of a matrix to another existing 2562 matrix (after first zeroing the second matrix). A related routine is 2563 MatConvert(), which first creates a new matrix and then copies the data. 2564 2565 Level: intermediate 2566 2567 Concepts: matrices^copying 2568 2569 .seealso: MatConvert(), MatDuplicate() 2570 2571 @*/ 2572 PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str) 2573 { 2574 PetscErrorCode ierr; 2575 2576 PetscFunctionBegin; 2577 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 2578 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 2579 PetscValidType(A,1); 2580 MatPreallocated(A); 2581 PetscValidType(B,2); 2582 MatPreallocated(B); 2583 PetscCheckSameComm(A,1,B,2); 2584 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2585 if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2586 if (A->M != B->M || A->N != B->N) SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim (%D,%D) (%D,%D)",A->M,B->M, 2587 A->N,B->N); 2588 2589 ierr = PetscLogEventBegin(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 2590 if (A->ops->copy) { 2591 ierr = (*A->ops->copy)(A,B,str);CHKERRQ(ierr); 2592 } else { /* generic conversion */ 2593 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 2594 } 2595 if (A->mapping) { 2596 if (B->mapping) {ierr = ISLocalToGlobalMappingDestroy(B->mapping);CHKERRQ(ierr);B->mapping = 0;} 2597 ierr = MatSetLocalToGlobalMapping(B,A->mapping);CHKERRQ(ierr); 2598 } 2599 if (A->bmapping) { 2600 if (B->bmapping) {ierr = ISLocalToGlobalMappingDestroy(B->bmapping);CHKERRQ(ierr);B->bmapping = 0;} 2601 ierr = MatSetLocalToGlobalMappingBlock(B,A->mapping);CHKERRQ(ierr); 2602 } 2603 ierr = PetscLogEventEnd(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 2604 ierr = PetscObjectIncreaseState((PetscObject)B);CHKERRQ(ierr); 2605 PetscFunctionReturn(0); 2606 } 2607 2608 #include "petscsys.h" 2609 PetscTruth MatConvertRegisterAllCalled = PETSC_FALSE; 2610 PetscFList MatConvertList = 0; 2611 2612 #undef __FUNCT__ 2613 #define __FUNCT__ "MatConvertRegister" 2614 /*@C 2615 MatConvertRegister - Allows one to register a routine that converts a sparse matrix 2616 from one format to another. 2617 2618 Not Collective 2619 2620 Input Parameters: 2621 + type - the type of matrix (defined in include/petscmat.h), for example, MATSEQAIJ. 2622 - Converter - the function that reads the matrix from the binary file. 2623 2624 Level: developer 2625 2626 .seealso: MatConvertRegisterAll(), MatConvert() 2627 2628 @*/ 2629 PetscErrorCode MatConvertRegister(const char sname[],const char path[],const char name[],PetscErrorCode (*function)(Mat,MatType,Mat*)) 2630 { 2631 PetscErrorCode ierr; 2632 char fullname[PETSC_MAX_PATH_LEN]; 2633 2634 PetscFunctionBegin; 2635 ierr = PetscFListConcat(path,name,fullname);CHKERRQ(ierr); 2636 ierr = PetscFListAdd(&MatConvertList,sname,fullname,(void (*)(void))function);CHKERRQ(ierr); 2637 PetscFunctionReturn(0); 2638 } 2639 2640 #undef __FUNCT__ 2641 #define __FUNCT__ "MatConvert" 2642 /*@C 2643 MatConvert - Converts a matrix to another matrix, either of the same 2644 or different type. 2645 2646 Collective on Mat 2647 2648 Input Parameters: 2649 + mat - the matrix 2650 - newtype - new matrix type. Use MATSAME to create a new matrix of the 2651 same type as the original matrix. 2652 2653 Output Parameter: 2654 . M - pointer to place new matrix 2655 2656 Notes: 2657 MatConvert() first creates a new matrix and then copies the data from 2658 the first matrix. A related routine is MatCopy(), which copies the matrix 2659 entries of one matrix to another already existing matrix context. 2660 2661 Level: intermediate 2662 2663 Concepts: matrices^converting between storage formats 2664 2665 .seealso: MatCopy(), MatDuplicate() 2666 @*/ 2667 PetscErrorCode MatConvert(Mat mat,const MatType newtype,Mat *M) 2668 { 2669 PetscErrorCode ierr; 2670 PetscTruth sametype,issame,flg; 2671 char convname[256],mtype[256]; 2672 2673 PetscFunctionBegin; 2674 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2675 PetscValidType(mat,1); 2676 MatPreallocated(mat); 2677 PetscValidPointer(M,3); 2678 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2679 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2680 2681 ierr = PetscOptionsGetString(PETSC_NULL,"-matconvert_type",mtype,256,&flg);CHKERRQ(ierr); 2682 if (flg) { 2683 newtype = mtype; 2684 } 2685 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2686 2687 ierr = PetscTypeCompare((PetscObject)mat,newtype,&sametype);CHKERRQ(ierr); 2688 ierr = PetscStrcmp(newtype,"same",&issame);CHKERRQ(ierr); 2689 if ((sametype || issame) && mat->ops->duplicate) { 2690 ierr = (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);CHKERRQ(ierr); 2691 } else { 2692 PetscErrorCode (*conv)(Mat,const MatType,Mat*)=PETSC_NULL; 2693 /* 2694 Order of precedence: 2695 1) See if a specialized converter is known to the current matrix. 2696 2) See if a specialized converter is known to the desired matrix class. 2697 3) See if a good general converter is registered for the desired class 2698 (as of 6/27/03 only MATMPIADJ falls into this category). 2699 4) See if a good general converter is known for the current matrix. 2700 5) Use a really basic converter. 2701 */ 2702 ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); 2703 ierr = PetscStrcat(convname,mat->type_name);CHKERRQ(ierr); 2704 ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); 2705 ierr = PetscStrcat(convname,newtype);CHKERRQ(ierr); 2706 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 2707 ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); 2708 if (!conv) { 2709 Mat B; 2710 ierr = MatCreate(mat->comm,0,0,0,0,&B);CHKERRQ(ierr); 2711 ierr = MatSetType(B,newtype);CHKERRQ(ierr); 2712 ierr = PetscObjectQueryFunction((PetscObject)B,convname,(void (**)(void))&conv);CHKERRQ(ierr); 2713 ierr = MatDestroy(B);CHKERRQ(ierr); 2714 if (!conv) { 2715 if (!MatConvertRegisterAllCalled) { 2716 ierr = MatConvertRegisterAll(PETSC_NULL);CHKERRQ(ierr); 2717 } 2718 ierr = PetscFListFind(mat->comm,MatConvertList,newtype,(void(**)(void))&conv);CHKERRQ(ierr); 2719 if (!conv) { 2720 if (mat->ops->convert) { 2721 conv = mat->ops->convert; 2722 } else { 2723 conv = MatConvert_Basic; 2724 } 2725 } 2726 } 2727 } 2728 ierr = (*conv)(mat,newtype,M);CHKERRQ(ierr); 2729 } 2730 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2731 ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); 2732 PetscFunctionReturn(0); 2733 } 2734 2735 2736 #undef __FUNCT__ 2737 #define __FUNCT__ "MatDuplicate" 2738 /*@C 2739 MatDuplicate - Duplicates a matrix including the non-zero structure. 2740 2741 Collective on Mat 2742 2743 Input Parameters: 2744 + mat - the matrix 2745 - op - either MAT_DO_NOT_COPY_VALUES or MAT_COPY_VALUES, cause it to copy nonzero 2746 values as well or not 2747 2748 Output Parameter: 2749 . M - pointer to place new matrix 2750 2751 Level: intermediate 2752 2753 Concepts: matrices^duplicating 2754 2755 .seealso: MatCopy(), MatConvert() 2756 @*/ 2757 PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M) 2758 { 2759 PetscErrorCode ierr; 2760 2761 PetscFunctionBegin; 2762 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2763 PetscValidType(mat,1); 2764 MatPreallocated(mat); 2765 PetscValidPointer(M,3); 2766 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2767 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2768 2769 *M = 0; 2770 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2771 if (!mat->ops->duplicate) { 2772 SETERRQ(PETSC_ERR_SUP,"Not written for this matrix type"); 2773 } 2774 ierr = (*mat->ops->duplicate)(mat,op,M);CHKERRQ(ierr); 2775 if (mat->mapping) { 2776 ierr = MatSetLocalToGlobalMapping(*M,mat->mapping);CHKERRQ(ierr); 2777 } 2778 if (mat->bmapping) { 2779 ierr = MatSetLocalToGlobalMappingBlock(*M,mat->mapping);CHKERRQ(ierr); 2780 } 2781 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2782 ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); 2783 PetscFunctionReturn(0); 2784 } 2785 2786 #undef __FUNCT__ 2787 #define __FUNCT__ "MatGetDiagonal" 2788 /*@ 2789 MatGetDiagonal - Gets the diagonal of a matrix. 2790 2791 Collective on Mat and Vec 2792 2793 Input Parameters: 2794 + mat - the matrix 2795 - v - the vector for storing the diagonal 2796 2797 Output Parameter: 2798 . v - the diagonal of the matrix 2799 2800 Notes: 2801 For the SeqAIJ matrix format, this routine may also be called 2802 on a LU factored matrix; in that case it routines the reciprocal of 2803 the diagonal entries in U. It returns the entries permuted by the 2804 row and column permutation used during the symbolic factorization. 2805 2806 Level: intermediate 2807 2808 Concepts: matrices^accessing diagonals 2809 2810 .seealso: MatGetRow(), MatGetSubmatrices(), MatGetSubmatrix(), MatGetRowMax() 2811 @*/ 2812 PetscErrorCode MatGetDiagonal(Mat mat,Vec v) 2813 { 2814 PetscErrorCode ierr; 2815 2816 PetscFunctionBegin; 2817 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2818 PetscValidType(mat,1); 2819 MatPreallocated(mat); 2820 PetscValidHeaderSpecific(v,VEC_COOKIE,2); 2821 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2822 if (!mat->ops->getdiagonal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2823 2824 ierr = (*mat->ops->getdiagonal)(mat,v);CHKERRQ(ierr); 2825 ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); 2826 PetscFunctionReturn(0); 2827 } 2828 2829 #undef __FUNCT__ 2830 #define __FUNCT__ "MatGetRowMax" 2831 /*@ 2832 MatGetRowMax - Gets the maximum value (in absolute value) of each 2833 row of the matrix 2834 2835 Collective on Mat and Vec 2836 2837 Input Parameters: 2838 . mat - the matrix 2839 2840 Output Parameter: 2841 . v - the vector for storing the maximums 2842 2843 Level: intermediate 2844 2845 Concepts: matrices^getting row maximums 2846 2847 .seealso: MatGetDiagonal(), MatGetSubmatrices(), MatGetSubmatrix() 2848 @*/ 2849 PetscErrorCode MatGetRowMax(Mat mat,Vec v) 2850 { 2851 PetscErrorCode ierr; 2852 2853 PetscFunctionBegin; 2854 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2855 PetscValidType(mat,1); 2856 MatPreallocated(mat); 2857 PetscValidHeaderSpecific(v,VEC_COOKIE,2); 2858 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2859 if (!mat->ops->getrowmax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2860 2861 ierr = (*mat->ops->getrowmax)(mat,v);CHKERRQ(ierr); 2862 ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); 2863 PetscFunctionReturn(0); 2864 } 2865 2866 #undef __FUNCT__ 2867 #define __FUNCT__ "MatTranspose" 2868 /*@C 2869 MatTranspose - Computes an in-place or out-of-place transpose of a matrix. 2870 2871 Collective on Mat 2872 2873 Input Parameter: 2874 . mat - the matrix to transpose 2875 2876 Output Parameters: 2877 . B - the transpose (or pass in PETSC_NULL for an in-place transpose) 2878 2879 Level: intermediate 2880 2881 Concepts: matrices^transposing 2882 2883 .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose() 2884 @*/ 2885 PetscErrorCode MatTranspose(Mat mat,Mat *B) 2886 { 2887 PetscErrorCode ierr; 2888 2889 PetscFunctionBegin; 2890 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2891 PetscValidType(mat,1); 2892 MatPreallocated(mat); 2893 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2894 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2895 if (!mat->ops->transpose) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2896 2897 ierr = PetscLogEventBegin(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 2898 ierr = (*mat->ops->transpose)(mat,B);CHKERRQ(ierr); 2899 ierr = PetscLogEventEnd(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 2900 if (B) {ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr);} 2901 PetscFunctionReturn(0); 2902 } 2903 2904 #undef __FUNCT__ 2905 #define __FUNCT__ "MatIsTranspose" 2906 /*@C 2907 MatIsTranspose - Test whether a matrix is another one's transpose, 2908 or its own, in which case it tests symmetry. 2909 2910 Collective on Mat 2911 2912 Input Parameter: 2913 + A - the matrix to test 2914 - B - the matrix to test against, this can equal the first parameter 2915 2916 Output Parameters: 2917 . flg - the result 2918 2919 Notes: 2920 Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm 2921 has a running time of the order of the number of nonzeros; the parallel 2922 test involves parallel copies of the block-offdiagonal parts of the matrix. 2923 2924 Level: intermediate 2925 2926 Concepts: matrices^transposing, matrix^symmetry 2927 2928 .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian() 2929 @*/ 2930 PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscTruth *flg) 2931 { 2932 PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscTruth*),(*g)(Mat,Mat,PetscReal,PetscTruth*); 2933 2934 PetscFunctionBegin; 2935 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 2936 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 2937 PetscValidPointer(flg,3); 2938 ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",(void (**)(void))&f);CHKERRQ(ierr); 2939 ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",(void (**)(void))&g);CHKERRQ(ierr); 2940 if (f && g) { 2941 if (f==g) { 2942 ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); 2943 } else { 2944 SETERRQ(PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test"); 2945 } 2946 } 2947 PetscFunctionReturn(0); 2948 } 2949 2950 #undef __FUNCT__ 2951 #define __FUNCT__ "MatPermute" 2952 /*@C 2953 MatPermute - Creates a new matrix with rows and columns permuted from the 2954 original. 