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 (int)info.nz_used,(int)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 ierr = MatZeroEntries(B);CHKERRQ(ierr); 2529 ierr = MatGetOwnershipRange(A,&rstart,&rend);CHKERRQ(ierr); 2530 for (i=rstart; i<rend; i++) { 2531 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2532 ierr = MatSetValues(B,1,&i,nz,cwork,vwork,INSERT_VALUES);CHKERRQ(ierr); 2533 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 2534 } 2535 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2536 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2537 ierr = PetscObjectIncreaseState((PetscObject)B);CHKERRQ(ierr); 2538 PetscFunctionReturn(0); 2539 } 2540 2541 #undef __FUNCT__ 2542 #define __FUNCT__ "MatCopy" 2543 /*@C 2544 MatCopy - Copys a matrix to another matrix. 2545 2546 Collective on Mat 2547 2548 Input Parameters: 2549 + A - the matrix 2550 - str - SAME_NONZERO_PATTERN or DIFFERENT_NONZERO_PATTERN 2551 2552 Output Parameter: 2553 . B - where the copy is put 2554 2555 Notes: 2556 If you use SAME_NONZERO_PATTERN then the two matrices had better have the 2557 same nonzero pattern or the routine will crash. 2558 2559 MatCopy() copies the matrix entries of a matrix to another existing 2560 matrix (after first zeroing the second matrix). A related routine is 2561 MatConvert(), which first creates a new matrix and then copies the data. 2562 2563 Level: intermediate 2564 2565 Concepts: matrices^copying 2566 2567 .seealso: MatConvert(), MatDuplicate() 2568 2569 @*/ 2570 PetscErrorCode MatCopy(Mat A,Mat B,MatStructure str) 2571 { 2572 PetscErrorCode ierr; 2573 2574 PetscFunctionBegin; 2575 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 2576 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 2577 PetscValidType(A,1); 2578 MatPreallocated(A); 2579 PetscValidType(B,2); 2580 MatPreallocated(B); 2581 PetscCheckSameComm(A,1,B,2); 2582 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2583 if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2584 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, 2585 A->N,B->N); 2586 2587 ierr = PetscLogEventBegin(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 2588 if (A->ops->copy) { 2589 ierr = (*A->ops->copy)(A,B,str);CHKERRQ(ierr); 2590 } else { /* generic conversion */ 2591 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 2592 } 2593 if (A->mapping) { 2594 if (B->mapping) {ierr = ISLocalToGlobalMappingDestroy(B->mapping);CHKERRQ(ierr);B->mapping = 0;} 2595 ierr = MatSetLocalToGlobalMapping(B,A->mapping);CHKERRQ(ierr); 2596 } 2597 if (A->bmapping) { 2598 if (B->bmapping) {ierr = ISLocalToGlobalMappingDestroy(B->bmapping);CHKERRQ(ierr);B->bmapping = 0;} 2599 ierr = MatSetLocalToGlobalMappingBlock(B,A->mapping);CHKERRQ(ierr); 2600 } 2601 ierr = PetscLogEventEnd(MAT_Copy,A,B,0,0);CHKERRQ(ierr); 2602 ierr = PetscObjectIncreaseState((PetscObject)B);CHKERRQ(ierr); 2603 PetscFunctionReturn(0); 2604 } 2605 2606 #include "petscsys.h" 2607 PetscTruth MatConvertRegisterAllCalled = PETSC_FALSE; 2608 PetscFList MatConvertList = 0; 2609 2610 #undef __FUNCT__ 2611 #define __FUNCT__ "MatConvertRegister" 2612 /*@C 2613 MatConvertRegister - Allows one to register a routine that converts a sparse matrix 2614 from one format to another. 2615 2616 Not Collective 2617 2618 Input Parameters: 2619 + type - the type of matrix (defined in include/petscmat.h), for example, MATSEQAIJ. 2620 - Converter - the function that reads the matrix from the binary file. 2621 2622 Level: developer 2623 2624 .seealso: MatConvertRegisterAll(), MatConvert() 2625 2626 @*/ 2627 PetscErrorCode MatConvertRegister(const char sname[],const char path[],const char name[],PetscErrorCode (*function)(Mat,MatType,Mat*)) 2628 { 2629 PetscErrorCode ierr; 2630 char fullname[PETSC_MAX_PATH_LEN]; 2631 2632 PetscFunctionBegin; 2633 ierr = PetscFListConcat(path,name,fullname);CHKERRQ(ierr); 2634 ierr = PetscFListAdd(&MatConvertList,sname,fullname,(void (*)(void))function);CHKERRQ(ierr); 2635 PetscFunctionReturn(0); 2636 } 2637 2638 #undef __FUNCT__ 2639 #define __FUNCT__ "MatConvert" 2640 /*@C 2641 MatConvert - Converts a matrix to another matrix, either of the same 2642 or different type. 2643 2644 Collective on Mat 2645 2646 Input Parameters: 2647 + mat - the matrix 2648 - newtype - new matrix type. Use MATSAME to create a new matrix of the 2649 same type as the original matrix. 2650 2651 Output Parameter: 2652 . M - pointer to place new matrix 2653 2654 Notes: 2655 MatConvert() first creates a new matrix and then copies the data from 2656 the first matrix. A related routine is MatCopy(), which copies the matrix 2657 entries of one matrix to another already existing matrix context. 2658 2659 Level: intermediate 2660 2661 Concepts: matrices^converting between storage formats 2662 2663 .seealso: MatCopy(), MatDuplicate() 2664 @*/ 2665 PetscErrorCode MatConvert(Mat mat,const MatType newtype,Mat *M) 2666 { 2667 PetscErrorCode ierr; 2668 PetscTruth sametype,issame,flg; 2669 char convname[256],mtype[256]; 2670 2671 PetscFunctionBegin; 2672 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2673 PetscValidType(mat,1); 2674 MatPreallocated(mat); 2675 PetscValidPointer(M,3); 2676 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2677 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2678 2679 ierr = PetscOptionsGetString(PETSC_NULL,"-matconvert_type",mtype,256,&flg);CHKERRQ(ierr); 2680 if (flg) { 2681 newtype = mtype; 2682 } 2683 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2684 2685 ierr = PetscTypeCompare((PetscObject)mat,newtype,&sametype);CHKERRQ(ierr); 2686 ierr = PetscStrcmp(newtype,"same",&issame);CHKERRQ(ierr); 2687 if ((sametype || issame) && mat->ops->duplicate) { 2688 ierr = (*mat->ops->duplicate)(mat,MAT_COPY_VALUES,M);CHKERRQ(ierr); 2689 } else { 2690 PetscErrorCode (*conv)(Mat,const MatType,Mat*)=PETSC_NULL; 2691 /* 2692 Order of precedence: 2693 1) See if a specialized converter is known to the current matrix. 2694 2) See if a specialized converter is known to the desired matrix class. 2695 3) See if a good general converter is registered for the desired class 2696 (as of 6/27/03 only MATMPIADJ falls into this category). 2697 4) See if a good general converter is known for the current matrix. 2698 5) Use a really basic converter. 2699 */ 2700 ierr = PetscStrcpy(convname,"MatConvert_");CHKERRQ(ierr); 2701 ierr = PetscStrcat(convname,mat->type_name);CHKERRQ(ierr); 2702 ierr = PetscStrcat(convname,"_");CHKERRQ(ierr); 2703 ierr = PetscStrcat(convname,newtype);CHKERRQ(ierr); 2704 ierr = PetscStrcat(convname,"_C");CHKERRQ(ierr); 2705 ierr = PetscObjectQueryFunction((PetscObject)mat,convname,(void (**)(void))&conv);CHKERRQ(ierr); 2706 if (!conv) { 2707 Mat B; 2708 ierr = MatCreate(mat->comm,0,0,0,0,&B);CHKERRQ(ierr); 2709 ierr = MatSetType(B,newtype);CHKERRQ(ierr); 2710 ierr = PetscObjectQueryFunction((PetscObject)B,convname,(void (**)(void))&conv);CHKERRQ(ierr); 2711 ierr = MatDestroy(B);CHKERRQ(ierr); 2712 if (!conv) { 2713 if (!MatConvertRegisterAllCalled) { 2714 ierr = MatConvertRegisterAll(PETSC_NULL);CHKERRQ(ierr); 2715 } 2716 ierr = PetscFListFind(mat->comm,MatConvertList,newtype,(void(**)(void))&conv);CHKERRQ(ierr); 2717 if (!conv) { 2718 if (mat->ops->convert) { 2719 conv = mat->ops->convert; 2720 } else { 2721 conv = MatConvert_Basic; 2722 } 2723 } 2724 } 2725 } 2726 ierr = (*conv)(mat,newtype,M);CHKERRQ(ierr); 2727 } 2728 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2729 ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); 2730 PetscFunctionReturn(0); 2731 } 2732 2733 2734 #undef __FUNCT__ 2735 #define __FUNCT__ "MatDuplicate" 2736 /*@C 2737 MatDuplicate - Duplicates a matrix including the non-zero structure. 2738 2739 Collective on Mat 2740 2741 Input Parameters: 2742 + mat - the matrix 2743 - op - either MAT_DO_NOT_COPY_VALUES or MAT_COPY_VALUES, cause it to copy nonzero 2744 values as well or not 2745 2746 Output Parameter: 2747 . M - pointer to place new matrix 2748 2749 Level: intermediate 2750 2751 Concepts: matrices^duplicating 2752 2753 .seealso: MatCopy(), MatConvert() 2754 @*/ 2755 PetscErrorCode MatDuplicate(Mat mat,MatDuplicateOption op,Mat *M) 2756 { 2757 PetscErrorCode ierr; 2758 2759 PetscFunctionBegin; 2760 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2761 PetscValidType(mat,1); 2762 MatPreallocated(mat); 2763 PetscValidPointer(M,3); 2764 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2765 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2766 2767 *M = 0; 2768 ierr = PetscLogEventBegin(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2769 if (!mat->ops->duplicate) { 2770 SETERRQ(PETSC_ERR_SUP,"Not written for this matrix type"); 2771 } 2772 ierr = (*mat->ops->duplicate)(mat,op,M);CHKERRQ(ierr); 2773 if (mat->mapping) { 2774 ierr = MatSetLocalToGlobalMapping(*M,mat->mapping);CHKERRQ(ierr); 2775 } 2776 if (mat->bmapping) { 2777 ierr = MatSetLocalToGlobalMappingBlock(*M,mat->mapping);CHKERRQ(ierr); 2778 } 2779 ierr = PetscLogEventEnd(MAT_Convert,mat,0,0,0);CHKERRQ(ierr); 2780 ierr = PetscObjectIncreaseState((PetscObject)*M);CHKERRQ(ierr); 2781 PetscFunctionReturn(0); 2782 } 2783 2784 #undef __FUNCT__ 2785 #define __FUNCT__ "MatGetDiagonal" 2786 /*@ 2787 MatGetDiagonal - Gets the diagonal of a matrix. 2788 2789 Collective on Mat and Vec 2790 2791 Input Parameters: 2792 + mat - the matrix 2793 - v - the vector for storing the diagonal 2794 2795 Output Parameter: 2796 . v - the diagonal of the matrix 2797 2798 Notes: 2799 For the SeqAIJ matrix format, this routine may also be called 2800 on a LU factored matrix; in that case it routines the reciprocal of 2801 the diagonal entries in U. It returns the entries permuted by the 2802 row and column permutation used during the symbolic factorization. 