2955 2956 Collective on Mat 2957 2958 Input Parameters: 2959 + mat - the matrix to permute 2960 . row - row permutation, each processor supplies only the permutation for its rows 2961 - col - column permutation, each processor needs the entire column permutation, that is 2962 this is the same size as the total number of columns in the matrix 2963 2964 Output Parameters: 2965 . B - the permuted matrix 2966 2967 Level: advanced 2968 2969 Concepts: matrices^permuting 2970 2971 .seealso: MatGetOrdering() 2972 @*/ 2973 PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B) 2974 { 2975 PetscErrorCode ierr; 2976 2977 PetscFunctionBegin; 2978 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2979 PetscValidType(mat,1); 2980 MatPreallocated(mat); 2981 PetscValidHeaderSpecific(row,IS_COOKIE,2); 2982 PetscValidHeaderSpecific(col,IS_COOKIE,3); 2983 PetscValidPointer(B,4); 2984 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2985 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2986 if (!mat->ops->permute) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2987 ierr = (*mat->ops->permute)(mat,row,col,B);CHKERRQ(ierr); 2988 ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr); 2989 PetscFunctionReturn(0); 2990 } 2991 2992 #undef __FUNCT__ 2993 #define __FUNCT__ "MatPermuteSparsify" 2994 /*@C 2995 MatPermuteSparsify - Creates a new matrix with rows and columns permuted from the 2996 original and sparsified to the prescribed tolerance. 2997 2998 Collective on Mat 2999 3000 Input Parameters: 3001 + A - The matrix to permute 3002 . band - The half-bandwidth of the sparsified matrix, or PETSC_DECIDE 3003 . frac - The half-bandwidth as a fraction of the total size, or 0.0 3004 . tol - The drop tolerance 3005 . rowp - The row permutation 3006 - colp - The column permutation 3007 3008 Output Parameter: 3009 . B - The permuted, sparsified matrix 3010 3011 Level: advanced 3012 3013 Note: 3014 The default behavior (band = PETSC_DECIDE and frac = 0.0) is to 3015 restrict the half-bandwidth of the resulting matrix to 5% of the 3016 total matrix size. 3017 3018 .keywords: matrix, permute, sparsify 3019 3020 .seealso: MatGetOrdering(), MatPermute() 3021 @*/ 3022 PetscErrorCode MatPermuteSparsify(Mat A, PetscInt band, PetscReal frac, PetscReal tol, IS rowp, IS colp, Mat *B) 3023 { 3024 IS irowp, icolp; 3025 PetscInt *rows, *cols; 3026 PetscInt M, N, locRowStart, locRowEnd; 3027 PetscInt nz, newNz; 3028 const PetscInt *cwork; 3029 PetscInt *cnew; 3030 const PetscScalar *vwork; 3031 PetscScalar *vnew; 3032 PetscInt bw, issize; 3033 PetscInt row, locRow, newRow, col, newCol; 3034 PetscErrorCode ierr; 3035 3036 PetscFunctionBegin; 3037 PetscValidHeaderSpecific(A, MAT_COOKIE,1); 3038 PetscValidHeaderSpecific(rowp, IS_COOKIE,5); 3039 PetscValidHeaderSpecific(colp, IS_COOKIE,6); 3040 PetscValidPointer(B,7); 3041 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3042 if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3043 if (!A->ops->permutesparsify) { 3044 ierr = MatGetSize(A, &M, &N);CHKERRQ(ierr); 3045 ierr = MatGetOwnershipRange(A, &locRowStart, &locRowEnd);CHKERRQ(ierr); 3046 ierr = ISGetSize(rowp, &issize);CHKERRQ(ierr); 3047 if (issize != M) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %D for row permutation, should be %D", issize, M); 3048 ierr = ISGetSize(colp, &issize);CHKERRQ(ierr); 3049 if (issize != N) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %D for column permutation, should be %D", issize, N); 3050 ierr = ISInvertPermutation(rowp, 0, &irowp);CHKERRQ(ierr); 3051 ierr = ISGetIndices(irowp, &rows);CHKERRQ(ierr); 3052 ierr = ISInvertPermutation(colp, 0, &icolp);CHKERRQ(ierr); 3053 ierr = ISGetIndices(icolp, &cols);CHKERRQ(ierr); 3054 ierr = PetscMalloc(N * sizeof(PetscInt), &cnew);CHKERRQ(ierr); 3055 ierr = PetscMalloc(N * sizeof(PetscScalar), &vnew);CHKERRQ(ierr); 3056 3057 /* Setup bandwidth to include */ 3058 if (band == PETSC_DECIDE) { 3059 if (frac <= 0.0) 3060 bw = (PetscInt) (M * 0.05); 3061 else 3062 bw = (PetscInt) (M * frac); 3063 } else { 3064 if (band <= 0) SETERRQ(PETSC_ERR_ARG_WRONG, "Bandwidth must be a positive integer"); 3065 bw = band; 3066 } 3067 3068 /* Put values into new matrix */ 3069 ierr = MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, B);CHKERRQ(ierr); 3070 for(row = locRowStart, locRow = 0; row < locRowEnd; row++, locRow++) { 3071 ierr = MatGetRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); 3072 newRow = rows[locRow]+locRowStart; 3073 for(col = 0, newNz = 0; col < nz; col++) { 3074 newCol = cols[cwork[col]]; 3075 if ((newCol >= newRow - bw) && (newCol < newRow + bw) && (PetscAbsScalar(vwork[col]) >= tol)) { 3076 cnew[newNz] = newCol; 3077 vnew[newNz] = vwork[col]; 3078 newNz++; 3079 } 3080 } 3081 ierr = MatSetValues(*B, 1, &newRow, newNz, cnew, vnew, INSERT_VALUES);CHKERRQ(ierr); 3082 ierr = MatRestoreRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); 3083 } 3084 ierr = PetscFree(cnew);CHKERRQ(ierr); 3085 ierr = PetscFree(vnew);CHKERRQ(ierr); 3086 ierr = MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3087 ierr = MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3088 ierr = ISRestoreIndices(irowp, &rows);CHKERRQ(ierr); 3089 ierr = ISRestoreIndices(icolp, &cols);CHKERRQ(ierr); 3090 ierr = ISDestroy(irowp);CHKERRQ(ierr); 3091 ierr = ISDestroy(icolp);CHKERRQ(ierr); 3092 } else { 3093 ierr = (*A->ops->permutesparsify)(A, band, frac, tol, rowp, colp, B);CHKERRQ(ierr); 3094 } 3095 ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr); 3096 PetscFunctionReturn(0); 3097 } 3098 3099 #undef __FUNCT__ 3100 #define __FUNCT__ "MatEqual" 3101 /*@ 3102 MatEqual - Compares two matrices. 3103 3104 Collective on Mat 3105 3106 Input Parameters: 3107 + A - the first matrix 3108 - B - the second matrix 3109 3110 Output Parameter: 3111 . flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise. 3112 3113 Level: intermediate 3114 3115 Concepts: matrices^equality between 3116 @*/ 3117 PetscErrorCode MatEqual(Mat A,Mat B,PetscTruth *flg) 3118 { 3119 PetscErrorCode ierr; 3120 3121 PetscFunctionBegin; 3122 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 3123 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 3124 PetscValidType(A,1); 3125 MatPreallocated(A); 3126 PetscValidType(B,2); 3127 MatPreallocated(B); 3128 PetscValidIntPointer(flg,3); 3129 PetscCheckSameComm(A,1,B,2); 3130 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3131 if (!B->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3132 if (A->M != B->M || A->N != B->N) SETERRQ4(PETSC_ERR_ARG_SIZ,"Mat A,Mat B: global dim %D %D %D %D",A->M,B->M,A->N,B->N); 3133 if (!A->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",A->type_name); 3134 if (!B->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",B->type_name); 3135 if (A->ops->equal != B->ops->equal) SETERRQ2(PETSC_ERR_ARG_INCOMP,"A is type: %s\nB is type: %s",A->type_name,B->type_name); 3136 ierr = (*A->ops->equal)(A,B,flg);CHKERRQ(ierr); 3137 PetscFunctionReturn(0); 3138 } 3139 3140 #undef __FUNCT__ 3141 #define __FUNCT__ "MatDiagonalScale" 3142 /*@ 3143 MatDiagonalScale - Scales a matrix on the left and right by diagonal 3144 matrices that are stored as vectors. Either of the two scaling 3145 matrices can be PETSC_NULL. 3146 3147 Collective on Mat 3148 3149 Input Parameters: 3150 + mat - the matrix to be scaled 3151 . l - the left scaling vector (or PETSC_NULL) 3152 - r - the right scaling vector (or PETSC_NULL) 3153 3154 Notes: 3155 MatDiagonalScale() computes A = LAR, where 3156 L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector) 3157 3158 Level: intermediate 3159 3160 Concepts: matrices^diagonal scaling 3161 Concepts: diagonal scaling of matrices 3162 3163 .seealso: MatScale() 3164 @*/ 3165 PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r) 3166 { 3167 PetscErrorCode ierr; 3168 3169 PetscFunctionBegin; 3170 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3171 PetscValidType(mat,1); 3172 MatPreallocated(mat); 3173 if (!mat->ops->diagonalscale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3174 if (l) {PetscValidHeaderSpecific(l,VEC_COOKIE,2);PetscCheckSameComm(mat,1,l,2);} 3175 if (r) {PetscValidHeaderSpecific(r,VEC_COOKIE,3);PetscCheckSameComm(mat,1,r,3);} 3176 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3177 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3178 3179 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3180 ierr = (*mat->ops->diagonalscale)(mat,l,r);CHKERRQ(ierr); 3181 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3182 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3183 PetscFunctionReturn(0); 3184 } 3185 3186 #undef __FUNCT__ 3187 #define __FUNCT__ "MatScale" 3188 /*@ 3189 MatScale - Scales all elements of a matrix by a given number. 3190 3191 Collective on Mat 3192 3193 Input Parameters: 3194 + mat - the matrix to be scaled 3195 - a - the scaling value 3196 3197 Output Parameter: 3198 . mat - the scaled matrix 3199 3200 Level: intermediate 3201 3202 Concepts: matrices^scaling all entries 3203 3204 .seealso: MatDiagonalScale() 3205 @*/ 3206 PetscErrorCode MatScale(const PetscScalar *a,Mat mat) 3207 { 3208 PetscErrorCode ierr; 3209 3210 PetscFunctionBegin; 3211 PetscValidScalarPointer(a,1); 3212 PetscValidHeaderSpecific(mat,MAT_COOKIE,2); 3213 PetscValidType(mat,2); 3214 MatPreallocated(mat); 3215 if (!mat->ops->scale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3216 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3217 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3218 3219 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3220 ierr = (*mat->ops->scale)(a,mat);CHKERRQ(ierr); 3221 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3222 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3223 PetscFunctionReturn(0); 3224 } 3225 3226 #undef __FUNCT__ 3227 #define __FUNCT__ "MatNorm" 3228 /*@ 3229 MatNorm - Calculates various norms of a matrix. 3230 3231 Collective on Mat 3232 3233 Input Parameters: 3234 + mat - the matrix 3235 - type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY 3236 3237 Output Parameters: 3238 . nrm - the resulting norm 3239 3240 Level: intermediate 3241 3242 Concepts: matrices^norm 3243 Concepts: norm^of matrix 3244 @*/ 3245 PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm) 3246 { 3247 PetscErrorCode ierr; 3248 3249 PetscFunctionBegin; 3250 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3251 PetscValidType(mat,1); 3252 MatPreallocated(mat); 3253 PetscValidScalarPointer(nrm,3); 3254 3255 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3256 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3257 if (!mat->ops->norm) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3258 ierr = (*mat->ops->norm)(mat,type,nrm);CHKERRQ(ierr); 3259 PetscFunctionReturn(0); 3260 } 3261 3262 /* 3263 This variable is used to prevent counting of MatAssemblyBegin() that 3264 are called from within a MatAssemblyEnd(). 3265 */ 3266 static PetscInt MatAssemblyEnd_InUse = 0; 3267 #undef __FUNCT__ 3268 #define __FUNCT__ "MatAssemblyBegin" 3269 /*@ 3270 MatAssemblyBegin - Begins assembling the matrix. This routine should 3271 be called after completing all calls to MatSetValues(). 3272 3273 Collective on Mat 3274 3275 Input Parameters: 3276 + mat - the matrix 3277 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 3278 3279 Notes: 3280 MatSetValues() generally caches the values. The matrix is ready to 3281 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 3282 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 3283 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 3284 using the matrix. 3285 3286 Level: beginner 3287 3288 Concepts: matrices^assembling 3289 3290 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled() 3291 @*/ 3292 PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type) 3293 { 3294 PetscErrorCode ierr; 3295 3296 PetscFunctionBegin; 3297 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3298 PetscValidType(mat,1); 3299 MatPreallocated(mat); 3300 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?"); 3301 if (mat->assembled) { 3302 mat->was_assembled = PETSC_TRUE; 3303 mat->assembled = PETSC_FALSE; 3304 } 3305 if (!MatAssemblyEnd_InUse) { 3306 ierr = PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 3307 if (mat->ops->assemblybegin){ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} 3308 ierr = PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 3309 } else { 3310 if (mat->ops->assemblybegin){ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} 3311 } 3312 PetscFunctionReturn(0); 3313 } 3314 3315 #undef __FUNCT__ 3316 #define __FUNCT__ "MatAssembed" 3317 /*@ 3318 MatAssembled - Indicates if a matrix has been assembled and is ready for 3319 use; for example, in matrix-vector product. 3320 3321 Collective on Mat 3322 3323 Input Parameter: 3324 . mat - the matrix 3325 3326 Output Parameter: 3327 . assembled - PETSC_TRUE or PETSC_FALSE 3328 3329 Level: advanced 3330 3331 Concepts: matrices^assembled? 3332 3333 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin() 3334 @*/ 3335 PetscErrorCode MatAssembled(Mat mat,PetscTruth *assembled) 3336 { 3337 PetscFunctionBegin; 3338 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3339 PetscValidType(mat,1); 3340 MatPreallocated(mat); 3341 PetscValidPointer(assembled,2); 3342 *assembled = mat->assembled; 3343 PetscFunctionReturn(0); 3344 } 3345 3346 #undef __FUNCT__ 3347 #define __FUNCT__ "MatView_Private" 3348 /* 3349 Processes command line options to determine if/how a matrix 3350 is to be viewed. Called by MatAssemblyEnd() and MatLoad(). 3351 */ 3352 PetscErrorCode MatView_Private(Mat mat) 3353 { 3354 PetscErrorCode ierr; 3355 PetscTruth flg; 3356 static PetscTruth incall = PETSC_FALSE; 3357 3358 PetscFunctionBegin; 3359 if (incall) PetscFunctionReturn(0); 3360 incall = PETSC_TRUE; 3361 ierr = PetscOptionsBegin(mat->comm,mat->prefix,"Matrix Options","Mat");CHKERRQ(ierr); 3362 ierr = PetscOptionsName("-mat_view_info","Information on matrix size","MatView",&flg);CHKERRQ(ierr); 3363 if (flg) { 3364 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr); 3365 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3366 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3367 } 3368 ierr = PetscOptionsName("-mat_view_info_detailed","Nonzeros in the matrix","MatView",&flg);CHKERRQ(ierr); 3369 if (flg) { 3370 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO_DETAIL);CHKERRQ(ierr); 3371 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3372 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3373 } 3374 ierr = PetscOptionsName("-mat_view","Print matrix to stdout","MatView",&flg);CHKERRQ(ierr); 3375 if (flg) { 3376 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3377 } 3378 ierr = PetscOptionsName("-mat_view_matlab","Print matrix to stdout in a format Matlab can read","MatView",&flg);CHKERRQ(ierr); 3379 if (flg) { 3380 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_MATLAB);CHKERRQ(ierr); 3381 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3382 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3383 } 3384 ierr = PetscOptionsName("-mat_view_socket","Send matrix to socket (can be read from matlab)","MatView",&flg);CHKERRQ(ierr); 3385 if (flg) { 3386 ierr = MatView(mat,PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); 3387 ierr = PetscViewerFlush(PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); 3388 } 3389 ierr = PetscOptionsName("-mat_view_binary","Save matrix to file in binary format","MatView",&flg);CHKERRQ(ierr); 3390 if (flg) { 3391 ierr = MatView(mat,PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); 3392 ierr = PetscViewerFlush(PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); 3393 } 3394 ierr = PetscOptionsEnd();CHKERRQ(ierr); 3395 /* cannot have inside PetscOptionsBegin() because uses PetscOptionsBegin() */ 3396 ierr = PetscOptionsHasName(mat->prefix,"-mat_view_draw",&flg);CHKERRQ(ierr); 3397 if (flg) { 3398 ierr = PetscOptionsHasName(mat->prefix,"-mat_view_contour",&flg);CHKERRQ(ierr); 3399 if (flg) { 3400 PetscViewerPushFormat(PETSC_VIEWER_DRAW_(mat->comm),PETSC_VIEWER_DRAW_CONTOUR);CHKERRQ(ierr); 3401 } 3402 ierr = MatView(mat,PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3403 ierr = PetscViewerFlush(PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3404 if (flg) { 3405 PetscViewerPopFormat(PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3406 } 3407 } 3408 incall = PETSC_FALSE; 3409 PetscFunctionReturn(0); 3410 } 3411 3412 #undef __FUNCT__ 3413 #define __FUNCT__ "MatAssemblyEnd" 3414 /*@ 3415 MatAssemblyEnd - Completes assembling the matrix. This routine should 3416 be called after MatAssemblyBegin(). 3417 3418 Collective on Mat 3419 3420 Input Parameters: 3421 + mat - the matrix 3422 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 3423 3424 Options Database Keys: 3425 + -mat_view_info - Prints info on matrix at conclusion of MatEndAssembly() 3426 . -mat_view_info_detailed - Prints more detailed info 3427 . -mat_view - Prints matrix in ASCII format 3428 . -mat_view_matlab - Prints matrix in Matlab format 3429 . -mat_view_draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). 3430 . -display <name> - Sets display name (default is host) 3431 . -draw_pause <sec> - Sets number of seconds to pause after display 3432 . -mat_view_socket - Sends matrix to socket, can be accessed from Matlab (see users manual) 3433 . -viewer_socket_machine <machine> 3434 . -viewer_socket_port <port> 3435 . -mat_view_binary - save matrix to file in binary format 3436 - -viewer_binary_filename <name> 3437 3438 Notes: 3439 MatSetValues() generally caches the values. The matrix is ready to 3440 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 3441 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 3442 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 3443 using the matrix. 3444 3445 Level: beginner 3446 3447 .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), MatView(), MatAssembled(), PetscViewerSocketOpen() 3448 @*/ 3449 PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type) 3450 { 3451 PetscErrorCode ierr; 3452 static PetscInt inassm = 0; 3453 PetscTruth flg; 3454 3455 PetscFunctionBegin; 3456 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3457 PetscValidType(mat,1); 3458 MatPreallocated(mat); 3459 3460 inassm++; 3461 MatAssemblyEnd_InUse++; 3462 if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */ 3463 ierr = PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 3464 if (mat->ops->assemblyend) { 3465 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 3466 } 3467 ierr = PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 3468 } else { 3469 if (mat->ops->assemblyend) { 3470 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 3471 } 3472 } 3473 3474 /* Flush assembly is not a true assembly */ 3475 if (type != MAT_FLUSH_ASSEMBLY) { 3476 mat->assembled = PETSC_TRUE; mat->num_ass++; 3477 } 3478 mat->insertmode = NOT_SET_VALUES; 3479 MatAssemblyEnd_InUse--; 3480 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3481 if (!mat->symmetric_eternal) { 3482 mat->symmetric_set = PETSC_FALSE; 3483 mat->hermitian_set = PETSC_FALSE; 3484 mat->structurally_symmetric_set = PETSC_FALSE; 3485 } 3486 if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) { 3487 ierr = MatView_Private(mat);CHKERRQ(ierr); 3488 ierr = PetscOptionsHasName(mat->prefix,"-mat_is_symmetric",&flg);CHKERRQ(ierr); 3489 if (flg) { 3490 PetscReal tol = 0.0; 3491 ierr = PetscOptionsGetReal(mat->prefix,"-mat_is_symmetric",&tol,PETSC_NULL);CHKERRQ(ierr); 3492 ierr = MatIsSymmetric(mat,tol,&flg);CHKERRQ(ierr); 3493 if (flg) { 3494 ierr = PetscPrintf(mat->comm,"Matrix is symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); 3495 } else { 3496 ierr = PetscPrintf(mat->comm,"Matrix is not symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); 3497 } 3498 } 3499 } 3500 inassm--; 3501 ierr = PetscOptionsHasName(mat->prefix,"-help",&flg);CHKERRQ(ierr); 3502 if (flg) { 3503 ierr = MatPrintHelp(mat);CHKERRQ(ierr); 3504 } 3505 PetscFunctionReturn(0); 3506 } 3507 3508 3509 #undef __FUNCT__ 3510 #define __FUNCT__ "MatCompress" 3511 /*@ 3512 MatCompress - Tries to store the matrix in as little space as 3513 possible. May fail if memory is already fully used, since it 3514 tries to allocate new space. 3515 3516 Collective on Mat 3517 3518 Input Parameters: 3519 . mat - the matrix 3520 3521 Level: advanced 3522 3523 @*/ 3524 PetscErrorCode MatCompress(Mat mat) 3525 { 3526 PetscErrorCode ierr; 3527 3528 PetscFunctionBegin; 3529 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3530 PetscValidType(mat,1); 3531 MatPreallocated(mat); 3532 if (mat->ops->compress) {ierr = (*mat->ops->compress)(mat);CHKERRQ(ierr);} 3533 PetscFunctionReturn(0); 3534 } 3535 3536 #undef __FUNCT__ 3537 #define __FUNCT__ "MatSetOption" 3538 /*@ 3539 MatSetOption - Sets a parameter option for a matrix. Some options 3540 may be specific to certain storage formats. Some options 3541 determine how values will be inserted (or added). Sorted, 3542 row-oriented input will generally assemble the fastest. The default 3543 is row-oriented, nonsorted input. 3544 3545 Collective on Mat 3546 3547 Input Parameters: 3548 + mat - the matrix 3549 - option - the option, one of those listed below (and possibly others), 3550 e.g., MAT_ROWS_SORTED, MAT_NEW_NONZERO_LOCATION_ERR 3551 3552 Options Describing Matrix Structure: 3553 + MAT_SYMMETRIC - symmetric in terms of both structure and value 3554 . MAT_HERMITIAN - transpose is the complex conjugation 3555 . MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure 3556 . MAT_NOT_SYMMETRIC - not symmetric in value 3557 . MAT_NOT_HERMITIAN - transpose is not the complex conjugation 3558 . MAT_NOT_STRUCTURALLY_SYMMETRIC - not symmetric nonzero structure 3559 . MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag 3560 you set to be kept with all future use of the matrix 3561 including after MatAssemblyBegin/End() which could 3562 potentially change the symmetry structure, i.e. you 3563 KNOW the matrix will ALWAYS have the property you set. 3564 - MAT_NOT_SYMMETRY_ETERNAL - if MatAssemblyBegin/End() is called then the 3565 flags you set will be dropped (in case potentially 3566 the symmetry etc was lost). 3567 3568 Options For Use with MatSetValues(): 3569 Insert a logically dense subblock, which can be 3570 + MAT_ROW_ORIENTED - row-oriented (default) 3571 . MAT_COLUMN_ORIENTED - column-oriented 3572 . MAT_ROWS_SORTED - sorted by row 3573 . MAT_ROWS_UNSORTED - not sorted by row (default) 3574 . MAT_COLUMNS_SORTED - sorted by column 3575 - MAT_COLUMNS_UNSORTED - not sorted by column (default) 3576 3577 Not these options reflect the data you pass in with MatSetValues(); it has 3578 nothing to do with how the data is stored internally in the matrix 3579 data structure. 3580 3581 When (re)assembling a matrix, we can restrict the input for 3582 efficiency/debugging purposes. These options include 3583 + MAT_NO_NEW_NONZERO_LOCATIONS - additional insertions will not be 3584 allowed if they generate a new nonzero 3585 . MAT_YES_NEW_NONZERO_LOCATIONS - additional insertions will be allowed 3586 . MAT_NO_NEW_DIAGONALS - additional insertions will not be allowed if 3587 they generate a nonzero in a new diagonal (for block diagonal format only) 3588 . MAT_YES_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only) 3589 . MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries 3590 . MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry 3591 - MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly 3592 3593 Notes: 3594 Some options are relevant only for particular matrix types and 3595 are thus ignored by others. Other options are not supported by 3596 certain matrix types and will generate an error message if set. 3597 3598 If using a Fortran 77 module to compute a matrix, one may need to 3599 use the column-oriented option (or convert to the row-oriented 3600 format). 3601 3602 MAT_NO_NEW_NONZERO_LOCATIONS indicates that any add or insertion 3603 that would generate a new entry in the nonzero structure is instead 3604 ignored. Thus, if memory has not alredy been allocated for this particular 3605 data, then the insertion is ignored. For dense matrices, in which 3606 the entire array is allocated, no entries are ever ignored. 3607 Set after the first MatAssemblyEnd() 3608 3609 MAT_NEW_NONZERO_LOCATION_ERR indicates that any add or insertion 3610 that would generate a new entry in the nonzero structure instead produces 3611 an error. (Currently supported for AIJ and BAIJ formats only.) 3612 This is a useful flag when using SAME_NONZERO_PATTERN in calling 3613 KSPSetOperators() to ensure that the nonzero pattern truely does 3614 remain unchanged. Set after the first MatAssemblyEnd() 3615 3616 MAT_NEW_NONZERO_ALLOCATION_ERR indicates that any add or insertion 3617 that would generate a new entry that has not been preallocated will 3618 instead produce an error. (Currently supported for AIJ and BAIJ formats 3619 only.) This is a useful flag when debugging matrix memory preallocation. 3620 3621 MAT_IGNORE_OFF_PROC_ENTRIES indicates entries destined for 3622 other processors should be dropped, rather than stashed. 3623 This is useful if you know that the "owning" processor is also 3624 always generating the correct matrix entries, so that PETSc need 3625 not transfer duplicate entries generated on another processor. 