2803 2804 Level: intermediate 2805 2806 Concepts: matrices^accessing diagonals 2807 2808 .seealso: MatGetRow(), MatGetSubmatrices(), MatGetSubmatrix(), MatGetRowMax() 2809 @*/ 2810 PetscErrorCode MatGetDiagonal(Mat mat,Vec v) 2811 { 2812 PetscErrorCode ierr; 2813 2814 PetscFunctionBegin; 2815 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2816 PetscValidType(mat,1); 2817 MatPreallocated(mat); 2818 PetscValidHeaderSpecific(v,VEC_COOKIE,2); 2819 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2820 if (!mat->ops->getdiagonal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2821 2822 ierr = (*mat->ops->getdiagonal)(mat,v);CHKERRQ(ierr); 2823 ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); 2824 PetscFunctionReturn(0); 2825 } 2826 2827 #undef __FUNCT__ 2828 #define __FUNCT__ "MatGetRowMax" 2829 /*@ 2830 MatGetRowMax - Gets the maximum value (in absolute value) of each 2831 row of the matrix 2832 2833 Collective on Mat and Vec 2834 2835 Input Parameters: 2836 . mat - the matrix 2837 2838 Output Parameter: 2839 . v - the vector for storing the maximums 2840 2841 Level: intermediate 2842 2843 Concepts: matrices^getting row maximums 2844 2845 .seealso: MatGetDiagonal(), MatGetSubmatrices(), MatGetSubmatrix() 2846 @*/ 2847 PetscErrorCode MatGetRowMax(Mat mat,Vec v) 2848 { 2849 PetscErrorCode ierr; 2850 2851 PetscFunctionBegin; 2852 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2853 PetscValidType(mat,1); 2854 MatPreallocated(mat); 2855 PetscValidHeaderSpecific(v,VEC_COOKIE,2); 2856 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2857 if (!mat->ops->getrowmax) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2858 2859 ierr = (*mat->ops->getrowmax)(mat,v);CHKERRQ(ierr); 2860 ierr = PetscObjectIncreaseState((PetscObject)v);CHKERRQ(ierr); 2861 PetscFunctionReturn(0); 2862 } 2863 2864 #undef __FUNCT__ 2865 #define __FUNCT__ "MatTranspose" 2866 /*@C 2867 MatTranspose - Computes an in-place or out-of-place transpose of a matrix. 2868 2869 Collective on Mat 2870 2871 Input Parameter: 2872 . mat - the matrix to transpose 2873 2874 Output Parameters: 2875 . B - the transpose (or pass in PETSC_NULL for an in-place transpose) 2876 2877 Level: intermediate 2878 2879 Concepts: matrices^transposing 2880 2881 .seealso: MatMultTranspose(), MatMultTransposeAdd(), MatIsTranspose() 2882 @*/ 2883 PetscErrorCode MatTranspose(Mat mat,Mat *B) 2884 { 2885 PetscErrorCode ierr; 2886 2887 PetscFunctionBegin; 2888 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2889 PetscValidType(mat,1); 2890 MatPreallocated(mat); 2891 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2892 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2893 if (!mat->ops->transpose) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2894 2895 ierr = PetscLogEventBegin(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 2896 ierr = (*mat->ops->transpose)(mat,B);CHKERRQ(ierr); 2897 ierr = PetscLogEventEnd(MAT_Transpose,mat,0,0,0);CHKERRQ(ierr); 2898 if (B) {ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr);} 2899 PetscFunctionReturn(0); 2900 } 2901 2902 #undef __FUNCT__ 2903 #define __FUNCT__ "MatIsTranspose" 2904 /*@C 2905 MatIsTranspose - Test whether a matrix is another one's transpose, 2906 or its own, in which case it tests symmetry. 2907 2908 Collective on Mat 2909 2910 Input Parameter: 2911 + A - the matrix to test 2912 - B - the matrix to test against, this can equal the first parameter 2913 2914 Output Parameters: 2915 . flg - the result 2916 2917 Notes: 2918 Only available for SeqAIJ/MPIAIJ matrices. The sequential algorithm 2919 has a running time of the order of the number of nonzeros; the parallel 2920 test involves parallel copies of the block-offdiagonal parts of the matrix. 2921 2922 Level: intermediate 2923 2924 Concepts: matrices^transposing, matrix^symmetry 2925 2926 .seealso: MatTranspose(), MatIsSymmetric(), MatIsHermitian() 2927 @*/ 2928 PetscErrorCode MatIsTranspose(Mat A,Mat B,PetscReal tol,PetscTruth *flg) 2929 { 2930 PetscErrorCode ierr,(*f)(Mat,Mat,PetscReal,PetscTruth*),(*g)(Mat,Mat,PetscReal,PetscTruth*); 2931 2932 PetscFunctionBegin; 2933 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 2934 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 2935 PetscValidPointer(flg,3); 2936 ierr = PetscObjectQueryFunction((PetscObject)A,"MatIsTranspose_C",(void (**)(void))&f);CHKERRQ(ierr); 2937 ierr = PetscObjectQueryFunction((PetscObject)B,"MatIsTranspose_C",(void (**)(void))&g);CHKERRQ(ierr); 2938 if (f && g) { 2939 if (f==g) { 2940 ierr = (*f)(A,B,tol,flg);CHKERRQ(ierr); 2941 } else { 2942 SETERRQ(PETSC_ERR_ARG_NOTSAMETYPE,"Matrices do not have the same comparator for symmetry test"); 2943 } 2944 } 2945 PetscFunctionReturn(0); 2946 } 2947 2948 #undef __FUNCT__ 2949 #define __FUNCT__ "MatPermute" 2950 /*@C 2951 MatPermute - Creates a new matrix with rows and columns permuted from the 2952 original. 2953 2954 Collective on Mat 2955 2956 Input Parameters: 2957 + mat - the matrix to permute 2958 . row - row permutation, each processor supplies only the permutation for its rows 2959 - col - column permutation, each processor needs the entire column permutation, that is 2960 this is the same size as the total number of columns in the matrix 2961 2962 Output Parameters: 2963 . B - the permuted matrix 2964 2965 Level: advanced 2966 2967 Concepts: matrices^permuting 2968 2969 .seealso: MatGetOrdering() 2970 @*/ 2971 PetscErrorCode MatPermute(Mat mat,IS row,IS col,Mat *B) 2972 { 2973 PetscErrorCode ierr; 2974 2975 PetscFunctionBegin; 2976 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2977 PetscValidType(mat,1); 2978 MatPreallocated(mat); 2979 PetscValidHeaderSpecific(row,IS_COOKIE,2); 2980 PetscValidHeaderSpecific(col,IS_COOKIE,3); 2981 PetscValidPointer(B,4); 2982 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2983 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2984 if (!mat->ops->permute) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 2985 ierr = (*mat->ops->permute)(mat,row,col,B);CHKERRQ(ierr); 2986 ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr); 2987 PetscFunctionReturn(0); 2988 } 2989 2990 #undef __FUNCT__ 2991 #define __FUNCT__ "MatPermuteSparsify" 2992 /*@C 2993 MatPermuteSparsify - Creates a new matrix with rows and columns permuted from the 2994 original and sparsified to the prescribed tolerance. 2995 2996 Collective on Mat 2997 2998 Input Parameters: 2999 + A - The matrix to permute 3000 . band - The half-bandwidth of the sparsified matrix, or PETSC_DECIDE 3001 . frac - The half-bandwidth as a fraction of the total size, or 0.0 3002 . tol - The drop tolerance 3003 . rowp - The row permutation 3004 - colp - The column permutation 3005 3006 Output Parameter: 3007 . B - The permuted, sparsified matrix 3008 3009 Level: advanced 3010 3011 Note: 3012 The default behavior (band = PETSC_DECIDE and frac = 0.0) is to 3013 restrict the half-bandwidth of the resulting matrix to 5% of the 3014 total matrix size. 3015 3016 .keywords: matrix, permute, sparsify 3017 3018 .seealso: MatGetOrdering(), MatPermute() 3019 @*/ 3020 PetscErrorCode MatPermuteSparsify(Mat A, PetscInt band, PetscReal frac, PetscReal tol, IS rowp, IS colp, Mat *B) 3021 { 3022 IS irowp, icolp; 3023 PetscInt *rows, *cols; 3024 PetscInt M, N, locRowStart, locRowEnd; 3025 PetscInt nz, newNz; 3026 const PetscInt *cwork; 3027 PetscInt *cnew; 3028 const PetscScalar *vwork; 3029 PetscScalar *vnew; 3030 PetscInt bw, size; 3031 PetscInt row, locRow, newRow, col, newCol; 3032 PetscErrorCode ierr; 3033 3034 PetscFunctionBegin; 3035 PetscValidHeaderSpecific(A, MAT_COOKIE,1); 3036 PetscValidHeaderSpecific(rowp, IS_COOKIE,5); 3037 PetscValidHeaderSpecific(colp, IS_COOKIE,6); 3038 PetscValidPointer(B,7); 3039 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix"); 3040 if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix"); 3041 if (!A->ops->permutesparsify) { 3042 ierr = MatGetSize(A, &M, &N);CHKERRQ(ierr); 3043 ierr = MatGetOwnershipRange(A, &locRowStart, &locRowEnd);CHKERRQ(ierr); 3044 ierr = ISGetSize(rowp, &size);CHKERRQ(ierr); 3045 if (size != M) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %d for row permutation, should be %d", size, M); 3046 ierr = ISGetSize(colp, &size);CHKERRQ(ierr); 3047 if (size != N) SETERRQ2(PETSC_ERR_ARG_WRONG, "Wrong size %d for column permutation, should be %d", size, N); 3048 ierr = ISInvertPermutation(rowp, 0, &irowp);CHKERRQ(ierr); 3049 ierr = ISGetIndices(irowp, &rows);CHKERRQ(ierr); 3050 ierr = ISInvertPermutation(colp, 0, &icolp);CHKERRQ(ierr); 3051 ierr = ISGetIndices(icolp, &cols);CHKERRQ(ierr); 3052 ierr = PetscMalloc(N * sizeof(PetscInt), &cnew);CHKERRQ(ierr); 3053 ierr = PetscMalloc(N * sizeof(PetscScalar), &vnew);CHKERRQ(ierr); 3054 3055 /* Setup bandwidth to include */ 3056 if (band == PETSC_DECIDE) { 3057 if (frac <= 0.0) 3058 bw = (PetscInt) (M * 0.05); 3059 else 3060 bw = (PetscInt) (M * frac); 3061 } else { 3062 if (band <= 0) SETERRQ(PETSC_ERR_ARG_WRONG, "Bandwidth must be a positive integer"); 3063 bw = band; 3064 } 3065 3066 /* Put values into new matrix */ 3067 ierr = MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, B);CHKERRQ(ierr); 3068 for(row = locRowStart, locRow = 0; row < locRowEnd; row++, locRow++) { 3069 ierr = MatGetRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); 3070 newRow = rows[locRow]+locRowStart; 3071 for(col = 0, newNz = 0; col < nz; col++) { 3072 newCol = cols[cwork[col]]; 3073 if ((newCol >= newRow - bw) && (newCol < newRow + bw) && (PetscAbsScalar(vwork[col]) >= tol)) { 3074 cnew[newNz] = newCol; 3075 vnew[newNz] = vwork[col]; 3076 newNz++; 3077 } 3078 } 3079 ierr = MatSetValues(*B, 1, &newRow, newNz, cnew, vnew, INSERT_VALUES);CHKERRQ(ierr); 3080 ierr = MatRestoreRow(A, row, &nz, &cwork, &vwork);CHKERRQ(ierr); 3081 } 3082 ierr = PetscFree(cnew);CHKERRQ(ierr); 3083 ierr = PetscFree(vnew);CHKERRQ(ierr); 3084 ierr = MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3085 ierr = MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 3086 ierr = ISRestoreIndices(irowp, &rows);CHKERRQ(ierr); 3087 ierr = ISRestoreIndices(icolp, &cols);CHKERRQ(ierr); 3088 ierr = ISDestroy(irowp);CHKERRQ(ierr); 3089 ierr = ISDestroy(icolp);CHKERRQ(ierr); 3090 } else { 3091 ierr = (*A->ops->permutesparsify)(A, band, frac, tol, rowp, colp, B);CHKERRQ(ierr); 3092 } 3093 ierr = PetscObjectIncreaseState((PetscObject)*B);CHKERRQ(ierr); 3094 PetscFunctionReturn(0); 3095 } 3096 3097 #undef __FUNCT__ 3098 #define __FUNCT__ "MatEqual" 3099 /*@ 3100 MatEqual - Compares two matrices. 3101 3102 Collective on Mat 3103 3104 Input Parameters: 3105 + A - the first matrix 3106 - B - the second matrix 3107 3108 Output Parameter: 3109 . flg - PETSC_TRUE if the matrices are equal; PETSC_FALSE otherwise. 3110 3111 Level: intermediate 3112 3113 Concepts: matrices^equality between 3114 @*/ 3115 PetscErrorCode MatEqual(Mat A,Mat B,PetscTruth *flg) 3116 { 3117 PetscErrorCode ierr; 3118 3119 PetscFunctionBegin; 3120 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 3121 PetscValidHeaderSpecific(B,MAT_COOKIE,2); 3122 PetscValidType(A,1); 3123 MatPreallocated(A); 3124 PetscValidType(B,2); 3125 MatPreallocated(B); 3126 PetscValidIntPointer(flg,3); 3127 PetscCheckSameComm(A,1,B,2); 3128 if (!A->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3129 if (!B->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3130 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); 3131 if (!A->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",A->type_name); 3132 if (!B->ops->equal) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",B->type_name); 3133 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); 3134 ierr = (*A->ops->equal)(A,B,flg);CHKERRQ(ierr); 3135 PetscFunctionReturn(0); 3136 } 3137 3138 #undef __FUNCT__ 3139 #define __FUNCT__ "MatDiagonalScale" 3140 /*@ 3141 MatDiagonalScale - Scales a matrix on the left and right by diagonal 3142 matrices that are stored as vectors. Either of the two scaling 3143 matrices can be PETSC_NULL. 3144 3145 Collective on Mat 3146 3147 Input Parameters: 3148 + mat - the matrix to be scaled 3149 . l - the left scaling vector (or PETSC_NULL) 3150 - r - the right scaling vector (or PETSC_NULL) 3151 3152 Notes: 3153 MatDiagonalScale() computes A = LAR, where 3154 L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector) 3155 3156 Level: intermediate 3157 3158 Concepts: matrices^diagonal scaling 3159 Concepts: diagonal scaling of matrices 3160 3161 .seealso: MatScale() 3162 @*/ 3163 PetscErrorCode MatDiagonalScale(Mat mat,Vec l,Vec r) 3164 { 3165 PetscErrorCode ierr; 3166 3167 PetscFunctionBegin; 3168 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3169 PetscValidType(mat,1); 3170 MatPreallocated(mat); 3171 if (!mat->ops->diagonalscale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3172 if (l) {PetscValidHeaderSpecific(l,VEC_COOKIE,2);PetscCheckSameComm(mat,1,l,2);} 3173 if (r) {PetscValidHeaderSpecific(r,VEC_COOKIE,3);PetscCheckSameComm(mat,1,r,3);} 3174 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3175 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3176 3177 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3178 ierr = (*mat->ops->diagonalscale)(mat,l,r);CHKERRQ(ierr); 3179 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3180 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3181 PetscFunctionReturn(0); 3182 } 3183 3184 #undef __FUNCT__ 3185 #define __FUNCT__ "MatScale" 3186 /*@ 3187 MatScale - Scales all elements of a matrix by a given number. 3188 3189 Collective on Mat 3190 3191 Input Parameters: 3192 + mat - the matrix to be scaled 3193 - a - the scaling value 3194 3195 Output Parameter: 3196 . mat - the scaled matrix 3197 3198 Level: intermediate 3199 3200 Concepts: matrices^scaling all entries 3201 3202 .seealso: MatDiagonalScale() 3203 @*/ 3204 PetscErrorCode MatScale(const PetscScalar *a,Mat mat) 3205 { 3206 PetscErrorCode ierr; 3207 3208 PetscFunctionBegin; 3209 PetscValidScalarPointer(a,1); 3210 PetscValidHeaderSpecific(mat,MAT_COOKIE,2); 3211 PetscValidType(mat,2); 3212 MatPreallocated(mat); 3213 if (!mat->ops->scale) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3214 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3215 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3216 3217 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3218 ierr = (*mat->ops->scale)(a,mat);CHKERRQ(ierr); 3219 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 3220 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3221 PetscFunctionReturn(0); 3222 } 3223 3224 #undef __FUNCT__ 3225 #define __FUNCT__ "MatNorm" 3226 /*@ 3227 MatNorm - Calculates various norms of a matrix. 3228 3229 Collective on Mat 3230 3231 Input Parameters: 3232 + mat - the matrix 3233 - type - the type of norm, NORM_1, NORM_FROBENIUS, NORM_INFINITY 3234 3235 Output Parameters: 3236 . nrm - the resulting norm 3237 3238 Level: intermediate 3239 3240 Concepts: matrices^norm 3241 Concepts: norm^of matrix 3242 @*/ 3243 PetscErrorCode MatNorm(Mat mat,NormType type,PetscReal *nrm) 3244 { 3245 PetscErrorCode ierr; 3246 3247 PetscFunctionBegin; 3248 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3249 PetscValidType(mat,1); 3250 MatPreallocated(mat); 3251 PetscValidScalarPointer(nrm,3); 3252 3253 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3254 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3255 if (!mat->ops->norm) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3256 ierr = (*mat->ops->norm)(mat,type,nrm);CHKERRQ(ierr); 3257 PetscFunctionReturn(0); 3258 } 3259 3260 /* 3261 This variable is used to prevent counting of MatAssemblyBegin() that 3262 are called from within a MatAssemblyEnd(). 3263 */ 3264 static PetscInt MatAssemblyEnd_InUse = 0; 3265 #undef __FUNCT__ 3266 #define __FUNCT__ "MatAssemblyBegin" 3267 /*@ 3268 MatAssemblyBegin - Begins assembling the matrix. This routine should 3269 be called after completing all calls to MatSetValues(). 3270 3271 Collective on Mat 3272 3273 Input Parameters: 3274 + mat - the matrix 3275 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 3276 3277 Notes: 3278 MatSetValues() generally caches the values. The matrix is ready to 3279 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 3280 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 3281 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 3282 using the matrix. 3283 3284 Level: beginner 3285 3286 Concepts: matrices^assembling 3287 3288 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssembled() 3289 @*/ 3290 PetscErrorCode MatAssemblyBegin(Mat mat,MatAssemblyType type) 3291 { 3292 PetscErrorCode ierr; 3293 3294 PetscFunctionBegin; 3295 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3296 PetscValidType(mat,1); 3297 MatPreallocated(mat); 3298 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix.\nDid you forget to call MatSetUnfactored()?"); 3299 if (mat->assembled) { 3300 mat->was_assembled = PETSC_TRUE; 3301 mat->assembled = PETSC_FALSE; 3302 } 3303 if (!MatAssemblyEnd_InUse) { 3304 ierr = PetscLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 3305 if (mat->ops->assemblybegin){ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} 3306 ierr = PetscLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);CHKERRQ(ierr); 3307 } else { 3308 if (mat->ops->assemblybegin){ierr = (*mat->ops->assemblybegin)(mat,type);CHKERRQ(ierr);} 3309 } 3310 PetscFunctionReturn(0); 3311 } 3312 3313 #undef __FUNCT__ 3314 #define __FUNCT__ "MatAssembed" 3315 /*@ 3316 MatAssembled - Indicates if a matrix has been assembled and is ready for 3317 use; for example, in matrix-vector product. 3318 3319 Collective on Mat 3320 3321 Input Parameter: 3322 . mat - the matrix 3323 3324 Output Parameter: 3325 . assembled - PETSC_TRUE or PETSC_FALSE 3326 3327 Level: advanced 3328 3329 Concepts: matrices^assembled? 3330 3331 .seealso: MatAssemblyEnd(), MatSetValues(), MatAssemblyBegin() 3332 @*/ 3333 PetscErrorCode MatAssembled(Mat mat,PetscTruth *assembled) 3334 { 3335 PetscFunctionBegin; 3336 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3337 PetscValidType(mat,1); 3338 MatPreallocated(mat); 3339 PetscValidPointer(assembled,2); 3340 *assembled = mat->assembled; 3341 PetscFunctionReturn(0); 3342 } 3343 3344 #undef __FUNCT__ 3345 #define __FUNCT__ "MatView_Private" 3346 /* 3347 Processes command line options to determine if/how a matrix 3348 is to be viewed. Called by MatAssemblyEnd() and MatLoad(). 3349 */ 3350 PetscErrorCode MatView_Private(Mat mat) 3351 { 3352 PetscErrorCode ierr; 3353 PetscTruth flg; 3354 static PetscTruth incall = PETSC_FALSE; 3355 3356 PetscFunctionBegin; 3357 if (incall) PetscFunctionReturn(0); 3358 incall = PETSC_TRUE; 3359 ierr = PetscOptionsBegin(mat->comm,mat->prefix,"Matrix Options","Mat");CHKERRQ(ierr); 3360 ierr = PetscOptionsName("-mat_view_info","Information on matrix size","MatView",&flg);CHKERRQ(ierr); 3361 if (flg) { 3362 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr); 3363 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3364 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3365 } 3366 ierr = PetscOptionsName("-mat_view_info_detailed","Nonzeros in the matrix","MatView",&flg);CHKERRQ(ierr); 3367 if (flg) { 3368 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_INFO_DETAIL);CHKERRQ(ierr); 3369 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3370 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3371 } 3372 ierr = PetscOptionsName("-mat_view","Print matrix to stdout","MatView",&flg);CHKERRQ(ierr); 3373 if (flg) { 3374 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3375 } 3376 ierr = PetscOptionsName("-mat_view_matlab","Print matrix to stdout in a format Matlab can read","MatView",&flg);CHKERRQ(ierr); 3377 if (flg) { 3378 ierr = PetscViewerPushFormat(PETSC_VIEWER_STDOUT_(mat->comm),PETSC_VIEWER_ASCII_MATLAB);CHKERRQ(ierr); 3379 ierr = MatView(mat,PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3380 ierr = PetscViewerPopFormat(PETSC_VIEWER_STDOUT_(mat->comm));CHKERRQ(ierr); 3381 } 3382 ierr = PetscOptionsName("-mat_view_socket","Send matrix to socket (can be read from matlab)","MatView",&flg);CHKERRQ(ierr); 3383 if (flg) { 3384 ierr = MatView(mat,PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); 3385 ierr = PetscViewerFlush(PETSC_VIEWER_SOCKET_(mat->comm));CHKERRQ(ierr); 3386 } 3387 ierr = PetscOptionsName("-mat_view_binary","Save matrix to file in binary format","MatView",&flg);CHKERRQ(ierr); 3388 if (flg) { 3389 ierr = MatView(mat,PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); 3390 ierr = PetscViewerFlush(PETSC_VIEWER_BINARY_(mat->comm));CHKERRQ(ierr); 3391 } 3392 ierr = PetscOptionsEnd();CHKERRQ(ierr); 3393 /* cannot have inside PetscOptionsBegin() because uses PetscOptionsBegin() */ 3394 ierr = PetscOptionsHasName(mat->prefix,"-mat_view_draw",&flg);CHKERRQ(ierr); 3395 if (flg) { 3396 ierr = PetscOptionsHasName(mat->prefix,"-mat_view_contour",&flg);CHKERRQ(ierr); 3397 if (flg) { 3398 PetscViewerPushFormat(PETSC_VIEWER_DRAW_(mat->comm),PETSC_VIEWER_DRAW_CONTOUR);CHKERRQ(ierr); 3399 } 3400 ierr = MatView(mat,PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3401 ierr = PetscViewerFlush(PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3402 if (flg) { 3403 PetscViewerPopFormat(PETSC_VIEWER_DRAW_(mat->comm));CHKERRQ(ierr); 3404 } 3405 } 3406 incall = PETSC_FALSE; 3407 PetscFunctionReturn(0); 3408 } 3409 3410 #undef __FUNCT__ 3411 #define __FUNCT__ "MatAssemblyEnd" 3412 /*@ 3413 MatAssemblyEnd - Completes assembling the matrix. This routine should 3414 be called after MatAssemblyBegin(). 3415 3416 Collective on Mat 3417 3418 Input Parameters: 3419 + mat - the matrix 3420 - type - type of assembly, either MAT_FLUSH_ASSEMBLY or MAT_FINAL_ASSEMBLY 3421 3422 Options Database Keys: 3423 + -mat_view_info - Prints info on matrix at conclusion of MatEndAssembly() 3424 . -mat_view_info_detailed - Prints more detailed info 3425 . -mat_view - Prints matrix in ASCII format 3426 . -mat_view_matlab - Prints matrix in Matlab format 3427 . -mat_view_draw - PetscDraws nonzero structure of matrix, using MatView() and PetscDrawOpenX(). 