3626 3627 MAT_USE_HASH_TABLE indicates that a hash table be used to improve the 3628 searches during matrix assembly. When this flag is set, the hash table 3629 is created during the first Matrix Assembly. This hash table is 3630 used the next time through, during MatSetVaules()/MatSetVaulesBlocked() 3631 to improve the searching of indices. MAT_NO_NEW_NONZERO_LOCATIONS flag 3632 should be used with MAT_USE_HASH_TABLE flag. This option is currently 3633 supported by MATMPIBAIJ format only. 3634 3635 MAT_KEEP_ZEROED_ROWS indicates when MatZeroRows() is called the zeroed entries 3636 are kept in the nonzero structure 3637 3638 MAT_IGNORE_ZERO_ENTRIES - for AIJ matrices this will stop zero values from creating 3639 a zero location in the matrix 3640 3641 MAT_USE_INODES - indicates using inode version of the code - works with AIJ and 3642 ROWBS matrix types 3643 3644 MAT_DO_NOT_USE_INODES - indicates not using inode version of the code - works 3645 with AIJ and ROWBS matrix types 3646 3647 Level: intermediate 3648 3649 Concepts: matrices^setting options 3650 3651 @*/ 3652 PetscErrorCode MatSetOption(Mat mat,MatOption op) 3653 { 3654 PetscErrorCode ierr; 3655 3656 PetscFunctionBegin; 3657 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3658 PetscValidType(mat,1); 3659 MatPreallocated(mat); 3660 switch (op) { 3661 case MAT_SYMMETRIC: 3662 mat->symmetric = PETSC_TRUE; 3663 mat->structurally_symmetric = PETSC_TRUE; 3664 mat->symmetric_set = PETSC_TRUE; 3665 mat->structurally_symmetric_set = PETSC_TRUE; 3666 break; 3667 case MAT_HERMITIAN: 3668 mat->hermitian = PETSC_TRUE; 3669 mat->structurally_symmetric = PETSC_TRUE; 3670 mat->hermitian_set = PETSC_TRUE; 3671 mat->structurally_symmetric_set = PETSC_TRUE; 3672 break; 3673 case MAT_STRUCTURALLY_SYMMETRIC: 3674 mat->structurally_symmetric = PETSC_TRUE; 3675 mat->structurally_symmetric_set = PETSC_TRUE; 3676 break; 3677 case MAT_NOT_SYMMETRIC: 3678 mat->symmetric = PETSC_FALSE; 3679 mat->symmetric_set = PETSC_TRUE; 3680 break; 3681 case MAT_NOT_HERMITIAN: 3682 mat->hermitian = PETSC_FALSE; 3683 mat->hermitian_set = PETSC_TRUE; 3684 break; 3685 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 3686 mat->structurally_symmetric = PETSC_FALSE; 3687 mat->structurally_symmetric_set = PETSC_TRUE; 3688 break; 3689 case MAT_SYMMETRY_ETERNAL: 3690 mat->symmetric_eternal = PETSC_TRUE; 3691 break; 3692 case MAT_NOT_SYMMETRY_ETERNAL: 3693 mat->symmetric_eternal = PETSC_FALSE; 3694 break; 3695 default: 3696 break; 3697 } 3698 if (mat->ops->setoption) { 3699 ierr = (*mat->ops->setoption)(mat,op);CHKERRQ(ierr); 3700 } 3701 PetscFunctionReturn(0); 3702 } 3703 3704 #undef __FUNCT__ 3705 #define __FUNCT__ "MatZeroEntries" 3706 /*@ 3707 MatZeroEntries - Zeros all entries of a matrix. For sparse matrices 3708 this routine retains the old nonzero structure. 3709 3710 Collective on Mat 3711 3712 Input Parameters: 3713 . mat - the matrix 3714 3715 Level: intermediate 3716 3717 Concepts: matrices^zeroing 3718 3719 .seealso: MatZeroRows() 3720 @*/ 3721 PetscErrorCode MatZeroEntries(Mat mat) 3722 { 3723 PetscErrorCode ierr; 3724 3725 PetscFunctionBegin; 3726 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3727 PetscValidType(mat,1); 3728 MatPreallocated(mat); 3729 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3730 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3731 if (!mat->ops->zeroentries) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3732 3733 ierr = PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 3734 ierr = (*mat->ops->zeroentries)(mat);CHKERRQ(ierr); 3735 ierr = PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 3736 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3737 PetscFunctionReturn(0); 3738 } 3739 3740 #undef __FUNCT__ 3741 #define __FUNCT__ "MatZeroRows" 3742 /*@C 3743 MatZeroRows - Zeros all entries (except possibly the main diagonal) 3744 of a set of rows of a matrix. 3745 3746 Collective on Mat 3747 3748 Input Parameters: 3749 + mat - the matrix 3750 . is - index set of rows to remove 3751 - diag - pointer to value put in all diagonals of eliminated rows. 3752 Note that diag is not a pointer to an array, but merely a 3753 pointer to a single value. 3754 3755 Notes: 3756 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 3757 but does not release memory. For the dense and block diagonal 3758 formats this does not alter the nonzero structure. 3759 3760 If the option MatSetOption(mat,MAT_KEEP_ZEROED_ROWS) the nonzero structure 3761 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 3762 merely zeroed. 3763 3764 The user can set a value in the diagonal entry (or for the AIJ and 3765 row formats can optionally remove the main diagonal entry from the 3766 nonzero structure as well, by passing a null pointer (PETSC_NULL 3767 in C or PETSC_NULL_SCALAR in Fortran) as the final argument). 3768 3769 For the parallel case, all processes that share the matrix (i.e., 3770 those in the communicator used for matrix creation) MUST call this 3771 routine, regardless of whether any rows being zeroed are owned by 3772 them. 3773 3774 Level: intermediate 3775 3776 Concepts: matrices^zeroing rows 3777 3778 .seealso: MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 3779 @*/ 3780 PetscErrorCode MatZeroRows(Mat mat,IS is,const PetscScalar *diag) 3781 { 3782 PetscErrorCode ierr; 3783 3784 PetscFunctionBegin; 3785 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3786 PetscValidType(mat,1); 3787 MatPreallocated(mat); 3788 PetscValidHeaderSpecific(is,IS_COOKIE,2); 3789 if (diag) PetscValidScalarPointer(diag,3); 3790 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3791 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3792 if (!mat->ops->zerorows) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3793 3794 ierr = (*mat->ops->zerorows)(mat,is,diag);CHKERRQ(ierr); 3795 ierr = MatView_Private(mat);CHKERRQ(ierr); 3796 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3797 PetscFunctionReturn(0); 3798 } 3799 3800 #undef __FUNCT__ 3801 #define __FUNCT__ "MatZeroRowsLocal" 3802 /*@C 3803 MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal) 3804 of a set of rows of a matrix; using local numbering of rows. 3805 3806 Collective on Mat 3807 3808 Input Parameters: 3809 + mat - the matrix 3810 . is - index set of rows to remove 3811 - diag - pointer to value put in all diagonals of eliminated rows. 3812 Note that diag is not a pointer to an array, but merely a 3813 pointer to a single value. 3814 3815 Notes: 3816 Before calling MatZeroRowsLocal(), the user must first set the 3817 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 3818 3819 For the AIJ matrix formats this removes the old nonzero structure, 3820 but does not release memory. For the dense and block diagonal 3821 formats this does not alter the nonzero structure. 3822 3823 If the option MatSetOption(mat,MAT_KEEP_ZEROED_ROWS) the nonzero structure 3824 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 3825 merely zeroed. 3826 3827 The user can set a value in the diagonal entry (or for the AIJ and 3828 row formats can optionally remove the main diagonal entry from the 3829 nonzero structure as well, by passing a null pointer (PETSC_NULL 3830 in C or PETSC_NULL_SCALAR in Fortran) as the final argument). 3831 3832 Level: intermediate 3833 3834 Concepts: matrices^zeroing 3835 3836 .seealso: MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 3837 @*/ 3838 PetscErrorCode MatZeroRowsLocal(Mat mat,IS is,const PetscScalar *diag) 3839 { 3840 PetscErrorCode ierr; 3841 IS newis; 3842 3843 PetscFunctionBegin; 3844 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3845 PetscValidType(mat,1); 3846 MatPreallocated(mat); 3847 PetscValidHeaderSpecific(is,IS_COOKIE,2); 3848 if (diag) PetscValidScalarPointer(diag,3); 3849 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3850 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3851 3852 if (mat->ops->zerorowslocal) { 3853 ierr = (*mat->ops->zerorowslocal)(mat,is,diag);CHKERRQ(ierr); 3854 } else { 3855 if (!mat->mapping) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); 3856 ierr = ISLocalToGlobalMappingApplyIS(mat->mapping,is,&newis);CHKERRQ(ierr); 3857 ierr = (*mat->ops->zerorows)(mat,newis,diag);CHKERRQ(ierr); 3858 ierr = ISDestroy(newis);CHKERRQ(ierr); 3859 } 3860 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3861 PetscFunctionReturn(0); 3862 } 3863 3864 #undef __FUNCT__ 3865 #define __FUNCT__ "MatGetSize" 3866 /*@ 3867 MatGetSize - Returns the numbers of rows and columns in a matrix. 3868 3869 Not Collective 3870 3871 Input Parameter: 3872 . mat - the matrix 3873 3874 Output Parameters: 3875 + m - the number of global rows 3876 - n - the number of global columns 3877 3878 Note: both output parameters can be PETSC_NULL on input. 3879 3880 Level: beginner 3881 3882 Concepts: matrices^size 3883 3884 .seealso: MatGetLocalSize() 3885 @*/ 3886 PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt* n) 3887 { 3888 PetscFunctionBegin; 3889 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3890 if (m) *m = mat->M; 3891 if (n) *n = mat->N; 3892 PetscFunctionReturn(0); 3893 } 3894 3895 #undef __FUNCT__ 3896 #define __FUNCT__ "MatGetLocalSize" 3897 /*@ 3898 MatGetLocalSize - Returns the number of rows and columns in a matrix 3899 stored locally. This information may be implementation dependent, so 3900 use with care. 3901 3902 Not Collective 3903 3904 Input Parameters: 3905 . mat - the matrix 3906 3907 Output Parameters: 3908 + m - the number of local rows 3909 - n - the number of local columns 3910 3911 Note: both output parameters can be PETSC_NULL on input. 3912 3913 Level: beginner 3914 3915 Concepts: matrices^local size 3916 3917 .seealso: MatGetSize() 3918 @*/ 3919 PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt* n) 3920 { 3921 PetscFunctionBegin; 3922 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3923 if (m) PetscValidIntPointer(m,2); 3924 if (n) PetscValidIntPointer(n,3); 3925 if (m) *m = mat->m; 3926 if (n) *n = mat->n; 3927 PetscFunctionReturn(0); 3928 } 3929 3930 #undef __FUNCT__ 3931 #define __FUNCT__ "MatGetOwnershipRange" 3932 /*@ 3933 MatGetOwnershipRange - Returns the range of matrix rows owned by 3934 this processor, assuming that the matrix is laid out with the first 3935 n1 rows on the first processor, the next n2 rows on the second, etc. 3936 For certain parallel layouts this range may not be well defined. 3937 3938 Not Collective 3939 3940 Input Parameters: 3941 . mat - the matrix 3942 3943 Output Parameters: 3944 + m - the global index of the first local row 3945 - n - one more than the global index of the last local row 3946 3947 Note: both output parameters can be PETSC_NULL on input. 3948 3949 Level: beginner 3950 3951 Concepts: matrices^row ownership 3952 @*/ 3953 PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt* n) 3954 { 3955 PetscErrorCode ierr; 3956 3957 PetscFunctionBegin; 3958 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3959 PetscValidType(mat,1); 3960 MatPreallocated(mat); 3961 if (m) PetscValidIntPointer(m,2); 3962 if (n) PetscValidIntPointer(n,3); 3963 ierr = PetscMapGetLocalRange(mat->rmap,m,n);CHKERRQ(ierr); 3964 PetscFunctionReturn(0); 3965 } 3966 3967 #undef __FUNCT__ 3968 #define __FUNCT__ "MatILUFactorSymbolic" 3969 /*@ 3970 MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix. 3971 Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric() 3972 to complete the factorization. 3973 3974 Collective on Mat 3975 3976 Input Parameters: 3977 + mat - the matrix 3978 . row - row permutation 3979 . column - column permutation 3980 - info - structure containing 3981 $ levels - number of levels of fill. 3982 $ expected fill - as ratio of original fill. 3983 $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices 3984 missing diagonal entries) 3985 3986 Output Parameters: 3987 . fact - new matrix that has been symbolically factored 3988 3989 Notes: 3990 See the users manual for additional information about 3991 choosing the fill factor for better efficiency. 3992 3993 Most users should employ the simplified KSP interface for linear solvers 3994 instead of working directly with matrix algebra routines such as this. 3995 See, e.g., KSPCreate(). 3996 3997 Level: developer 3998 3999 Concepts: matrices^symbolic LU factorization 4000 Concepts: matrices^factorization 4001 Concepts: LU^symbolic factorization 4002 4003 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 4004 MatGetOrdering(), MatFactorInfo 4005 4006 @*/ 4007 PetscErrorCode MatILUFactorSymbolic(Mat mat,IS row,IS col,MatFactorInfo *info,Mat *fact) 4008 { 4009 PetscErrorCode ierr; 4010 4011 PetscFunctionBegin; 4012 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4013 PetscValidType(mat,1); 4014 MatPreallocated(mat); 4015 PetscValidHeaderSpecific(row,IS_COOKIE,2); 4016 PetscValidHeaderSpecific(col,IS_COOKIE,3); 4017 PetscValidPointer(info,4); 4018 PetscValidPointer(fact,5); 4019 if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %D",(PetscInt)info->levels); 4020 if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); 4021 if (!mat->ops->ilufactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ILU",mat->type_name); 4022 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4023 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4024 4025 ierr = PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 4026 ierr = (*mat->ops->ilufactorsymbolic)(mat,row,col,info,fact);CHKERRQ(ierr); 4027 ierr = PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 4028 PetscFunctionReturn(0); 4029 } 4030 4031 #undef __FUNCT__ 4032 #define __FUNCT__ "MatICCFactorSymbolic" 4033 /*@ 4034 MatICCFactorSymbolic - Performs symbolic incomplete 4035 Cholesky factorization for a symmetric matrix. Use 4036 MatCholeskyFactorNumeric() to complete the factorization. 