3428 . -display <name> - Sets display name (default is host) 3429 . -draw_pause <sec> - Sets number of seconds to pause after display 3430 . -mat_view_socket - Sends matrix to socket, can be accessed from Matlab (see users manual) 3431 . -viewer_socket_machine <machine> 3432 . -viewer_socket_port <port> 3433 . -mat_view_binary - save matrix to file in binary format 3434 - -viewer_binary_filename <name> 3435 3436 Notes: 3437 MatSetValues() generally caches the values. The matrix is ready to 3438 use only after MatAssemblyBegin() and MatAssemblyEnd() have been called. 3439 Use MAT_FLUSH_ASSEMBLY when switching between ADD_VALUES and INSERT_VALUES 3440 in MatSetValues(); use MAT_FINAL_ASSEMBLY for the final assembly before 3441 using the matrix. 3442 3443 Level: beginner 3444 3445 .seealso: MatAssemblyBegin(), MatSetValues(), PetscDrawOpenX(), MatView(), MatAssembled(), PetscViewerSocketOpen() 3446 @*/ 3447 PetscErrorCode MatAssemblyEnd(Mat mat,MatAssemblyType type) 3448 { 3449 PetscErrorCode ierr; 3450 static PetscInt inassm = 0; 3451 PetscTruth flg; 3452 3453 PetscFunctionBegin; 3454 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3455 PetscValidType(mat,1); 3456 MatPreallocated(mat); 3457 3458 inassm++; 3459 MatAssemblyEnd_InUse++; 3460 if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */ 3461 ierr = PetscLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 3462 if (mat->ops->assemblyend) { 3463 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 3464 } 3465 ierr = PetscLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);CHKERRQ(ierr); 3466 } else { 3467 if (mat->ops->assemblyend) { 3468 ierr = (*mat->ops->assemblyend)(mat,type);CHKERRQ(ierr); 3469 } 3470 } 3471 3472 /* Flush assembly is not a true assembly */ 3473 if (type != MAT_FLUSH_ASSEMBLY) { 3474 mat->assembled = PETSC_TRUE; mat->num_ass++; 3475 } 3476 mat->insertmode = NOT_SET_VALUES; 3477 MatAssemblyEnd_InUse--; 3478 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3479 if (!mat->symmetric_eternal) { 3480 mat->symmetric_set = PETSC_FALSE; 3481 mat->hermitian_set = PETSC_FALSE; 3482 mat->structurally_symmetric_set = PETSC_FALSE; 3483 } 3484 if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) { 3485 ierr = MatView_Private(mat);CHKERRQ(ierr); 3486 ierr = PetscOptionsHasName(mat->prefix,"-mat_is_symmetric",&flg);CHKERRQ(ierr); 3487 if (flg) { 3488 PetscReal tol = 0.0; 3489 ierr = PetscOptionsGetReal(mat->prefix,"-mat_is_symmetric",&tol,PETSC_NULL);CHKERRQ(ierr); 3490 ierr = MatIsSymmetric(mat,tol,&flg);CHKERRQ(ierr); 3491 if (flg) { 3492 ierr = PetscPrintf(mat->comm,"Matrix is symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); 3493 } else { 3494 ierr = PetscPrintf(mat->comm,"Matrix is not symmetric (tolerance %g)\n",tol);CHKERRQ(ierr); 3495 } 3496 } 3497 } 3498 inassm--; 3499 ierr = PetscOptionsHasName(mat->prefix,"-help",&flg);CHKERRQ(ierr); 3500 if (flg) { 3501 ierr = MatPrintHelp(mat);CHKERRQ(ierr); 3502 } 3503 PetscFunctionReturn(0); 3504 } 3505 3506 3507 #undef __FUNCT__ 3508 #define __FUNCT__ "MatCompress" 3509 /*@ 3510 MatCompress - Tries to store the matrix in as little space as 3511 possible. May fail if memory is already fully used, since it 3512 tries to allocate new space. 3513 3514 Collective on Mat 3515 3516 Input Parameters: 3517 . mat - the matrix 3518 3519 Level: advanced 3520 3521 @*/ 3522 PetscErrorCode MatCompress(Mat mat) 3523 { 3524 PetscErrorCode ierr; 3525 3526 PetscFunctionBegin; 3527 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3528 PetscValidType(mat,1); 3529 MatPreallocated(mat); 3530 if (mat->ops->compress) {ierr = (*mat->ops->compress)(mat);CHKERRQ(ierr);} 3531 PetscFunctionReturn(0); 3532 } 3533 3534 #undef __FUNCT__ 3535 #define __FUNCT__ "MatSetOption" 3536 /*@ 3537 MatSetOption - Sets a parameter option for a matrix. Some options 3538 may be specific to certain storage formats. Some options 3539 determine how values will be inserted (or added). Sorted, 3540 row-oriented input will generally assemble the fastest. The default 3541 is row-oriented, nonsorted input. 3542 3543 Collective on Mat 3544 3545 Input Parameters: 3546 + mat - the matrix 3547 - option - the option, one of those listed below (and possibly others), 3548 e.g., MAT_ROWS_SORTED, MAT_NEW_NONZERO_LOCATION_ERR 3549 3550 Options Describing Matrix Structure: 3551 + MAT_SYMMETRIC - symmetric in terms of both structure and value 3552 . MAT_HERMITIAN - transpose is the complex conjugation 3553 . MAT_STRUCTURALLY_SYMMETRIC - symmetric nonzero structure 3554 . MAT_NOT_SYMMETRIC - not symmetric in value 3555 . MAT_NOT_HERMITIAN - transpose is not the complex conjugation 3556 . MAT_NOT_STRUCTURALLY_SYMMETRIC - not symmetric nonzero structure 3557 . MAT_SYMMETRY_ETERNAL - if you would like the symmetry/Hermitian flag 3558 you set to be kept with all future use of the matrix 3559 including after MatAssemblyBegin/End() which could 3560 potentially change the symmetry structure, i.e. you 3561 KNOW the matrix will ALWAYS have the property you set. 3562 - MAT_NOT_SYMMETRY_ETERNAL - if MatAssemblyBegin/End() is called then the 3563 flags you set will be dropped (in case potentially 3564 the symmetry etc was lost). 3565 3566 Options For Use with MatSetValues(): 3567 Insert a logically dense subblock, which can be 3568 + MAT_ROW_ORIENTED - row-oriented (default) 3569 . MAT_COLUMN_ORIENTED - column-oriented 3570 . MAT_ROWS_SORTED - sorted by row 3571 . MAT_ROWS_UNSORTED - not sorted by row (default) 3572 . MAT_COLUMNS_SORTED - sorted by column 3573 - MAT_COLUMNS_UNSORTED - not sorted by column (default) 3574 3575 Not these options reflect the data you pass in with MatSetValues(); it has 3576 nothing to do with how the data is stored internally in the matrix 3577 data structure. 3578 3579 When (re)assembling a matrix, we can restrict the input for 3580 efficiency/debugging purposes. These options include 3581 + MAT_NO_NEW_NONZERO_LOCATIONS - additional insertions will not be 3582 allowed if they generate a new nonzero 3583 . MAT_YES_NEW_NONZERO_LOCATIONS - additional insertions will be allowed 3584 . MAT_NO_NEW_DIAGONALS - additional insertions will not be allowed if 3585 they generate a nonzero in a new diagonal (for block diagonal format only) 3586 . MAT_YES_NEW_DIAGONALS - new diagonals will be allowed (for block diagonal format only) 3587 . MAT_IGNORE_OFF_PROC_ENTRIES - drops off-processor entries 3588 . MAT_NEW_NONZERO_LOCATION_ERR - generates an error for new matrix entry 3589 - MAT_USE_HASH_TABLE - uses a hash table to speed up matrix assembly 3590 3591 Notes: 3592 Some options are relevant only for particular matrix types and 3593 are thus ignored by others. Other options are not supported by 3594 certain matrix types and will generate an error message if set. 3595 3596 If using a Fortran 77 module to compute a matrix, one may need to 3597 use the column-oriented option (or convert to the row-oriented 3598 format). 3599 3600 MAT_NO_NEW_NONZERO_LOCATIONS indicates that any add or insertion 3601 that would generate a new entry in the nonzero structure is instead 3602 ignored. Thus, if memory has not alredy been allocated for this particular 3603 data, then the insertion is ignored. For dense matrices, in which 3604 the entire array is allocated, no entries are ever ignored. 3605 Set after the first MatAssemblyEnd() 3606 3607 MAT_NEW_NONZERO_LOCATION_ERR indicates that any add or insertion 3608 that would generate a new entry in the nonzero structure instead produces 3609 an error. (Currently supported for AIJ and BAIJ formats only.) 3610 This is a useful flag when using SAME_NONZERO_PATTERN in calling 3611 KSPSetOperators() to ensure that the nonzero pattern truely does 3612 remain unchanged. Set after the first MatAssemblyEnd() 3613 3614 MAT_NEW_NONZERO_ALLOCATION_ERR indicates that any add or insertion 3615 that would generate a new entry that has not been preallocated will 3616 instead produce an error. (Currently supported for AIJ and BAIJ formats 3617 only.) This is a useful flag when debugging matrix memory preallocation. 3618 3619 MAT_IGNORE_OFF_PROC_ENTRIES indicates entries destined for 3620 other processors should be dropped, rather than stashed. 3621 This is useful if you know that the "owning" processor is also 3622 always generating the correct matrix entries, so that PETSc need 3623 not transfer duplicate entries generated on another processor. 3624 3625 MAT_USE_HASH_TABLE indicates that a hash table be used to improve the 3626 searches during matrix assembly. When this flag is set, the hash table 3627 is created during the first Matrix Assembly. This hash table is 3628 used the next time through, during MatSetVaules()/MatSetVaulesBlocked() 3629 to improve the searching of indices. MAT_NO_NEW_NONZERO_LOCATIONS flag 3630 should be used with MAT_USE_HASH_TABLE flag. This option is currently 3631 supported by MATMPIBAIJ format only. 3632 3633 MAT_KEEP_ZEROED_ROWS indicates when MatZeroRows() is called the zeroed entries 3634 are kept in the nonzero structure 3635 3636 MAT_IGNORE_ZERO_ENTRIES - for AIJ matrices this will stop zero values from creating 3637 a zero location in the matrix 3638 3639 MAT_USE_INODES - indicates using inode version of the code - works with AIJ and 3640 ROWBS matrix types 3641 3642 MAT_DO_NOT_USE_INODES - indicates not using inode version of the code - works 3643 with AIJ and ROWBS matrix types 3644 3645 Level: intermediate 3646 3647 Concepts: matrices^setting options 3648 3649 @*/ 3650 PetscErrorCode MatSetOption(Mat mat,MatOption op) 3651 { 3652 PetscErrorCode ierr; 3653 3654 PetscFunctionBegin; 3655 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3656 PetscValidType(mat,1); 3657 MatPreallocated(mat); 3658 switch (op) { 3659 case MAT_SYMMETRIC: 3660 mat->symmetric = PETSC_TRUE; 3661 mat->structurally_symmetric = PETSC_TRUE; 3662 mat->symmetric_set = PETSC_TRUE; 3663 mat->structurally_symmetric_set = PETSC_TRUE; 3664 break; 3665 case MAT_HERMITIAN: 3666 mat->hermitian = PETSC_TRUE; 3667 mat->structurally_symmetric = PETSC_TRUE; 3668 mat->hermitian_set = PETSC_TRUE; 3669 mat->structurally_symmetric_set = PETSC_TRUE; 3670 break; 3671 case MAT_STRUCTURALLY_SYMMETRIC: 3672 mat->structurally_symmetric = PETSC_TRUE; 3673 mat->structurally_symmetric_set = PETSC_TRUE; 3674 break; 3675 case MAT_NOT_SYMMETRIC: 3676 mat->symmetric = PETSC_FALSE; 3677 mat->symmetric_set = PETSC_TRUE; 3678 break; 3679 case MAT_NOT_HERMITIAN: 3680 mat->hermitian = PETSC_FALSE; 3681 mat->hermitian_set = PETSC_TRUE; 3682 break; 3683 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 3684 mat->structurally_symmetric = PETSC_FALSE; 3685 mat->structurally_symmetric_set = PETSC_TRUE; 3686 break; 3687 case MAT_SYMMETRY_ETERNAL: 3688 mat->symmetric_eternal = PETSC_TRUE; 3689 break; 3690 case MAT_NOT_SYMMETRY_ETERNAL: 3691 mat->symmetric_eternal = PETSC_FALSE; 3692 break; 3693 default: 3694 break; 3695 } 3696 if (mat->ops->setoption) { 3697 ierr = (*mat->ops->setoption)(mat,op);CHKERRQ(ierr); 3698 } 3699 PetscFunctionReturn(0); 3700 } 3701 3702 #undef __FUNCT__ 3703 #define __FUNCT__ "MatZeroEntries" 3704 /*@ 3705 MatZeroEntries - Zeros all entries of a matrix. For sparse matrices 3706 this routine retains the old nonzero structure. 