4037 4038 Collective on Mat 4039 4040 Input Parameters: 4041 + mat - the matrix 4042 . perm - row and column permutation 4043 - info - structure containing 4044 $ levels - number of levels of fill. 4045 $ expected fill - as ratio of original fill. 4046 4047 Output Parameter: 4048 . fact - the factored matrix 4049 4050 Notes: 4051 Currently only no-fill factorization is supported. 4052 4053 Most users should employ the simplified KSP interface for linear solvers 4054 instead of working directly with matrix algebra routines such as this. 4055 See, e.g., KSPCreate(). 4056 4057 Level: developer 4058 4059 Concepts: matrices^symbolic incomplete Cholesky factorization 4060 Concepts: matrices^factorization 4061 Concepts: Cholsky^symbolic factorization 4062 4063 .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 4064 @*/ 4065 PetscErrorCode MatICCFactorSymbolic(Mat mat,IS perm,MatFactorInfo *info,Mat *fact) 4066 { 4067 PetscErrorCode ierr; 4068 4069 PetscFunctionBegin; 4070 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4071 PetscValidType(mat,1); 4072 MatPreallocated(mat); 4073 PetscValidHeaderSpecific(perm,IS_COOKIE,2); 4074 PetscValidPointer(info,3); 4075 PetscValidPointer(fact,4); 4076 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4077 if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %D",(PetscInt) info->levels); 4078 if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); 4079 if (!mat->ops->iccfactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ICC",mat->type_name); 4080 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4081 4082 ierr = PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 4083 ierr = (*mat->ops->iccfactorsymbolic)(mat,perm,info,fact);CHKERRQ(ierr); 4084 ierr = PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 4085 PetscFunctionReturn(0); 4086 } 4087 4088 #undef __FUNCT__ 4089 #define __FUNCT__ "MatGetArray" 4090 /*@C 4091 MatGetArray - Returns a pointer to the element values in the matrix. 4092 The result of this routine is dependent on the underlying matrix data 4093 structure, and may not even work for certain matrix types. You MUST 4094 call MatRestoreArray() when you no longer need to access the array. 4095 4096 Not Collective 4097 4098 Input Parameter: 4099 . mat - the matrix 4100 4101 Output Parameter: 4102 . v - the location of the values 4103 4104 4105 Fortran Note: 4106 This routine is used differently from Fortran, e.g., 4107 .vb 4108 Mat mat 4109 PetscScalar mat_array(1) 4110 PetscOffset i_mat 4111 PetscErrorCode ierr 4112 call MatGetArray(mat,mat_array,i_mat,ierr) 4113 4114 C Access first local entry in matrix; note that array is 4115 C treated as one dimensional 4116 value = mat_array(i_mat + 1) 4117 4118 [... other code ...] 4119 call MatRestoreArray(mat,mat_array,i_mat,ierr) 4120 .ve 4121 4122 See the Fortran chapter of the users manual and 4123 petsc/src/mat/examples/tests for details. 4124 4125 Level: advanced 4126 4127 Concepts: matrices^access array 4128 4129 .seealso: MatRestoreArray(), MatGetArrayF90() 4130 @*/ 4131 PetscErrorCode MatGetArray(Mat mat,PetscScalar *v[]) 4132 { 4133 PetscErrorCode ierr; 4134 4135 PetscFunctionBegin; 4136 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4137 PetscValidType(mat,1); 4138 MatPreallocated(mat); 4139 PetscValidPointer(v,2); 4140 if (!mat->ops->getarray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4141 ierr = (*mat->ops->getarray)(mat,v);CHKERRQ(ierr); 4142 PetscFunctionReturn(0); 4143 } 4144 4145 #undef __FUNCT__ 4146 #define __FUNCT__ "MatRestoreArray" 4147 /*@C 4148 MatRestoreArray - Restores the matrix after MatGetArray() has been called. 4149 4150 Not Collective 4151 4152 Input Parameter: 4153 + mat - the matrix 4154 - v - the location of the values 4155 4156 Fortran Note: 4157 This routine is used differently from Fortran, e.g., 4158 .vb 4159 Mat mat 4160 PetscScalar mat_array(1) 4161 PetscOffset i_mat 4162 PetscErrorCode ierr 4163 call MatGetArray(mat,mat_array,i_mat,ierr) 4164 4165 C Access first local entry in matrix; note that array is 4166 C treated as one dimensional 4167 value = mat_array(i_mat + 1) 4168 4169 [... other code ...] 4170 call MatRestoreArray(mat,mat_array,i_mat,ierr) 4171 .ve 4172 4173 See the Fortran chapter of the users manual and 4174 petsc/src/mat/examples/tests for details 4175 4176 Level: advanced 4177 4178 .seealso: MatGetArray(), MatRestoreArrayF90() 4179 @*/ 4180 PetscErrorCode MatRestoreArray(Mat mat,PetscScalar *v[]) 4181 { 4182 PetscErrorCode ierr; 4183 4184 PetscFunctionBegin; 4185 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4186 PetscValidType(mat,1); 4187 MatPreallocated(mat); 4188 PetscValidPointer(v,2); 4189 #if defined(PETSC_USE_BOPT_g) 4190 CHKMEMQ; 4191 #endif 4192 if (!mat->ops->restorearray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4193 ierr = (*mat->ops->restorearray)(mat,v);CHKERRQ(ierr); 4194 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 4195 PetscFunctionReturn(0); 4196 } 4197 4198 #undef __FUNCT__ 4199 #define __FUNCT__ "MatGetSubMatrices" 4200 /*@C 4201 MatGetSubMatrices - Extracts several submatrices from a matrix. If submat 4202 points to an array of valid matrices, they may be reused to store the new 4203 submatrices. 4204 4205 Collective on Mat 4206 4207 Input Parameters: 4208 + mat - the matrix 4209 . n - the number of submatrixes to be extracted (on this processor, may be zero) 4210 . irow, icol - index sets of rows and columns to extract 4211 - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 4212 4213 Output Parameter: 4214 . submat - the array of submatrices 4215 4216 Notes: 4217 MatGetSubMatrices() can extract only sequential submatrices 4218 (from both sequential and parallel matrices). Use MatGetSubMatrix() 4219 to extract a parallel submatrix. 4220 4221 When extracting submatrices from a parallel matrix, each processor can 4222 form a different submatrix by setting the rows and columns of its 4223 individual index sets according to the local submatrix desired. 4224 4225 When finished using the submatrices, the user should destroy 4226 them with MatDestroyMatrices(). 4227 4228 MAT_REUSE_MATRIX can only be used when the nonzero structure of the 4229 original matrix has not changed from that last call to MatGetSubMatrices(). 4230 4231 This routine creates the matrices in submat; you should NOT create them before 4232 calling it. It also allocates the array of matrix pointers submat. 4233 4234 Fortran Note: 4235 The Fortran interface is slightly different from that given below; it 4236 requires one to pass in as submat a Mat (integer) array of size at least m. 4237 4238 Level: advanced 4239 4240 Concepts: matrices^accessing submatrices 4241 Concepts: submatrices 4242 4243 .seealso: MatDestroyMatrices(), MatGetSubMatrix(), MatGetRow(), MatGetDiagonal() 4244 @*/ 4245 PetscErrorCode MatGetSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) 4246 { 4247 PetscErrorCode ierr; 4248 PetscInt i; 4249 PetscTruth eq; 4250 4251 PetscFunctionBegin; 4252 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4253 PetscValidType(mat,1); 4254 MatPreallocated(mat); 4255 if (n) { 4256 PetscValidPointer(irow,3); 4257 PetscValidHeaderSpecific(*irow,IS_COOKIE,3); 4258 PetscValidPointer(icol,4); 4259 PetscValidHeaderSpecific(*icol,IS_COOKIE,4); 4260 } 4261 PetscValidPointer(submat,6); 4262 if (n && scall == MAT_REUSE_MATRIX) { 4263 PetscValidPointer(*submat,6); 4264 PetscValidHeaderSpecific(**submat,MAT_COOKIE,6); 4265 } 4266 if (!mat->ops->getsubmatrices) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4267 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4268 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4269 4270 ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 4271 ierr = (*mat->ops->getsubmatrices)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); 4272 ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 4273 for (i=0; i<n; i++) { 4274 if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) { 4275 ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); 4276 if (eq) { 4277 if (mat->symmetric){ 4278 ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC);CHKERRQ(ierr); 4279 } else if (mat->hermitian) { 4280 ierr = MatSetOption((*submat)[i],MAT_HERMITIAN);CHKERRQ(ierr); 4281 } else if (mat->structurally_symmetric) { 4282 ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC);CHKERRQ(ierr); 4283 } 4284 } 4285 } 4286 } 4287 PetscFunctionReturn(0); 4288 } 4289 4290 #undef __FUNCT__ 4291 #define __FUNCT__ "MatDestroyMatrices" 4292 /*@C 4293 MatDestroyMatrices - Destroys a set of matrices obtained with MatGetSubMatrices(). 4294 4295 Collective on Mat 4296 4297 Input Parameters: 4298 + n - the number of local matrices 4299 - mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling 4300 sequence of MatGetSubMatrices()) 4301 4302 Level: advanced 4303 4304 Notes: Frees not only the matrices, but also the array that contains the matrices 4305 4306 .seealso: MatGetSubMatrices() 4307 @*/ 4308 PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[]) 4309 { 4310 PetscErrorCode ierr; 4311 PetscInt i; 4312 4313 PetscFunctionBegin; 4314 if (n < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %D",n); 4315 PetscValidPointer(mat,2); 4316 for (i=0; i<n; i++) { 4317 ierr = MatDestroy((*mat)[i]);CHKERRQ(ierr); 4318 } 4319 /* memory is allocated even if n = 0 */ 4320 ierr = PetscFree(*mat);CHKERRQ(ierr); 4321 PetscFunctionReturn(0); 4322 } 4323 4324 #undef __FUNCT__ 4325 #define __FUNCT__ "MatIncreaseOverlap" 4326 /*@ 4327 MatIncreaseOverlap - Given a set of submatrices indicated by index sets, 4328 replaces the index sets by larger ones that represent submatrices with 4329 additional overlap. 4330 4331 Collective on Mat 4332 4333 Input Parameters: 4334 + mat - the matrix 4335 . n - the number of index sets 4336 . is - the array of index sets (these index sets will changed during the call) 4337 - ov - the additional overlap requested 4338 4339 Level: developer 4340 4341 Concepts: overlap 4342 Concepts: ASM^computing overlap 4343 4344 .seealso: MatGetSubMatrices() 4345 @*/ 4346 PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov) 4347 { 4348 PetscErrorCode ierr; 4349 4350 PetscFunctionBegin; 4351 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4352 PetscValidType(mat,1); 4353 MatPreallocated(mat); 4354 if (n < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %D",n); 4355 if (n) { 4356 PetscValidPointer(is,3); 4357 PetscValidHeaderSpecific(*is,IS_COOKIE,3); 4358 } 4359 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4360 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4361 4362 if (!ov) PetscFunctionReturn(0); 4363 if (!mat->ops->increaseoverlap) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4364 ierr = PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 4365 ierr = (*mat->ops->increaseoverlap)(mat,n,is,ov);CHKERRQ(ierr); 4366 ierr = PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 4367 PetscFunctionReturn(0); 4368 } 4369 4370 #undef __FUNCT__ 4371 #define __FUNCT__ "MatPrintHelp" 4372 /*@ 4373 MatPrintHelp - Prints all the options for the matrix. 4374 4375 Collective on Mat 4376 4377 Input Parameter: 4378 . mat - the matrix 4379 4380 Options Database Keys: 4381 + -help - Prints matrix options 4382 - -h - Prints matrix options 4383 4384 Level: developer 4385 4386 .seealso: MatCreate(), MatCreateXXX() 4387 @*/ 4388 PetscErrorCode MatPrintHelp(Mat mat) 4389 { 4390 static PetscTruth called = PETSC_FALSE; 4391 PetscErrorCode ierr; 4392 4393 PetscFunctionBegin; 4394 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4395 PetscValidType(mat,1); 4396 MatPreallocated(mat); 4397 4398 if (!called) { 4399 if (mat->ops->printhelp) { 4400 ierr = (*mat->ops->printhelp)(mat);CHKERRQ(ierr); 4401 } 4402 called = PETSC_TRUE; 4403 } 4404 PetscFunctionReturn(0); 4405 } 4406 4407 #undef __FUNCT__ 4408 #define __FUNCT__ "MatGetBlockSize" 4409 /*@ 4410 MatGetBlockSize - Returns the matrix block size; useful especially for the 4411 block row and block diagonal formats. 4412 4413 Not Collective 4414 4415 Input Parameter: 4416 . mat - the matrix 4417 4418 Output Parameter: 4419 . bs - block size 4420 4421 Notes: 4422 Block diagonal formats are MATSEQBDIAG, MATMPIBDIAG. 4423 Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ 4424 4425 Level: intermediate 4426 4427 Concepts: matrices^block size 4428 4429 .seealso: MatCreateSeqBAIJ(), MatCreateMPIBAIJ(), MatCreateSeqBDiag(), MatCreateMPIBDiag() 4430 @*/ 4431 PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs) 4432 { 4433 PetscErrorCode ierr; 4434 4435 PetscFunctionBegin; 4436 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4437 PetscValidType(mat,1); 4438 MatPreallocated(mat); 4439 PetscValidIntPointer(bs,2); 4440 if (!mat->ops->getblocksize) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4441 ierr = (*mat->ops->getblocksize)(mat,bs);CHKERRQ(ierr); 4442 PetscFunctionReturn(0); 4443 } 4444 4445 #undef __FUNCT__ 4446 #define __FUNCT__ "MatGetRowIJ" 4447 /*@C 4448 MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices. 4449 4450 Collective on Mat 4451 4452 Input Parameters: 4453 + mat - the matrix 4454 . shift - 0 or 1 indicating we want the indices starting at 0 or 1 4455 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4456 symmetrized 4457 4458 Output Parameters: 4459 + n - number of rows in the (possibly compressed) matrix 4460 . ia - the row pointers 4461 . ja - the column indices 4462 - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned 4463 4464 Level: developer 4465 4466 .seealso: MatGetColumnIJ(), MatRestoreRowIJ() 4467 @*/ 4468 PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4469 { 4470 PetscErrorCode ierr; 4471 4472 PetscFunctionBegin; 4473 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4474 PetscValidType(mat,1); 4475 MatPreallocated(mat); 4476 PetscValidIntPointer(n,4); 4477 if (ia) PetscValidIntPointer(ia,5); 4478 if (ja) PetscValidIntPointer(ja,6); 4479 PetscValidIntPointer(done,7); 4480 if (!mat->ops->getrowij) *done = PETSC_FALSE; 4481 else { 4482 *done = PETSC_TRUE; 4483 ierr = (*mat->ops->getrowij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4484 } 4485 PetscFunctionReturn(0); 4486 } 4487 4488 #undef __FUNCT__ 4489 #define __FUNCT__ "MatGetColumnIJ" 4490 /*@C 4491 MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices. 4492 4493 Collective on Mat 4494 4495 Input Parameters: 4496 + mat - the matrix 4497 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4498 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4499 symmetrized 4500 4501 Output Parameters: 4502 + n - number of columns in the (possibly compressed) matrix 4503 . ia - the column pointers 4504 . ja - the row indices 4505 - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned 4506 4507 Level: developer 4508 4509 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 4510 @*/ 4511 PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4512 { 4513 PetscErrorCode ierr; 4514 4515 PetscFunctionBegin; 4516 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4517 PetscValidType(mat,1); 4518 MatPreallocated(mat); 4519 PetscValidIntPointer(n,4); 4520 if (ia) PetscValidIntPointer(ia,5); 4521 if (ja) PetscValidIntPointer(ja,6); 4522 PetscValidIntPointer(done,7); 4523 4524 if (!mat->ops->getcolumnij) *done = PETSC_FALSE; 4525 else { 4526 *done = PETSC_TRUE; 4527 ierr = (*mat->ops->getcolumnij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4528 } 4529 PetscFunctionReturn(0); 4530 } 4531 4532 #undef __FUNCT__ 4533 #define __FUNCT__ "MatRestoreRowIJ" 4534 /*@C 4535 MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with 4536 MatGetRowIJ(). 4537 4538 Collective on Mat 4539 4540 Input Parameters: 4541 + mat - the matrix 4542 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4543 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4544 symmetrized 4545 4546 Output Parameters: 4547 + n - size of (possibly compressed) matrix 4548 . ia - the row pointers 4549 . ja - the column indices 4550 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 4551 4552 Level: developer 4553 4554 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 4555 @*/ 4556 PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4557 { 4558 PetscErrorCode ierr; 4559 4560 PetscFunctionBegin; 4561 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4562 PetscValidType(mat,1); 4563 MatPreallocated(mat); 4564 if (ia) PetscValidIntPointer(ia,5); 4565 if (ja) PetscValidIntPointer(ja,6); 4566 PetscValidIntPointer(done,7); 4567 4568 if (!mat->ops->restorerowij) *done = PETSC_FALSE; 4569 else { 4570 *done = PETSC_TRUE; 4571 ierr = (*mat->ops->restorerowij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4572 } 4573 PetscFunctionReturn(0); 4574 } 4575 4576 #undef __FUNCT__ 4577 #define __FUNCT__ "MatRestoreColumnIJ" 4578 /*@C 4579 MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with 4580 MatGetColumnIJ(). 4581 4582 Collective on Mat 4583 4584 Input Parameters: 4585 + mat - the matrix 4586 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4587 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4588 symmetrized 4589 4590 Output Parameters: 4591 + n - size of (possibly compressed) matrix 4592 . ia - the column pointers 4593 . ja - the row indices 4594 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 4595 4596 Level: developer 4597 4598 .seealso: MatGetColumnIJ(), MatRestoreRowIJ() 4599 @*/ 4600 PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4601 { 4602 PetscErrorCode ierr; 4603 4604 PetscFunctionBegin; 4605 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4606 PetscValidType(mat,1); 4607 MatPreallocated(mat); 4608 if (ia) PetscValidIntPointer(ia,5); 4609 if (ja) PetscValidIntPointer(ja,6); 4610 PetscValidIntPointer(done,7); 4611 4612 if (!mat->ops->restorecolumnij) *done = PETSC_FALSE; 4613 else { 4614 *done = PETSC_TRUE; 4615 ierr = (*mat->ops->restorecolumnij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4616 } 4617 PetscFunctionReturn(0); 4618 } 4619 4620 #undef __FUNCT__ 4621 #define __FUNCT__ "MatColoringPatch" 4622 /*@C 4623 MatColoringPatch -Used inside matrix coloring routines that 4624 use MatGetRowIJ() and/or MatGetColumnIJ(). 4625 4626 Collective on Mat 4627 4628 Input Parameters: 4629 + mat - the matrix 4630 . n - number of colors 4631 - colorarray - array indicating color for each column 4632 4633 Output Parameters: 4634 . iscoloring - coloring generated using colorarray information 4635 4636 Level: developer 4637 4638 .seealso: MatGetRowIJ(), MatGetColumnIJ() 4639 4640 @*/ 4641 PetscErrorCode MatColoringPatch(Mat mat,PetscInt n,PetscInt ncolors,ISColoringValue colorarray[],ISColoring *iscoloring) 4642 { 4643 PetscErrorCode ierr; 4644 4645 PetscFunctionBegin; 4646 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4647 PetscValidType(mat,1); 4648 MatPreallocated(mat); 4649 PetscValidIntPointer(colorarray,4); 4650 PetscValidPointer(iscoloring,5); 4651 4652 if (!mat->ops->coloringpatch){ 4653 ierr = ISColoringCreate(mat->comm,n,colorarray,iscoloring);CHKERRQ(ierr); 4654 } else { 4655 ierr = (*mat->ops->coloringpatch)(mat,n,ncolors,colorarray,iscoloring);CHKERRQ(ierr); 4656 } 4657 PetscFunctionReturn(0); 4658 } 4659 4660 4661 #undef __FUNCT__ 4662 #define __FUNCT__ "MatSetUnfactored" 4663 /*@ 4664 MatSetUnfactored - Resets a factored matrix to be treated as unfactored. 4665 4666 Collective on Mat 4667 4668 Input Parameter: 4669 . mat - the factored matrix to be reset 4670 4671 Notes: 4672 This routine should be used only with factored matrices formed by in-place 4673 factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE 4674 format). This option can save memory, for example, when solving nonlinear 4675 systems with a matrix-free Newton-Krylov method and a matrix-based, in-place 4676 ILU(0) preconditioner. 4677 4678 Note that one can specify in-place ILU(0) factorization by calling 4679 .vb 4680 PCType(pc,PCILU); 4681 PCILUSeUseInPlace(pc); 4682 .ve 4683 or by using the options -pc_type ilu -pc_ilu_in_place 4684 4685 In-place factorization ILU(0) can also be used as a local 4686 solver for the blocks within the block Jacobi or additive Schwarz 4687 methods (runtime option: -sub_pc_ilu_in_place). See the discussion 4688 of these preconditioners in the users manual for details on setting 4689 local solver options. 4690 4691 Most users should employ the simplified KSP interface for linear solvers 4692 instead of working directly with matrix algebra routines such as this. 4693 See, e.g., KSPCreate(). 4694 4695 Level: developer 4696 4697 .seealso: PCILUSetUseInPlace(), PCLUSetUseInPlace() 4698 4699 Concepts: matrices^unfactored 4700 4701 @*/ 4702 PetscErrorCode MatSetUnfactored(Mat mat) 4703 { 4704 PetscErrorCode ierr; 4705 4706 PetscFunctionBegin; 4707 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4708 PetscValidType(mat,1); 4709 MatPreallocated(mat); 4710 mat->factor = 0; 4711 if (!mat->ops->setunfactored) PetscFunctionReturn(0); 4712 ierr = (*mat->ops->setunfactored)(mat);CHKERRQ(ierr); 4713 PetscFunctionReturn(0); 4714 } 4715 4716 /*MC 4717 MatGetArrayF90 - Accesses a matrix array from Fortran90. 4718 4719 Synopsis: 4720 MatGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 4721 4722 Not collective 4723 4724 Input Parameter: 4725 . x - matrix 4726 4727 Output Parameters: 4728 + xx_v - the Fortran90 pointer to the array 4729 - ierr - error code 4730 4731 Example of Usage: 4732 .vb 4733 PetscScalar, pointer xx_v(:) 4734 .... 4735 call MatGetArrayF90(x,xx_v,ierr) 4736 a = xx_v(3) 4737 call MatRestoreArrayF90(x,xx_v,ierr) 4738 .ve 4739 4740 Notes: 4741 Not yet supported for all F90 compilers 4742 4743 Level: advanced 4744 4745 .seealso: MatRestoreArrayF90(), MatGetArray(), MatRestoreArray() 4746 4747 Concepts: matrices^accessing array 4748 4749 M*/ 4750 4751 /*MC 4752 MatRestoreArrayF90 - Restores a matrix array that has been 4753 accessed with MatGetArrayF90(). 4754 4755 Synopsis: 4756 MatRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 4757 4758 Not collective 4759 4760 Input Parameters: 4761 + x - matrix 4762 - xx_v - the Fortran90 pointer to the array 4763 4764 Output Parameter: 4765 . ierr - error code 4766 4767 Example of Usage: 4768 .vb 4769 PetscScalar, pointer xx_v(:) 4770 .... 4771 call MatGetArrayF90(x,xx_v,ierr) 4772 a = xx_v(3) 4773 call MatRestoreArrayF90(x,xx_v,ierr) 4774 .ve 4775 4776 Notes: 4777 Not yet supported for all F90 compilers 4778 4779 Level: advanced 4780 4781 .seealso: MatGetArrayF90(), MatGetArray(), MatRestoreArray() 4782 4783 M*/ 4784 4785 4786 #undef __FUNCT__ 4787 #define __FUNCT__ "MatGetSubMatrix" 4788 /*@ 4789 MatGetSubMatrix - Gets a single submatrix on the same number of processors 4790 as the original matrix. 4791 4792 Collective on Mat 4793 4794 Input Parameters: 4795 + mat - the original matrix 4796 . isrow - rows this processor should obtain 4797 . iscol - columns for all processors you wish to keep 4798 . csize - number of columns "local" to this processor (does nothing for sequential 4799 matrices). This should match the result from VecGetLocalSize(x,...) if you 4800 plan to use the matrix in a A*x; alternatively, you can use PETSC_DECIDE 4801 - cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 4802 4803 Output Parameter: 4804 . newmat - the new submatrix, of the same type as the old 4805 4806 Level: advanced 4807 4808 Notes: the iscol argument MUST be the same on each processor. You might be 4809 able to create the iscol argument with ISAllGather(). 4810 4811 The first time this is called you should use a cll of MAT_INITIAL_MATRIX, 4812 the MatGetSubMatrix() routine will create the newmat for you. Any additional calls 4813 to this routine with a mat of the same nonzero structure and with a cll of MAT_REUSE_MATRIX 4814 will reuse the matrix generated the first time. 4815 4816 Concepts: matrices^submatrices 4817 4818 .seealso: MatGetSubMatrices(), ISAllGather() 4819 @*/ 4820 PetscErrorCode MatGetSubMatrix(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse cll,Mat *newmat) 4821 { 4822 PetscErrorCode ierr; 4823 PetscMPIInt size; 4824 Mat *local; 4825 4826 PetscFunctionBegin; 4827 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4828 PetscValidHeaderSpecific(isrow,IS_COOKIE,2); 4829 PetscValidHeaderSpecific(iscol,IS_COOKIE,3); 4830 PetscValidPointer(newmat,6); 4831 if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat,MAT_COOKIE,6); 4832 PetscValidType(mat,1); 4833 MatPreallocated(mat); 4834 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4835 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 4836 4837 /* if original matrix is on just one processor then use submatrix generated */ 4838 if (!mat->ops->getsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) { 4839 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&newmat);CHKERRQ(ierr); 4840 PetscFunctionReturn(0); 4841 } else if (!mat->ops->getsubmatrix && size == 1) { 4842 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 4843 *newmat = *local; 4844 ierr = PetscFree(local);CHKERRQ(ierr); 4845 PetscFunctionReturn(0); 4846 } 4847 4848 if (!mat->ops->getsubmatrix) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4849 ierr = (*mat->ops->getsubmatrix)(mat,isrow,iscol,csize,cll,newmat);CHKERRQ(ierr); 4850 ierr = PetscObjectIncreaseState((PetscObject)*newmat);CHKERRQ(ierr); 4851 PetscFunctionReturn(0); 4852 } 4853 4854 #undef __FUNCT__ 4855 #define __FUNCT__ "MatGetPetscMaps" 4856 /*@C 4857 MatGetPetscMaps - Returns the maps associated with the matrix. 