3707 3708 Collective on Mat 3709 3710 Input Parameters: 3711 . mat - the matrix 3712 3713 Level: intermediate 3714 3715 Concepts: matrices^zeroing 3716 3717 .seealso: MatZeroRows() 3718 @*/ 3719 PetscErrorCode MatZeroEntries(Mat mat) 3720 { 3721 PetscErrorCode ierr; 3722 3723 PetscFunctionBegin; 3724 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3725 PetscValidType(mat,1); 3726 MatPreallocated(mat); 3727 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3728 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3729 if (!mat->ops->zeroentries) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3730 3731 ierr = PetscLogEventBegin(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 3732 ierr = (*mat->ops->zeroentries)(mat);CHKERRQ(ierr); 3733 ierr = PetscLogEventEnd(MAT_ZeroEntries,mat,0,0,0);CHKERRQ(ierr); 3734 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3735 PetscFunctionReturn(0); 3736 } 3737 3738 #undef __FUNCT__ 3739 #define __FUNCT__ "MatZeroRows" 3740 /*@C 3741 MatZeroRows - Zeros all entries (except possibly the main diagonal) 3742 of a set of rows of a matrix. 3743 3744 Collective on Mat 3745 3746 Input Parameters: 3747 + mat - the matrix 3748 . is - index set of rows to remove 3749 - diag - pointer to value put in all diagonals of eliminated rows. 3750 Note that diag is not a pointer to an array, but merely a 3751 pointer to a single value. 3752 3753 Notes: 3754 For the AIJ and BAIJ matrix formats this removes the old nonzero structure, 3755 but does not release memory. For the dense and block diagonal 3756 formats this does not alter the nonzero structure. 3757 3758 If the option MatSetOption(mat,MAT_KEEP_ZEROED_ROWS) the nonzero structure 3759 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 3760 merely zeroed. 3761 3762 The user can set a value in the diagonal entry (or for the AIJ and 3763 row formats can optionally remove the main diagonal entry from the 3764 nonzero structure as well, by passing a null pointer (PETSC_NULL 3765 in C or PETSC_NULL_SCALAR in Fortran) as the final argument). 3766 3767 For the parallel case, all processes that share the matrix (i.e., 3768 those in the communicator used for matrix creation) MUST call this 3769 routine, regardless of whether any rows being zeroed are owned by 3770 them. 3771 3772 Level: intermediate 3773 3774 Concepts: matrices^zeroing rows 3775 3776 .seealso: MatZeroEntries(), MatZeroRowsLocal(), MatSetOption() 3777 @*/ 3778 PetscErrorCode MatZeroRows(Mat mat,IS is,const PetscScalar *diag) 3779 { 3780 PetscErrorCode ierr; 3781 3782 PetscFunctionBegin; 3783 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3784 PetscValidType(mat,1); 3785 MatPreallocated(mat); 3786 PetscValidHeaderSpecific(is,IS_COOKIE,2); 3787 if (diag) PetscValidScalarPointer(diag,3); 3788 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3789 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3790 if (!mat->ops->zerorows) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 3791 3792 ierr = (*mat->ops->zerorows)(mat,is,diag);CHKERRQ(ierr); 3793 ierr = MatView_Private(mat);CHKERRQ(ierr); 3794 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3795 PetscFunctionReturn(0); 3796 } 3797 3798 #undef __FUNCT__ 3799 #define __FUNCT__ "MatZeroRowsLocal" 3800 /*@C 3801 MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal) 3802 of a set of rows of a matrix; using local numbering of rows. 3803 3804 Collective on Mat 3805 3806 Input Parameters: 3807 + mat - the matrix 3808 . is - index set of rows to remove 3809 - diag - pointer to value put in all diagonals of eliminated rows. 3810 Note that diag is not a pointer to an array, but merely a 3811 pointer to a single value. 3812 3813 Notes: 3814 Before calling MatZeroRowsLocal(), the user must first set the 3815 local-to-global mapping by calling MatSetLocalToGlobalMapping(). 3816 3817 For the AIJ matrix formats this removes the old nonzero structure, 3818 but does not release memory. For the dense and block diagonal 3819 formats this does not alter the nonzero structure. 3820 3821 If the option MatSetOption(mat,MAT_KEEP_ZEROED_ROWS) the nonzero structure 3822 of the matrix is not changed (even for AIJ and BAIJ matrices) the values are 3823 merely zeroed. 3824 3825 The user can set a value in the diagonal entry (or for the AIJ and 3826 row formats can optionally remove the main diagonal entry from the 3827 nonzero structure as well, by passing a null pointer (PETSC_NULL 3828 in C or PETSC_NULL_SCALAR in Fortran) as the final argument). 3829 3830 Level: intermediate 3831 3832 Concepts: matrices^zeroing 3833 3834 .seealso: MatZeroEntries(), MatZeroRows(), MatSetLocalToGlobalMapping 3835 @*/ 3836 PetscErrorCode MatZeroRowsLocal(Mat mat,IS is,const PetscScalar *diag) 3837 { 3838 PetscErrorCode ierr; 3839 IS newis; 3840 3841 PetscFunctionBegin; 3842 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3843 PetscValidType(mat,1); 3844 MatPreallocated(mat); 3845 PetscValidHeaderSpecific(is,IS_COOKIE,2); 3846 if (diag) PetscValidScalarPointer(diag,3); 3847 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 3848 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 3849 3850 if (mat->ops->zerorowslocal) { 3851 ierr = (*mat->ops->zerorowslocal)(mat,is,diag);CHKERRQ(ierr); 3852 } else { 3853 if (!mat->mapping) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Need to provide local to global mapping to matrix first"); 3854 ierr = ISLocalToGlobalMappingApplyIS(mat->mapping,is,&newis);CHKERRQ(ierr); 3855 ierr = (*mat->ops->zerorows)(mat,newis,diag);CHKERRQ(ierr); 3856 ierr = ISDestroy(newis);CHKERRQ(ierr); 3857 } 3858 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 3859 PetscFunctionReturn(0); 3860 } 3861 3862 #undef __FUNCT__ 3863 #define __FUNCT__ "MatGetSize" 3864 /*@ 3865 MatGetSize - Returns the numbers of rows and columns in a matrix. 3866 3867 Not Collective 3868 3869 Input Parameter: 3870 . mat - the matrix 3871 3872 Output Parameters: 3873 + m - the number of global rows 3874 - n - the number of global columns 3875 3876 Level: beginner 3877 3878 Concepts: matrices^size 3879 3880 .seealso: MatGetLocalSize() 3881 @*/ 3882 PetscErrorCode MatGetSize(Mat mat,PetscInt *m,PetscInt* n) 3883 { 3884 PetscFunctionBegin; 3885 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3886 if (m) *m = mat->M; 3887 if (n) *n = mat->N; 3888 PetscFunctionReturn(0); 3889 } 3890 3891 #undef __FUNCT__ 3892 #define __FUNCT__ "MatGetLocalSize" 3893 /*@ 3894 MatGetLocalSize - Returns the number of rows and columns in a matrix 3895 stored locally. This information may be implementation dependent, so 3896 use with care. 3897 3898 Not Collective 3899 3900 Input Parameters: 3901 . mat - the matrix 3902 3903 Output Parameters: 3904 + m - the number of local rows 3905 - n - the number of local columns 3906 3907 Level: beginner 3908 3909 Concepts: matrices^local size 3910 3911 .seealso: MatGetSize() 3912 @*/ 3913 PetscErrorCode MatGetLocalSize(Mat mat,PetscInt *m,PetscInt* n) 3914 { 3915 PetscFunctionBegin; 3916 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3917 if (m) PetscValidIntPointer(m,2); 3918 if (n) PetscValidIntPointer(n,3); 3919 if (m) *m = mat->m; 3920 if (n) *n = mat->n; 3921 PetscFunctionReturn(0); 3922 } 3923 3924 #undef __FUNCT__ 3925 #define __FUNCT__ "MatGetOwnershipRange" 3926 /*@ 3927 MatGetOwnershipRange - Returns the range of matrix rows owned by 3928 this processor, assuming that the matrix is laid out with the first 3929 n1 rows on the first processor, the next n2 rows on the second, etc. 3930 For certain parallel layouts this range may not be well defined. 3931 3932 Not Collective 3933 3934 Input Parameters: 3935 . mat - the matrix 3936 3937 Output Parameters: 3938 + m - the global index of the first local row 3939 - n - one more than the global index of the last local row 3940 3941 Level: beginner 3942 3943 Concepts: matrices^row ownership 3944 @*/ 3945 PetscErrorCode MatGetOwnershipRange(Mat mat,PetscInt *m,PetscInt* n) 3946 { 3947 PetscErrorCode ierr; 3948 3949 PetscFunctionBegin; 3950 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 3951 PetscValidType(mat,1); 3952 MatPreallocated(mat); 3953 if (m) PetscValidIntPointer(m,2); 3954 if (n) PetscValidIntPointer(n,3); 3955 ierr = PetscMapGetLocalRange(mat->rmap,m,n);CHKERRQ(ierr); 3956 PetscFunctionReturn(0); 3957 } 3958 3959 #undef __FUNCT__ 3960 #define __FUNCT__ "MatILUFactorSymbolic" 3961 /*@ 3962 MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix. 3963 Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric() 3964 to complete the factorization. 3965 3966 Collective on Mat 3967 3968 Input Parameters: 3969 + mat - the matrix 3970 . row - row permutation 3971 . column - column permutation 3972 - info - structure containing 3973 $ levels - number of levels of fill. 3974 $ expected fill - as ratio of original fill. 3975 $ 1 or 0 - indicating force fill on diagonal (improves robustness for matrices 3976 missing diagonal entries) 3977 3978 Output Parameters: 3979 . fact - new matrix that has been symbolically factored 3980 3981 Notes: 3982 See the users manual for additional information about 3983 choosing the fill factor for better efficiency. 3984 3985 Most users should employ the simplified KSP interface for linear solvers 3986 instead of working directly with matrix algebra routines such as this. 3987 See, e.g., KSPCreate(). 3988 3989 Level: developer 3990 3991 Concepts: matrices^symbolic LU factorization 3992 Concepts: matrices^factorization 3993 Concepts: LU^symbolic factorization 3994 3995 .seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 3996 MatGetOrdering(), MatFactorInfo 3997 3998 @*/ 3999 PetscErrorCode MatILUFactorSymbolic(Mat mat,IS row,IS col,MatFactorInfo *info,Mat *fact) 4000 { 4001 PetscErrorCode ierr; 4002 4003 PetscFunctionBegin; 4004 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4005 PetscValidType(mat,1); 4006 MatPreallocated(mat); 4007 PetscValidHeaderSpecific(row,IS_COOKIE,2); 4008 PetscValidHeaderSpecific(col,IS_COOKIE,3); 4009 PetscValidPointer(info,4); 4010 PetscValidPointer(fact,5); 4011 if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels of fill negative %d",(int)info->levels); 4012 if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); 4013 if (!mat->ops->ilufactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ILU",mat->type_name); 4014 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4015 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4016 4017 ierr = PetscLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 4018 ierr = (*mat->ops->ilufactorsymbolic)(mat,row,col,info,fact);CHKERRQ(ierr); 4019 ierr = PetscLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);CHKERRQ(ierr); 4020 PetscFunctionReturn(0); 4021 } 4022 4023 #undef __FUNCT__ 4024 #define __FUNCT__ "MatICCFactorSymbolic" 4025 /*@ 4026 MatICCFactorSymbolic - Performs symbolic incomplete 4027 Cholesky factorization for a symmetric matrix. Use 4028 MatCholeskyFactorNumeric() to complete the factorization. 4029 4030 Collective on Mat 4031 4032 Input Parameters: 4033 + mat - the matrix 4034 . perm - row and column permutation 4035 - info - structure containing 4036 $ levels - number of levels of fill. 4037 $ expected fill - as ratio of original fill. 4038 4039 Output Parameter: 4040 . fact - the factored matrix 4041 4042 Notes: 4043 Currently only no-fill factorization is supported. 4044 4045 Most users should employ the simplified KSP interface for linear solvers 4046 instead of working directly with matrix algebra routines such as this. 4047 See, e.g., KSPCreate(). 4048 4049 Level: developer 4050 4051 Concepts: matrices^symbolic incomplete Cholesky factorization 4052 Concepts: matrices^factorization 4053 Concepts: Cholsky^symbolic factorization 4054 4055 .seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor(), MatFactorInfo 4056 @*/ 4057 PetscErrorCode MatICCFactorSymbolic(Mat mat,IS perm,MatFactorInfo *info,Mat *fact) 4058 { 4059 PetscErrorCode ierr; 4060 4061 PetscFunctionBegin; 4062 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4063 PetscValidType(mat,1); 4064 MatPreallocated(mat); 4065 PetscValidHeaderSpecific(perm,IS_COOKIE,2); 4066 PetscValidPointer(info,3); 4067 PetscValidPointer(fact,4); 4068 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4069 if (info->levels < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Levels negative %d",(int) info->levels); 4070 if (info->fill < 1.0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Expected fill less than 1.0 %g",info->fill); 4071 if (!mat->ops->iccfactorsymbolic) SETERRQ1(PETSC_ERR_SUP,"Matrix type %s symbolic ICC",mat->type_name); 4072 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4073 4074 ierr = PetscLogEventBegin(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 4075 ierr = (*mat->ops->iccfactorsymbolic)(mat,perm,info,fact);CHKERRQ(ierr); 4076 ierr = PetscLogEventEnd(MAT_ICCFactorSymbolic,mat,perm,0,0);CHKERRQ(ierr); 4077 PetscFunctionReturn(0); 4078 } 4079 4080 #undef __FUNCT__ 4081 #define __FUNCT__ "MatGetArray" 4082 /*@C 4083 MatGetArray - Returns a pointer to the element values in the matrix. 4084 The result of this routine is dependent on the underlying matrix data 4085 structure, and may not even work for certain matrix types. You MUST 4086 call MatRestoreArray() when you no longer need to access the array. 4087 4088 Not Collective 4089 4090 Input Parameter: 4091 . mat - the matrix 4092 4093 Output Parameter: 4094 . v - the location of the values 4095 4096 4097 Fortran Note: 4098 This routine is used differently from Fortran, e.g., 4099 .vb 4100 Mat mat 4101 PetscScalar mat_array(1) 4102 PetscOffset i_mat 4103 PetscErrorCode ierr 4104 call MatGetArray(mat,mat_array,i_mat,ierr) 4105 4106 C Access first local entry in matrix; note that array is 4107 C treated as one dimensional 4108 value = mat_array(i_mat + 1) 4109 4110 [... other code ...] 4111 call MatRestoreArray(mat,mat_array,i_mat,ierr) 4112 .ve 4113 4114 See the Fortran chapter of the users manual and 4115 petsc/src/mat/examples/tests for details. 4116 4117 Level: advanced 4118 4119 Concepts: matrices^access array 4120 4121 .seealso: MatRestoreArray(), MatGetArrayF90() 4122 @*/ 4123 PetscErrorCode MatGetArray(Mat mat,PetscScalar *v[]) 4124 { 4125 PetscErrorCode ierr; 4126 4127 PetscFunctionBegin; 4128 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4129 PetscValidType(mat,1); 4130 MatPreallocated(mat); 4131 PetscValidPointer(v,2); 4132 if (!mat->ops->getarray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4133 ierr = (*mat->ops->getarray)(mat,v);CHKERRQ(ierr); 4134 PetscFunctionReturn(0); 4135 } 4136 4137 #undef __FUNCT__ 4138 #define __FUNCT__ "MatRestoreArray" 4139 /*@C 4140 MatRestoreArray - Restores the matrix after MatGetArray() has been called. 4141 4142 Not Collective 4143 4144 Input Parameter: 4145 + mat - the matrix 4146 - v - the location of the values 4147 4148 Fortran Note: 4149 This routine is used differently from Fortran, e.g., 4150 .vb 4151 Mat mat 4152 PetscScalar mat_array(1) 4153 PetscOffset i_mat 4154 PetscErrorCode ierr 4155 call MatGetArray(mat,mat_array,i_mat,ierr) 4156 4157 C Access first local entry in matrix; note that array is 4158 C treated as one dimensional 4159 value = mat_array(i_mat + 1) 4160 4161 [... other code ...] 4162 call MatRestoreArray(mat,mat_array,i_mat,ierr) 4163 .ve 4164 4165 See the Fortran chapter of the users manual and 4166 petsc/src/mat/examples/tests for details 4167 4168 Level: advanced 4169 4170 .seealso: MatGetArray(), MatRestoreArrayF90() 4171 @*/ 4172 PetscErrorCode MatRestoreArray(Mat mat,PetscScalar *v[]) 4173 { 4174 PetscErrorCode ierr; 4175 4176 PetscFunctionBegin; 4177 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4178 PetscValidType(mat,1); 4179 MatPreallocated(mat); 4180 PetscValidPointer(v,2); 4181 #if defined(PETSC_USE_BOPT_g) 4182 CHKMEMQ; 4183 #endif 4184 if (!mat->ops->restorearray) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4185 ierr = (*mat->ops->restorearray)(mat,v);CHKERRQ(ierr); 4186 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 4187 PetscFunctionReturn(0); 4188 } 4189 4190 #undef __FUNCT__ 4191 #define __FUNCT__ "MatGetSubMatrices" 4192 /*@C 4193 MatGetSubMatrices - Extracts several submatrices from a matrix. If submat 4194 points to an array of valid matrices, they may be reused to store the new 4195 submatrices. 4196 4197 Collective on Mat 4198 4199 Input Parameters: 4200 + mat - the matrix 4201 . n - the number of submatrixes to be extracted (on this processor, may be zero) 4202 . irow, icol - index sets of rows and columns to extract 4203 - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 4204 4205 Output Parameter: 4206 . submat - the array of submatrices 4207 4208 Notes: 4209 MatGetSubMatrices() can extract only sequential submatrices 4210 (from both sequential and parallel matrices). Use MatGetSubMatrix() 4211 to extract a parallel submatrix. 4212 4213 When extracting submatrices from a parallel matrix, each processor can 4214 form a different submatrix by setting the rows and columns of its 4215 individual index sets according to the local submatrix desired. 4216 4217 When finished using the submatrices, the user should destroy 4218 them with MatDestroyMatrices(). 4219 4220 MAT_REUSE_MATRIX can only be used when the nonzero structure of the 4221 original matrix has not changed from that last call to MatGetSubMatrices(). 4222 4223 This routine creates the matrices in submat; you should NOT create them before 4224 calling it. It also allocates the array of matrix pointers submat. 4225 4226 Fortran Note: 4227 The Fortran interface is slightly different from that given below; it 4228 requires one to pass in as submat a Mat (integer) array of size at least m. 4229 4230 Level: advanced 4231 4232 Concepts: matrices^accessing submatrices 4233 Concepts: submatrices 4234 4235 .seealso: MatDestroyMatrices(), MatGetSubMatrix(), MatGetRow(), MatGetDiagonal() 4236 @*/ 4237 PetscErrorCode MatGetSubMatrices(Mat mat,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *submat[]) 4238 { 4239 PetscErrorCode ierr; 4240 PetscInt i; 4241 PetscTruth eq; 4242 4243 PetscFunctionBegin; 4244 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4245 PetscValidType(mat,1); 4246 MatPreallocated(mat); 4247 if (n) { 4248 PetscValidPointer(irow,3); 4249 PetscValidHeaderSpecific(*irow,IS_COOKIE,3); 4250 PetscValidPointer(icol,4); 4251 PetscValidHeaderSpecific(*icol,IS_COOKIE,4); 4252 } 4253 PetscValidPointer(submat,6); 4254 if (n && scall == MAT_REUSE_MATRIX) { 4255 PetscValidPointer(*submat,6); 4256 PetscValidHeaderSpecific(**submat,MAT_COOKIE,6); 4257 } 4258 if (!mat->ops->getsubmatrices) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4259 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4260 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4261 4262 ierr = PetscLogEventBegin(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 4263 ierr = (*mat->ops->getsubmatrices)(mat,n,irow,icol,scall,submat);CHKERRQ(ierr); 4264 ierr = PetscLogEventEnd(MAT_GetSubMatrices,mat,0,0,0);CHKERRQ(ierr); 4265 for (i=0; i<n; i++) { 4266 if (mat->symmetric || mat->structurally_symmetric || mat->hermitian) { 4267 ierr = ISEqual(irow[i],icol[i],&eq);CHKERRQ(ierr); 4268 if (eq) { 4269 if (mat->symmetric){ 4270 ierr = MatSetOption((*submat)[i],MAT_SYMMETRIC);CHKERRQ(ierr); 4271 } else if (mat->hermitian) { 4272 ierr = MatSetOption((*submat)[i],MAT_HERMITIAN);CHKERRQ(ierr); 4273 } else if (mat->structurally_symmetric) { 4274 ierr = MatSetOption((*submat)[i],MAT_STRUCTURALLY_SYMMETRIC);CHKERRQ(ierr); 4275 } 4276 } 4277 } 4278 } 4279 PetscFunctionReturn(0); 4280 } 4281 4282 #undef __FUNCT__ 4283 #define __FUNCT__ "MatDestroyMatrices" 4284 /*@C 4285 MatDestroyMatrices - Destroys a set of matrices obtained with MatGetSubMatrices(). 4286 4287 Collective on Mat 4288 4289 Input Parameters: 4290 + n - the number of local matrices 4291 - mat - the matrices (note that this is a pointer to the array of matrices, just to match the calling 4292 sequence of MatGetSubMatrices()) 4293 4294 Level: advanced 4295 4296 Notes: Frees not only the matrices, but also the array that contains the matrices 4297 4298 .seealso: MatGetSubMatrices() 4299 @*/ 4300 PetscErrorCode MatDestroyMatrices(PetscInt n,Mat *mat[]) 4301 { 4302 PetscErrorCode ierr; 4303 PetscInt i; 4304 4305 PetscFunctionBegin; 4306 if (n < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Trying to destroy negative number of matrices %d",n); 4307 PetscValidPointer(mat,2); 4308 for (i=0; i<n; i++) { 4309 ierr = MatDestroy((*mat)[i]);CHKERRQ(ierr); 4310 } 4311 /* memory is allocated even if n = 0 */ 4312 ierr = PetscFree(*mat);CHKERRQ(ierr); 4313 PetscFunctionReturn(0); 4314 } 4315 4316 #undef __FUNCT__ 4317 #define __FUNCT__ "MatIncreaseOverlap" 4318 /*@ 4319 MatIncreaseOverlap - Given a set of submatrices indicated by index sets, 4320 replaces the index sets by larger ones that represent submatrices with 4321 additional overlap. 