4858 4859 Not Collective 4860 4861 Input Parameter: 4862 . mat - the matrix 4863 4864 Output Parameters: 4865 + rmap - the row (right) map 4866 - cmap - the column (left) map 4867 4868 Level: developer 4869 4870 Concepts: maps^getting from matrix 4871 4872 @*/ 4873 PetscErrorCode MatGetPetscMaps(Mat mat,PetscMap *rmap,PetscMap *cmap) 4874 { 4875 PetscErrorCode ierr; 4876 4877 PetscFunctionBegin; 4878 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4879 PetscValidType(mat,1); 4880 MatPreallocated(mat); 4881 ierr = (*mat->ops->getmaps)(mat,rmap,cmap);CHKERRQ(ierr); 4882 PetscFunctionReturn(0); 4883 } 4884 4885 /* 4886 Version that works for all PETSc matrices 4887 */ 4888 #undef __FUNCT__ 4889 #define __FUNCT__ "MatGetPetscMaps_Petsc" 4890 PetscErrorCode MatGetPetscMaps_Petsc(Mat mat,PetscMap *rmap,PetscMap *cmap) 4891 { 4892 PetscFunctionBegin; 4893 if (rmap) *rmap = mat->rmap; 4894 if (cmap) *cmap = mat->cmap; 4895 PetscFunctionReturn(0); 4896 } 4897 4898 #undef __FUNCT__ 4899 #define __FUNCT__ "MatStashSetInitialSize" 4900 /*@ 4901 MatStashSetInitialSize - sets the sizes of the matrix stash, that is 4902 used during the assembly process to store values that belong to 4903 other processors. 4904 4905 Not Collective 4906 4907 Input Parameters: 4908 + mat - the matrix 4909 . size - the initial size of the stash. 4910 - bsize - the initial size of the block-stash(if used). 4911 4912 Options Database Keys: 4913 + -matstash_initial_size <size> or <size0,size1,...sizep-1> 4914 - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> 4915 4916 Level: intermediate 4917 4918 Notes: 4919 The block-stash is used for values set with VecSetValuesBlocked() while 4920 the stash is used for values set with VecSetValues() 4921 4922 Run with the option -log_info and look for output of the form 4923 MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs. 4924 to determine the appropriate value, MM, to use for size and 4925 MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs. 4926 to determine the value, BMM to use for bsize 4927 4928 Concepts: stash^setting matrix size 4929 Concepts: matrices^stash 4930 4931 @*/ 4932 PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize) 4933 { 4934 PetscErrorCode ierr; 4935 4936 PetscFunctionBegin; 4937 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4938 PetscValidType(mat,1); 4939 MatPreallocated(mat); 4940 ierr = MatStashSetInitialSize_Private(&mat->stash,size);CHKERRQ(ierr); 4941 ierr = MatStashSetInitialSize_Private(&mat->bstash,bsize);CHKERRQ(ierr); 4942 PetscFunctionReturn(0); 4943 } 4944 4945 #undef __FUNCT__ 4946 #define __FUNCT__ "MatInterpolateAdd" 4947 /*@ 4948 MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of 4949 the matrix 4950 4951 Collective on Mat 4952 4953 Input Parameters: 4954 + mat - the matrix 4955 . x,y - the vectors 4956 - w - where the result is stored 4957 4958 Level: intermediate 4959 4960 Notes: 4961 w may be the same vector as y. 4962 4963 This allows one to use either the restriction or interpolation (its transpose) 4964 matrix to do the interpolation 4965 4966 Concepts: interpolation 4967 4968 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 4969 4970 @*/ 4971 PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w) 4972 { 4973 PetscErrorCode ierr; 4974 PetscInt M,N; 4975 4976 PetscFunctionBegin; 4977 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 4978 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 4979 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 4980 PetscValidHeaderSpecific(w,VEC_COOKIE,4); 4981 PetscValidType(A,1); 4982 MatPreallocated(A); 4983 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 4984 if (N > M) { 4985 ierr = MatMultTransposeAdd(A,x,y,w);CHKERRQ(ierr); 4986 } else { 4987 ierr = MatMultAdd(A,x,y,w);CHKERRQ(ierr); 4988 } 4989 PetscFunctionReturn(0); 4990 } 4991 4992 #undef __FUNCT__ 4993 #define __FUNCT__ "MatInterpolate" 4994 /*@ 4995 MatInterpolate - y = A*x or A'*x depending on the shape of 4996 the matrix 4997 4998 Collective on Mat 4999 5000 Input Parameters: 5001 + mat - the matrix 5002 - x,y - the vectors 5003 5004 Level: intermediate 5005 5006 Notes: 5007 This allows one to use either the restriction or interpolation (its transpose) 5008 matrix to do the interpolation 5009 5010 Concepts: matrices^interpolation 5011 5012 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 5013 5014 @*/ 5015 PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y) 5016 { 5017 PetscErrorCode ierr; 5018 PetscInt M,N; 5019 5020 PetscFunctionBegin; 5021 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5022 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 5023 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 5024 PetscValidType(A,1); 5025 MatPreallocated(A); 5026 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 5027 if (N > M) { 5028 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 5029 } else { 5030 ierr = MatMult(A,x,y);CHKERRQ(ierr); 5031 } 5032 PetscFunctionReturn(0); 5033 } 5034 5035 #undef __FUNCT__ 5036 #define __FUNCT__ "MatRestrict" 5037 /*@ 5038 MatRestrict - y = A*x or A'*x 5039 5040 Collective on Mat 5041 5042 Input Parameters: 5043 + mat - the matrix 5044 - x,y - the vectors 5045 5046 Level: intermediate 5047 5048 Notes: 5049 This allows one to use either the restriction or interpolation (its transpose) 5050 matrix to do the restriction 5051 5052 Concepts: matrices^restriction 5053 5054 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate() 5055 5056 @*/ 5057 PetscErrorCode MatRestrict(Mat A,Vec x,Vec y) 5058 { 5059 PetscErrorCode ierr; 5060 PetscInt M,N; 5061 5062 PetscFunctionBegin; 5063 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5064 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 5065 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 5066 PetscValidType(A,1); 5067 MatPreallocated(A); 5068 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 5069 if (N > M) { 5070 ierr = MatMult(A,x,y);CHKERRQ(ierr); 5071 } else { 5072 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 5073 } 5074 PetscFunctionReturn(0); 5075 } 5076 5077 #undef __FUNCT__ 5078 #define __FUNCT__ "MatNullSpaceAttach" 5079 /*@C 5080 MatNullSpaceAttach - attaches a null space to a matrix. 5081 This null space will be removed from the resulting vector whenever 5082 MatMult() is called 5083 5084 Collective on Mat 5085 5086 Input Parameters: 5087 + mat - the matrix 5088 - nullsp - the null space object 5089 5090 Level: developer 5091 5092 Notes: 5093 Overwrites any previous null space that may have been attached 5094 5095 Concepts: null space^attaching to matrix 5096 5097 .seealso: MatCreate(), MatNullSpaceCreate() 5098 @*/ 5099 PetscErrorCode MatNullSpaceAttach(Mat mat,MatNullSpace nullsp) 5100 { 5101 PetscErrorCode ierr; 5102 5103 PetscFunctionBegin; 5104 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5105 PetscValidType(mat,1); 5106 MatPreallocated(mat); 5107 PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_COOKIE,2); 5108 5109 if (mat->nullsp) { 5110 ierr = MatNullSpaceDestroy(mat->nullsp);CHKERRQ(ierr); 5111 } 5112 mat->nullsp = nullsp; 5113 ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); 5114 PetscFunctionReturn(0); 5115 } 5116 5117 #undef __FUNCT__ 5118 #define __FUNCT__ "MatICCFactor" 5119 /*@ 5120 MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix. 5121 5122 Collective on Mat 5123 5124 Input Parameters: 5125 + mat - the matrix 5126 . row - row/column permutation 5127 . fill - expected fill factor >= 1.0 5128 - level - level of fill, for ICC(k) 5129 5130 Notes: 5131 Probably really in-place only when level of fill is zero, otherwise allocates 5132 new space to store factored matrix and deletes previous memory. 5133 5134 Most users should employ the simplified KSP interface for linear solvers 5135 instead of working directly with matrix algebra routines such as this. 5136 See, e.g., KSPCreate(). 5137 5138 Level: developer 5139 5140 Concepts: matrices^incomplete Cholesky factorization 5141 Concepts: Cholesky factorization 5142 5143 .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 5144 @*/ 5145 PetscErrorCode MatICCFactor(Mat mat,IS row,MatFactorInfo* info) 5146 { 5147 PetscErrorCode ierr; 5148 5149 PetscFunctionBegin; 5150 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5151 PetscValidType(mat,1); 5152 MatPreallocated(mat); 5153 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); 5154 PetscValidPointer(info,3); 5155 if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"matrix must be square"); 5156 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5157 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5158 if (!mat->ops->iccfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5159 ierr = (*mat->ops->iccfactor)(mat,row,info);CHKERRQ(ierr); 5160 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5161 PetscFunctionReturn(0); 5162 } 5163 5164 #undef __FUNCT__ 5165 #define __FUNCT__ "MatSetValuesAdic" 5166 /*@ 5167 MatSetValuesAdic - Sets values computed with ADIC automatic differentiation into a matrix. 5168 5169 Not Collective 5170 5171 Input Parameters: 5172 + mat - the matrix 5173 - v - the values compute with ADIC 5174 5175 Level: developer 5176 5177 Notes: 5178 Must call MatSetColoring() before using this routine. Also this matrix must already 5179 have its nonzero pattern determined. 5180 5181 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5182 MatSetValues(), MatSetColoring(), MatSetValuesAdifor() 5183 @*/ 5184 PetscErrorCode MatSetValuesAdic(Mat mat,void *v) 5185 { 5186 PetscErrorCode ierr; 5187 5188 PetscFunctionBegin; 5189 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5190 PetscValidType(mat,1); 5191 PetscValidPointer(mat,2); 5192 5193 if (!mat->assembled) { 5194 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5195 } 5196 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5197 if (!mat->ops->setvaluesadic) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5198 ierr = (*mat->ops->setvaluesadic)(mat,v);CHKERRQ(ierr); 5199 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5200 ierr = MatView_Private(mat);CHKERRQ(ierr); 5201 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5202 PetscFunctionReturn(0); 5203 } 5204 5205 5206 #undef __FUNCT__ 5207 #define __FUNCT__ "MatSetColoring" 5208 /*@ 5209 MatSetColoring - Sets a coloring used by calls to MatSetValuesAdic() 5210 5211 Not Collective 5212 5213 Input Parameters: 5214 + mat - the matrix 5215 - coloring - the coloring 5216 5217 Level: developer 5218 5219 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5220 MatSetValues(), MatSetValuesAdic() 5221 @*/ 5222 PetscErrorCode MatSetColoring(Mat mat,ISColoring coloring) 5223 { 5224 PetscErrorCode ierr; 5225 5226 PetscFunctionBegin; 5227 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5228 PetscValidType(mat,1); 5229 PetscValidPointer(coloring,2); 5230 5231 if (!mat->assembled) { 5232 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5233 } 5234 if (!mat->ops->setcoloring) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5235 ierr = (*mat->ops->setcoloring)(mat,coloring);CHKERRQ(ierr); 5236 PetscFunctionReturn(0); 5237 } 5238 5239 #undef __FUNCT__ 5240 #define __FUNCT__ "MatSetValuesAdifor" 5241 /*@ 5242 MatSetValuesAdifor - Sets values computed with automatic differentiation into a matrix. 5243 5244 Not Collective 5245 5246 Input Parameters: 5247 + mat - the matrix 5248 . nl - leading dimension of v 5249 - v - the values compute with ADIFOR 5250 5251 Level: developer 5252 5253 Notes: 5254 Must call MatSetColoring() before using this routine. Also this matrix must already 5255 have its nonzero pattern determined. 