4322 4323 Collective on Mat 4324 4325 Input Parameters: 4326 + mat - the matrix 4327 . n - the number of index sets 4328 . is - the array of index sets (these index sets will changed during the call) 4329 - ov - the additional overlap requested 4330 4331 Level: developer 4332 4333 Concepts: overlap 4334 Concepts: ASM^computing overlap 4335 4336 .seealso: MatGetSubMatrices() 4337 @*/ 4338 PetscErrorCode MatIncreaseOverlap(Mat mat,PetscInt n,IS is[],PetscInt ov) 4339 { 4340 PetscErrorCode ierr; 4341 4342 PetscFunctionBegin; 4343 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4344 PetscValidType(mat,1); 4345 MatPreallocated(mat); 4346 if (n < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more domains, you have %d",n); 4347 if (n) { 4348 PetscValidPointer(is,3); 4349 PetscValidHeaderSpecific(*is,IS_COOKIE,3); 4350 } 4351 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 4352 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4353 4354 if (!ov) PetscFunctionReturn(0); 4355 if (!mat->ops->increaseoverlap) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4356 ierr = PetscLogEventBegin(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 4357 ierr = (*mat->ops->increaseoverlap)(mat,n,is,ov);CHKERRQ(ierr); 4358 ierr = PetscLogEventEnd(MAT_IncreaseOverlap,mat,0,0,0);CHKERRQ(ierr); 4359 PetscFunctionReturn(0); 4360 } 4361 4362 #undef __FUNCT__ 4363 #define __FUNCT__ "MatPrintHelp" 4364 /*@ 4365 MatPrintHelp - Prints all the options for the matrix. 4366 4367 Collective on Mat 4368 4369 Input Parameter: 4370 . mat - the matrix 4371 4372 Options Database Keys: 4373 + -help - Prints matrix options 4374 - -h - Prints matrix options 4375 4376 Level: developer 4377 4378 .seealso: MatCreate(), MatCreateXXX() 4379 @*/ 4380 PetscErrorCode MatPrintHelp(Mat mat) 4381 { 4382 static PetscTruth called = PETSC_FALSE; 4383 PetscErrorCode ierr; 4384 4385 PetscFunctionBegin; 4386 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4387 PetscValidType(mat,1); 4388 MatPreallocated(mat); 4389 4390 if (!called) { 4391 if (mat->ops->printhelp) { 4392 ierr = (*mat->ops->printhelp)(mat);CHKERRQ(ierr); 4393 } 4394 called = PETSC_TRUE; 4395 } 4396 PetscFunctionReturn(0); 4397 } 4398 4399 #undef __FUNCT__ 4400 #define __FUNCT__ "MatGetBlockSize" 4401 /*@ 4402 MatGetBlockSize - Returns the matrix block size; useful especially for the 4403 block row and block diagonal formats. 4404 4405 Not Collective 4406 4407 Input Parameter: 4408 . mat - the matrix 4409 4410 Output Parameter: 4411 . bs - block size 4412 4413 Notes: 4414 Block diagonal formats are MATSEQBDIAG, MATMPIBDIAG. 4415 Block row formats are MATSEQBAIJ, MATMPIBAIJ, MATSEQSBAIJ, MATMPISBAIJ 4416 4417 Level: intermediate 4418 4419 Concepts: matrices^block size 4420 4421 .seealso: MatCreateSeqBAIJ(), MatCreateMPIBAIJ(), MatCreateSeqBDiag(), MatCreateMPIBDiag() 4422 @*/ 4423 PetscErrorCode MatGetBlockSize(Mat mat,PetscInt *bs) 4424 { 4425 PetscErrorCode ierr; 4426 4427 PetscFunctionBegin; 4428 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4429 PetscValidType(mat,1); 4430 MatPreallocated(mat); 4431 PetscValidIntPointer(bs,2); 4432 if (!mat->ops->getblocksize) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4433 ierr = (*mat->ops->getblocksize)(mat,bs);CHKERRQ(ierr); 4434 PetscFunctionReturn(0); 4435 } 4436 4437 #undef __FUNCT__ 4438 #define __FUNCT__ "MatGetRowIJ" 4439 /*@C 4440 MatGetRowIJ - Returns the compressed row storage i and j indices for sequential matrices. 4441 4442 Collective on Mat 4443 4444 Input Parameters: 4445 + mat - the matrix 4446 . shift - 0 or 1 indicating we want the indices starting at 0 or 1 4447 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4448 symmetrized 4449 4450 Output Parameters: 4451 + n - number of rows in the (possibly compressed) matrix 4452 . ia - the row pointers 4453 . ja - the column indices 4454 - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned 4455 4456 Level: developer 4457 4458 .seealso: MatGetColumnIJ(), MatRestoreRowIJ() 4459 @*/ 4460 PetscErrorCode MatGetRowIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4461 { 4462 PetscErrorCode ierr; 4463 4464 PetscFunctionBegin; 4465 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4466 PetscValidType(mat,1); 4467 MatPreallocated(mat); 4468 PetscValidIntPointer(n,4); 4469 if (ia) PetscValidIntPointer(ia,5); 4470 if (ja) PetscValidIntPointer(ja,6); 4471 PetscValidIntPointer(done,7); 4472 if (!mat->ops->getrowij) *done = PETSC_FALSE; 4473 else { 4474 *done = PETSC_TRUE; 4475 ierr = (*mat->ops->getrowij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4476 } 4477 PetscFunctionReturn(0); 4478 } 4479 4480 #undef __FUNCT__ 4481 #define __FUNCT__ "MatGetColumnIJ" 4482 /*@C 4483 MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices. 4484 4485 Collective on Mat 4486 4487 Input Parameters: 4488 + mat - the matrix 4489 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4490 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4491 symmetrized 4492 4493 Output Parameters: 4494 + n - number of columns in the (possibly compressed) matrix 4495 . ia - the column pointers 4496 . ja - the row indices 4497 - done - PETSC_TRUE or PETSC_FALSE, indicating whether the values have been returned 4498 4499 Level: developer 4500 4501 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 4502 @*/ 4503 PetscErrorCode MatGetColumnIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4504 { 4505 PetscErrorCode ierr; 4506 4507 PetscFunctionBegin; 4508 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4509 PetscValidType(mat,1); 4510 MatPreallocated(mat); 4511 PetscValidIntPointer(n,4); 4512 if (ia) PetscValidIntPointer(ia,5); 4513 if (ja) PetscValidIntPointer(ja,6); 4514 PetscValidIntPointer(done,7); 4515 4516 if (!mat->ops->getcolumnij) *done = PETSC_FALSE; 4517 else { 4518 *done = PETSC_TRUE; 4519 ierr = (*mat->ops->getcolumnij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4520 } 4521 PetscFunctionReturn(0); 4522 } 4523 4524 #undef __FUNCT__ 4525 #define __FUNCT__ "MatRestoreRowIJ" 4526 /*@C 4527 MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with 4528 MatGetRowIJ(). 4529 4530 Collective on Mat 4531 4532 Input Parameters: 4533 + mat - the matrix 4534 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4535 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4536 symmetrized 4537 4538 Output Parameters: 4539 + n - size of (possibly compressed) matrix 4540 . ia - the row pointers 4541 . ja - the column indices 4542 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 4543 4544 Level: developer 4545 4546 .seealso: MatGetRowIJ(), MatRestoreColumnIJ() 4547 @*/ 4548 PetscErrorCode MatRestoreRowIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4549 { 4550 PetscErrorCode ierr; 4551 4552 PetscFunctionBegin; 4553 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4554 PetscValidType(mat,1); 4555 MatPreallocated(mat); 4556 if (ia) PetscValidIntPointer(ia,5); 4557 if (ja) PetscValidIntPointer(ja,6); 4558 PetscValidIntPointer(done,7); 4559 4560 if (!mat->ops->restorerowij) *done = PETSC_FALSE; 4561 else { 4562 *done = PETSC_TRUE; 4563 ierr = (*mat->ops->restorerowij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4564 } 4565 PetscFunctionReturn(0); 4566 } 4567 4568 #undef __FUNCT__ 4569 #define __FUNCT__ "MatRestoreColumnIJ" 4570 /*@C 4571 MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with 4572 MatGetColumnIJ(). 4573 4574 Collective on Mat 4575 4576 Input Parameters: 4577 + mat - the matrix 4578 . shift - 1 or zero indicating we want the indices starting at 0 or 1 4579 - symmetric - PETSC_TRUE or PETSC_FALSE indicating the matrix data structure should be 4580 symmetrized 4581 4582 Output Parameters: 4583 + n - size of (possibly compressed) matrix 4584 . ia - the column pointers 4585 . ja - the row indices 4586 - done - PETSC_TRUE or PETSC_FALSE indicated that the values have been returned 4587 4588 Level: developer 4589 4590 .seealso: MatGetColumnIJ(), MatRestoreRowIJ() 4591 @*/ 4592 PetscErrorCode MatRestoreColumnIJ(Mat mat,PetscInt shift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt* ja[],PetscTruth *done) 4593 { 4594 PetscErrorCode ierr; 4595 4596 PetscFunctionBegin; 4597 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4598 PetscValidType(mat,1); 4599 MatPreallocated(mat); 4600 if (ia) PetscValidIntPointer(ia,5); 4601 if (ja) PetscValidIntPointer(ja,6); 4602 PetscValidIntPointer(done,7); 4603 4604 if (!mat->ops->restorecolumnij) *done = PETSC_FALSE; 4605 else { 4606 *done = PETSC_TRUE; 4607 ierr = (*mat->ops->restorecolumnij)(mat,shift,symmetric,n,ia,ja,done);CHKERRQ(ierr); 4608 } 4609 PetscFunctionReturn(0); 4610 } 4611 4612 #undef __FUNCT__ 4613 #define __FUNCT__ "MatColoringPatch" 4614 /*@C 4615 MatColoringPatch -Used inside matrix coloring routines that 4616 use MatGetRowIJ() and/or MatGetColumnIJ(). 4617 4618 Collective on Mat 4619 4620 Input Parameters: 4621 + mat - the matrix 4622 . n - number of colors 4623 - colorarray - array indicating color for each column 4624 4625 Output Parameters: 4626 . iscoloring - coloring generated using colorarray information 4627 4628 Level: developer 4629 4630 .seealso: MatGetRowIJ(), MatGetColumnIJ() 4631 4632 @*/ 4633 PetscErrorCode MatColoringPatch(Mat mat,PetscInt n,PetscInt ncolors,ISColoringValue colorarray[],ISColoring *iscoloring) 4634 { 4635 PetscErrorCode ierr; 4636 4637 PetscFunctionBegin; 4638 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4639 PetscValidType(mat,1); 4640 MatPreallocated(mat); 4641 PetscValidIntPointer(colorarray,4); 4642 PetscValidPointer(iscoloring,5); 4643 4644 if (!mat->ops->coloringpatch){ 4645 ierr = ISColoringCreate(mat->comm,n,colorarray,iscoloring);CHKERRQ(ierr); 4646 } else { 4647 ierr = (*mat->ops->coloringpatch)(mat,n,ncolors,colorarray,iscoloring);CHKERRQ(ierr); 4648 } 4649 PetscFunctionReturn(0); 4650 } 4651 4652 4653 #undef __FUNCT__ 4654 #define __FUNCT__ "MatSetUnfactored" 4655 /*@ 4656 MatSetUnfactored - Resets a factored matrix to be treated as unfactored. 4657 4658 Collective on Mat 4659 4660 Input Parameter: 4661 . mat - the factored matrix to be reset 4662 4663 Notes: 4664 This routine should be used only with factored matrices formed by in-place 4665 factorization via ILU(0) (or by in-place LU factorization for the MATSEQDENSE 4666 format). This option can save memory, for example, when solving nonlinear 4667 systems with a matrix-free Newton-Krylov method and a matrix-based, in-place 4668 ILU(0) preconditioner. 4669 4670 Note that one can specify in-place ILU(0) factorization by calling 4671 .vb 4672 PCType(pc,PCILU); 4673 PCILUSeUseInPlace(pc); 4674 .ve 4675 or by using the options -pc_type ilu -pc_ilu_in_place 4676 4677 In-place factorization ILU(0) can also be used as a local 4678 solver for the blocks within the block Jacobi or additive Schwarz 4679 methods (runtime option: -sub_pc_ilu_in_place). See the discussion 4680 of these preconditioners in the users manual for details on setting 4681 local solver options. 