5256 5257 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5258 MatSetValues(), MatSetColoring() 5259 @*/ 5260 PetscErrorCode MatSetValuesAdifor(Mat mat,PetscInt nl,void *v) 5261 { 5262 PetscErrorCode ierr; 5263 5264 PetscFunctionBegin; 5265 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5266 PetscValidType(mat,1); 5267 PetscValidPointer(v,3); 5268 5269 if (!mat->assembled) { 5270 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5271 } 5272 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5273 if (!mat->ops->setvaluesadifor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5274 ierr = (*mat->ops->setvaluesadifor)(mat,nl,v);CHKERRQ(ierr); 5275 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5276 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5277 PetscFunctionReturn(0); 5278 } 5279 5280 EXTERN PetscErrorCode MatMPIAIJDiagonalScaleLocal(Mat,Vec); 5281 EXTERN PetscErrorCode MatMPIBAIJDiagonalScaleLocal(Mat,Vec); 5282 5283 #undef __FUNCT__ 5284 #define __FUNCT__ "MatDiagonalScaleLocal" 5285 /*@ 5286 MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the 5287 ghosted ones. 5288 5289 Not Collective 5290 5291 Input Parameters: 5292 + mat - the matrix 5293 - diag = the diagonal values, including ghost ones 5294 5295 Level: developer 5296 5297 Notes: Works only for MPIAIJ and MPIBAIJ matrices 5298 5299 .seealso: MatDiagonalScale() 5300 @*/ 5301 PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag) 5302 { 5303 PetscErrorCode ierr; 5304 PetscMPIInt size; 5305 5306 PetscFunctionBegin; 5307 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5308 PetscValidHeaderSpecific(diag,VEC_COOKIE,2); 5309 PetscValidType(mat,1); 5310 5311 if (!mat->assembled) { 5312 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5313 } 5314 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 5315 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 5316 if (size == 1) { 5317 PetscInt n,m; 5318 ierr = VecGetSize(diag,&n);CHKERRQ(ierr); 5319 ierr = MatGetSize(mat,0,&m);CHKERRQ(ierr); 5320 if (m == n) { 5321 ierr = MatDiagonalScale(mat,0,diag);CHKERRQ(ierr); 5322 } else { 5323 SETERRQ(PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions"); 5324 } 5325 } else { 5326 PetscErrorCode (*f)(Mat,Vec); 5327 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",(void (**)(void))&f);CHKERRQ(ierr); 5328 if (f) { 5329 ierr = (*f)(mat,diag);CHKERRQ(ierr); 5330 } else { 5331 SETERRQ(PETSC_ERR_SUP,"Only supported for MPIAIJ and MPIBAIJ parallel matrices"); 5332 } 5333 } 5334 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 5335 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5336 PetscFunctionReturn(0); 5337 } 5338 5339 #undef __FUNCT__ 5340 #define __FUNCT__ "MatGetInertia" 5341 /*@ 5342 MatGetInertia - Gets the inertia from a factored matrix 5343 5344 Collective on Mat 5345 5346 Input Parameter: 5347 . mat - the matrix 5348 5349 Output Parameters: 5350 + nneg - number of negative eigenvalues 5351 . nzero - number of zero eigenvalues 5352 - npos - number of positive eigenvalues 5353 5354 Level: advanced 5355 5356 Notes: Matrix must have been factored by MatCholeskyFactor() 5357 5358 5359 @*/ 5360 PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos) 5361 { 5362 PetscErrorCode ierr; 5363 5364 PetscFunctionBegin; 5365 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5366 PetscValidType(mat,1); 5367 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 5368 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled"); 5369 if (!mat->ops->getinertia) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5370 ierr = (*mat->ops->getinertia)(mat,nneg,nzero,npos);CHKERRQ(ierr); 5371 PetscFunctionReturn(0); 5372 } 5373 5374 /* ----------------------------------------------------------------*/ 5375 #undef __FUNCT__ 5376 #define __FUNCT__ "MatSolves" 5377 /*@ 5378 MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors 5379 5380 Collective on Mat and Vecs 5381 5382 Input Parameters: 5383 + mat - the factored matrix 5384 - b - the right-hand-side vectors 5385 5386 Output Parameter: 5387 . x - the result vectors 5388 5389 Notes: 5390 The vectors b and x cannot be the same. I.e., one cannot 5391 call MatSolves(A,x,x). 5392 5393 Notes: 5394 Most users should employ the simplified KSP interface for linear solvers 5395 instead of working directly with matrix algebra routines such as this. 5396 See, e.g., KSPCreate(). 5397 5398 Level: developer 5399 5400 Concepts: matrices^triangular solves 5401 5402 .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve() 5403 @*/ 5404 PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x) 5405 { 5406 PetscErrorCode ierr; 5407 5408 PetscFunctionBegin; 5409 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5410 PetscValidType(mat,1); 5411 MatPreallocated(mat); 5412 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 5413 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 5414 if (!mat->M && !mat->N) PetscFunctionReturn(0); 5415 5416 if (!mat->ops->solves) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5417 ierr = PetscLogEventBegin(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 5418 ierr = (*mat->ops->solves)(mat,b,x);CHKERRQ(ierr); 5419 ierr = PetscLogEventEnd(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 5420 PetscFunctionReturn(0); 5421 } 5422 5423 #undef __FUNCT__ 5424 #define __FUNCT__ "MatIsSymmetric" 5425 /*@C 5426 MatIsSymmetric - Test whether a matrix is symmetric 5427 5428 Collective on Mat 5429 5430 Input Parameter: 5431 + A - the matrix to test 5432 - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose) 5433 5434 Output Parameters: 5435 . flg - the result 5436 5437 Level: intermediate 5438 5439 Concepts: matrix^symmetry 5440 5441 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown() 5442 @*/ 5443 PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscTruth *flg) 5444 { 5445 PetscErrorCode ierr; 5446 5447 PetscFunctionBegin; 5448 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5449 PetscValidPointer(flg,2); 5450 if (!A->symmetric_set) { 5451 if (!A->ops->issymmetric) { 5452 MatType mattype; 5453 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 5454 SETERRQ1(PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); 5455 } 5456 ierr = (*A->ops->issymmetric)(A,tol,&A->symmetric);CHKERRQ(ierr); 5457 A->symmetric_set = PETSC_TRUE; 5458 if (A->symmetric) { 5459 A->structurally_symmetric_set = PETSC_TRUE; 5460 A->structurally_symmetric = PETSC_TRUE; 5461 } 5462 } 5463 *flg = A->symmetric; 5464 PetscFunctionReturn(0); 5465 } 5466 5467 #undef __FUNCT__ 5468 #define __FUNCT__ "MatIsSymmetricKnown" 5469 /*@C 5470 MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric. 5471 5472 Collective on Mat 5473 5474 Input Parameter: 5475 . A - the matrix to check 5476 5477 Output Parameters: 5478 + set - if the symmetric flag is set (this tells you if the next flag is valid) 5479 - flg - the result 5480 5481 Level: advanced 5482 5483 Concepts: matrix^symmetry 5484 5485 Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric() 5486 if you want it explicitly checked 5487 5488 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() 5489 @*/ 5490 PetscErrorCode MatIsSymmetricKnown(Mat A,PetscTruth *set,PetscTruth *flg) 5491 { 5492 PetscFunctionBegin; 5493 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5494 PetscValidPointer(set,2); 5495 PetscValidPointer(flg,3); 5496 if (A->symmetric_set) { 5497 *set = PETSC_TRUE; 5498 *flg = A->symmetric; 5499 } else { 5500 *set = PETSC_FALSE; 5501 } 5502 PetscFunctionReturn(0); 5503 } 5504 5505 #undef __FUNCT__ 5506 #define __FUNCT__ "MatIsHermitianKnown" 5507 /*@C 5508 MatIsHermitianKnown - Checks the flag on the matrix to see if it is hermitian. 5509 5510 Collective on Mat 5511 5512 Input Parameter: 5513 . A - the matrix to check 5514 5515 Output Parameters: 5516 + set - if the hermitian flag is set (this tells you if the next flag is valid) 5517 - flg - the result 5518 5519 Level: advanced 5520 5521 Concepts: matrix^symmetry 5522 5523 Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsHermitian() 5524 if you want it explicitly checked 5525 5526 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() 5527 @*/ 5528 PetscErrorCode MatIsHermitianKnown(Mat A,PetscTruth *set,PetscTruth *flg) 5529 { 5530 PetscFunctionBegin; 5531 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5532 PetscValidPointer(set,2); 5533 PetscValidPointer(flg,3); 5534 if (A->hermitian_set) { 5535 *set = PETSC_TRUE; 5536 *flg = A->hermitian; 5537 } else { 5538 *set = PETSC_FALSE; 5539 } 5540 PetscFunctionReturn(0); 5541 } 5542 5543 #undef __FUNCT__ 5544 #define __FUNCT__ "MatIsStructurallySymmetric" 5545 /*@C 5546 MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric 5547 5548 Collective on Mat 5549 5550 Input Parameter: 5551 . A - the matrix to test 5552 5553 Output Parameters: 5554 . flg - the result 5555 5556 Level: intermediate 5557 5558 Concepts: matrix^symmetry 5559 5560 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption() 5561 @*/ 5562 PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscTruth *flg) 5563 { 5564 PetscErrorCode ierr; 5565 5566 PetscFunctionBegin; 5567 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5568 PetscValidPointer(flg,2); 5569 if (!A->structurally_symmetric_set) { 5570 if (!A->ops->isstructurallysymmetric) SETERRQ(PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric"); 5571 ierr = (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);CHKERRQ(ierr); 5572 A->structurally_symmetric_set = PETSC_TRUE; 5573 } 5574 *flg = A->structurally_symmetric; 5575 PetscFunctionReturn(0); 5576 } 5577 5578 #undef __FUNCT__ 5579 #define __FUNCT__ "MatIsHermitian" 5580 /*@C 5581 MatIsHermitian - Test whether a matrix is Hermitian, i.e. it is the complex conjugate of its transpose. 5582 5583 Collective on Mat 5584 5585 Input Parameter: 5586 . A - the matrix to test 5587 5588 Output Parameters: 5589 . flg - the result 5590 5591 Level: intermediate 5592 5593 Concepts: matrix^symmetry 5594 5595 .seealso: MatTranspose(), MatIsTranspose(), MatIsSymmetric(), MatIsStructurallySymmetric(), MatSetOption() 5596 @*/ 5597 PetscErrorCode MatIsHermitian(Mat A,PetscTruth *flg) 5598 { 5599 PetscErrorCode ierr; 5600 5601 PetscFunctionBegin; 5602 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5603 PetscValidPointer(flg,2); 5604 if (!A->hermitian_set) { 5605 if (!A->ops->ishermitian) SETERRQ(PETSC_ERR_SUP,"Matrix does not support checking for being Hermitian"); 5606 ierr = (*A->ops->ishermitian)(A,&A->hermitian);CHKERRQ(ierr); 5607 A->hermitian_set = PETSC_TRUE; 5608 if (A->hermitian) { 5609 A->structurally_symmetric_set = PETSC_TRUE; 5610 A->structurally_symmetric = PETSC_TRUE; 5611 } 5612 } 5613 *flg = A->hermitian; 5614 PetscFunctionReturn(0); 5615 } 5616 5617 #undef __FUNCT__ 5618 #define __FUNCT__ "MatStashGetInfo" 5619 extern PetscErrorCode MatStashGetInfo_Private(MatStash*,PetscInt*,PetscInt*); 5620 /*@ 5621 MatStashGetInfo - Gets how many values are currently in the vector stash, i.e. need 5622 to be communicated to other processors during the MatAssemblyBegin/End() process 5623 5624 Not collective 5625 5626 Input Parameter: 5627 . vec - the vector 5628 5629 Output Parameters: 5630 + nstash - the size of the stash 5631 . reallocs - the number of additional mallocs incurred. 5632 . bnstash - the size of the block stash 5633 - breallocs - the number of additional mallocs incurred.in the block stash 5634 5635 Level: advanced 5636 5637 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize() 5638 5639 @*/ 5640 PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *brealloc) 5641 { 5642 PetscErrorCode ierr; 5643 PetscFunctionBegin; 5644 ierr = MatStashGetInfo_Private(&mat->stash,nstash,reallocs);CHKERRQ(ierr); 5645 ierr = MatStashGetInfo_Private(&mat->bstash,nstash,reallocs);CHKERRQ(ierr); 5646 PetscFunctionReturn(0); 5647 } 5648 5649 #undef __FUNCT__ 5650 #define __FUNCT__ "MatGetVecs" 5651 /*@ 5652 MatGetVecs - Get vector(s) compatible with the matrix, i.e. with the same 5653 parallel layout 5654 5655 Collective on Mat 5656 5657 Input Parameter: 5658 . mat - the matrix 5659 5660 Output Parameter: 5661 + right - (optional) vector that the matrix can be multiplied against 5662 - left - (optional) vector that the matrix vector product can be stored in 5663 5664 Level: advanced 5665 5666 .seealso: MatCreate() 5667 @*/ 5668 PetscErrorCode MatGetVecs(Mat mat,Vec *right,Vec *left) 5669 { 5670 PetscErrorCode ierr; 5671 5672 PetscFunctionBegin; 5673 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5674 PetscValidType(mat,1); 5675 MatPreallocated(mat); 5676 if (mat->ops->getvecs) { 5677 ierr = (*mat->ops->getvecs)(mat,right,left);CHKERRQ(ierr); 5678 } else { 5679 PetscMPIInt size; 5680 ierr = MPI_Comm_size(mat->comm, &size);CHKERRQ(ierr); 5681 if (right) { 5682 ierr = VecCreate(mat->comm,right);CHKERRQ(ierr); 5683 ierr = VecSetSizes(*right,mat->n,PETSC_DETERMINE);CHKERRQ(ierr); 5684 if (size > 1) {ierr = VecSetType(*right,VECMPI);CHKERRQ(ierr);} 5685 else {ierr = VecSetType(*right,VECSEQ);CHKERRQ(ierr);} 5686 } 5687 if (left) { 5688 ierr = VecCreate(mat->comm,left);CHKERRQ(ierr); 5689 ierr = VecSetSizes(*left,mat->m,PETSC_DETERMINE);CHKERRQ(ierr); 5690 if (size > 1) {ierr = VecSetType(*left,VECMPI);CHKERRQ(ierr);} 5691 else {ierr = VecSetType(*left,VECSEQ);CHKERRQ(ierr);} 5692 } 5693 } 5694 PetscFunctionReturn(0); 5695 } 5696 5697 #undef __FUNCT__ 5698 #define __FUNCT__ "MatFactorInfoInitialize" 5699 /*@C 5700 MatFactorInfoInitialize - Initializes a MatFactorInfo data structure 5701 with default values. 5702 5703 Not Collective 5704 5705 Input Parameters: 5706 . info - the MatFactorInfo data structure 5707 5708 5709 Notes: The solvers are generally used through the KSP and PC objects, for example 5710 PCLU, PCILU, PCCHOLESKY, PCICC 5711 5712 Level: developer 5713 5714 .seealso: MatFactorInfo 5715 @*/ 5716 5717 PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info) 5718 { 5719 PetscErrorCode ierr; 5720 5721 PetscFunctionBegin; 5722 ierr = PetscMemzero(info,sizeof(MatFactorInfo));CHKERRQ(ierr); 5723 PetscFunctionReturn(0); 5724 } 5725