4682 4683 Most users should employ the simplified KSP interface for linear solvers 4684 instead of working directly with matrix algebra routines such as this. 4685 See, e.g., KSPCreate(). 4686 4687 Level: developer 4688 4689 .seealso: PCILUSetUseInPlace(), PCLUSetUseInPlace() 4690 4691 Concepts: matrices^unfactored 4692 4693 @*/ 4694 PetscErrorCode MatSetUnfactored(Mat mat) 4695 { 4696 PetscErrorCode ierr; 4697 4698 PetscFunctionBegin; 4699 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4700 PetscValidType(mat,1); 4701 MatPreallocated(mat); 4702 mat->factor = 0; 4703 if (!mat->ops->setunfactored) PetscFunctionReturn(0); 4704 ierr = (*mat->ops->setunfactored)(mat);CHKERRQ(ierr); 4705 PetscFunctionReturn(0); 4706 } 4707 4708 /*MC 4709 MatGetArrayF90 - Accesses a matrix array from Fortran90. 4710 4711 Synopsis: 4712 MatGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 4713 4714 Not collective 4715 4716 Input Parameter: 4717 . x - matrix 4718 4719 Output Parameters: 4720 + xx_v - the Fortran90 pointer to the array 4721 - ierr - error code 4722 4723 Example of Usage: 4724 .vb 4725 PetscScalar, pointer xx_v(:) 4726 .... 4727 call MatGetArrayF90(x,xx_v,ierr) 4728 a = xx_v(3) 4729 call MatRestoreArrayF90(x,xx_v,ierr) 4730 .ve 4731 4732 Notes: 4733 Not yet supported for all F90 compilers 4734 4735 Level: advanced 4736 4737 .seealso: MatRestoreArrayF90(), MatGetArray(), MatRestoreArray() 4738 4739 Concepts: matrices^accessing array 4740 4741 M*/ 4742 4743 /*MC 4744 MatRestoreArrayF90 - Restores a matrix array that has been 4745 accessed with MatGetArrayF90(). 4746 4747 Synopsis: 4748 MatRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr) 4749 4750 Not collective 4751 4752 Input Parameters: 4753 + x - matrix 4754 - xx_v - the Fortran90 pointer to the array 4755 4756 Output Parameter: 4757 . ierr - error code 4758 4759 Example of Usage: 4760 .vb 4761 PetscScalar, pointer xx_v(:) 4762 .... 4763 call MatGetArrayF90(x,xx_v,ierr) 4764 a = xx_v(3) 4765 call MatRestoreArrayF90(x,xx_v,ierr) 4766 .ve 4767 4768 Notes: 4769 Not yet supported for all F90 compilers 4770 4771 Level: advanced 4772 4773 .seealso: MatGetArrayF90(), MatGetArray(), MatRestoreArray() 4774 4775 M*/ 4776 4777 4778 #undef __FUNCT__ 4779 #define __FUNCT__ "MatGetSubMatrix" 4780 /*@ 4781 MatGetSubMatrix - Gets a single submatrix on the same number of processors 4782 as the original matrix. 4783 4784 Collective on Mat 4785 4786 Input Parameters: 4787 + mat - the original matrix 4788 . isrow - rows this processor should obtain 4789 . iscol - columns for all processors you wish to keep 4790 . csize - number of columns "local" to this processor (does nothing for sequential 4791 matrices). This should match the result from VecGetLocalSize(x,...) if you 4792 plan to use the matrix in a A*x; alternatively, you can use PETSC_DECIDE 4793 - cll - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX 4794 4795 Output Parameter: 4796 . newmat - the new submatrix, of the same type as the old 4797 4798 Level: advanced 4799 4800 Notes: the iscol argument MUST be the same on each processor. You might be 4801 able to create the iscol argument with ISAllGather(). 4802 4803 The first time this is called you should use a cll of MAT_INITIAL_MATRIX, 4804 the MatGetSubMatrix() routine will create the newmat for you. Any additional calls 4805 to this routine with a mat of the same nonzero structure and with a cll of MAT_REUSE_MATRIX 4806 will reuse the matrix generated the first time. 4807 4808 Concepts: matrices^submatrices 4809 4810 .seealso: MatGetSubMatrices(), ISAllGather() 4811 @*/ 4812 PetscErrorCode MatGetSubMatrix(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse cll,Mat *newmat) 4813 { 4814 PetscErrorCode ierr; 4815 PetscMPIInt size; 4816 Mat *local; 4817 4818 PetscFunctionBegin; 4819 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4820 PetscValidHeaderSpecific(isrow,IS_COOKIE,2); 4821 PetscValidHeaderSpecific(iscol,IS_COOKIE,3); 4822 PetscValidPointer(newmat,6); 4823 if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat,MAT_COOKIE,6); 4824 PetscValidType(mat,1); 4825 MatPreallocated(mat); 4826 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 4827 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 4828 4829 /* if original matrix is on just one processor then use submatrix generated */ 4830 if (!mat->ops->getsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) { 4831 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&newmat);CHKERRQ(ierr); 4832 PetscFunctionReturn(0); 4833 } else if (!mat->ops->getsubmatrix && size == 1) { 4834 ierr = MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);CHKERRQ(ierr); 4835 *newmat = *local; 4836 ierr = PetscFree(local);CHKERRQ(ierr); 4837 PetscFunctionReturn(0); 4838 } 4839 4840 if (!mat->ops->getsubmatrix) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 4841 ierr = (*mat->ops->getsubmatrix)(mat,isrow,iscol,csize,cll,newmat);CHKERRQ(ierr); 4842 ierr = PetscObjectIncreaseState((PetscObject)*newmat);CHKERRQ(ierr); 4843 PetscFunctionReturn(0); 4844 } 4845 4846 #undef __FUNCT__ 4847 #define __FUNCT__ "MatGetPetscMaps" 4848 /*@C 4849 MatGetPetscMaps - Returns the maps associated with the matrix. 4850 4851 Not Collective 4852 4853 Input Parameter: 4854 . mat - the matrix 4855 4856 Output Parameters: 4857 + rmap - the row (right) map 4858 - cmap - the column (left) map 4859 4860 Level: developer 4861 4862 Concepts: maps^getting from matrix 4863 4864 @*/ 4865 PetscErrorCode MatGetPetscMaps(Mat mat,PetscMap *rmap,PetscMap *cmap) 4866 { 4867 PetscErrorCode ierr; 4868 4869 PetscFunctionBegin; 4870 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4871 PetscValidType(mat,1); 4872 MatPreallocated(mat); 4873 ierr = (*mat->ops->getmaps)(mat,rmap,cmap);CHKERRQ(ierr); 4874 PetscFunctionReturn(0); 4875 } 4876 4877 /* 4878 Version that works for all PETSc matrices 4879 */ 4880 #undef __FUNCT__ 4881 #define __FUNCT__ "MatGetPetscMaps_Petsc" 4882 PetscErrorCode MatGetPetscMaps_Petsc(Mat mat,PetscMap *rmap,PetscMap *cmap) 4883 { 4884 PetscFunctionBegin; 4885 if (rmap) *rmap = mat->rmap; 4886 if (cmap) *cmap = mat->cmap; 4887 PetscFunctionReturn(0); 4888 } 4889 4890 #undef __FUNCT__ 4891 #define __FUNCT__ "MatStashSetInitialSize" 4892 /*@ 4893 MatStashSetInitialSize - sets the sizes of the matrix stash, that is 4894 used during the assembly process to store values that belong to 4895 other processors. 4896 4897 Not Collective 4898 4899 Input Parameters: 4900 + mat - the matrix 4901 . size - the initial size of the stash. 4902 - bsize - the initial size of the block-stash(if used). 4903 4904 Options Database Keys: 4905 + -matstash_initial_size <size> or <size0,size1,...sizep-1> 4906 - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> 4907 4908 Level: intermediate 4909 4910 Notes: 4911 The block-stash is used for values set with VecSetValuesBlocked() while 4912 the stash is used for values set with VecSetValues() 4913 4914 Run with the option -log_info and look for output of the form 4915 MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs. 4916 to determine the appropriate value, MM, to use for size and 4917 MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs. 4918 to determine the value, BMM to use for bsize 4919 4920 Concepts: stash^setting matrix size 4921 Concepts: matrices^stash 4922 4923 @*/ 4924 PetscErrorCode MatStashSetInitialSize(Mat mat,PetscInt size, PetscInt bsize) 4925 { 4926 PetscErrorCode ierr; 4927 4928 PetscFunctionBegin; 4929 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 4930 PetscValidType(mat,1); 4931 MatPreallocated(mat); 4932 ierr = MatStashSetInitialSize_Private(&mat->stash,size);CHKERRQ(ierr); 4933 ierr = MatStashSetInitialSize_Private(&mat->bstash,bsize);CHKERRQ(ierr); 4934 PetscFunctionReturn(0); 4935 } 4936 4937 #undef __FUNCT__ 4938 #define __FUNCT__ "MatInterpolateAdd" 4939 /*@ 4940 MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of 4941 the matrix 4942 4943 Collective on Mat 4944 4945 Input Parameters: 4946 + mat - the matrix 4947 . x,y - the vectors 4948 - w - where the result is stored 4949 4950 Level: intermediate 4951 4952 Notes: 4953 w may be the same vector as y. 4954 4955 This allows one to use either the restriction or interpolation (its transpose) 4956 matrix to do the interpolation 4957 4958 Concepts: interpolation 4959 4960 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 4961 4962 @*/ 4963 PetscErrorCode MatInterpolateAdd(Mat A,Vec x,Vec y,Vec w) 4964 { 4965 PetscErrorCode ierr; 4966 PetscInt M,N; 4967 4968 PetscFunctionBegin; 4969 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 4970 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 4971 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 4972 PetscValidHeaderSpecific(w,VEC_COOKIE,4); 4973 PetscValidType(A,1); 4974 MatPreallocated(A); 4975 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 4976 if (N > M) { 4977 ierr = MatMultTransposeAdd(A,x,y,w);CHKERRQ(ierr); 4978 } else { 4979 ierr = MatMultAdd(A,x,y,w);CHKERRQ(ierr); 4980 } 4981 PetscFunctionReturn(0); 4982 } 4983 4984 #undef __FUNCT__ 4985 #define __FUNCT__ "MatInterpolate" 4986 /*@ 4987 MatInterpolate - y = A*x or A'*x depending on the shape of 4988 the matrix 4989 4990 Collective on Mat 4991 4992 Input Parameters: 4993 + mat - the matrix 4994 - x,y - the vectors 4995 4996 Level: intermediate 4997 4998 Notes: 4999 This allows one to use either the restriction or interpolation (its transpose) 5000 matrix to do the interpolation 5001 5002 Concepts: matrices^interpolation 5003 5004 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatRestrict() 5005 5006 @*/ 5007 PetscErrorCode MatInterpolate(Mat A,Vec x,Vec y) 5008 { 5009 PetscErrorCode ierr; 5010 PetscInt M,N; 5011 5012 PetscFunctionBegin; 5013 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5014 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 5015 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 5016 PetscValidType(A,1); 5017 MatPreallocated(A); 5018 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 5019 if (N > M) { 5020 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 5021 } else { 5022 ierr = MatMult(A,x,y);CHKERRQ(ierr); 5023 } 5024 PetscFunctionReturn(0); 5025 } 5026 5027 #undef __FUNCT__ 5028 #define __FUNCT__ "MatRestrict" 5029 /*@ 5030 MatRestrict - y = A*x or A'*x 5031 5032 Collective on Mat 5033 5034 Input Parameters: 5035 + mat - the matrix 5036 - x,y - the vectors 5037 5038 Level: intermediate 5039 5040 Notes: 5041 This allows one to use either the restriction or interpolation (its transpose) 5042 matrix to do the restriction 5043 5044 Concepts: matrices^restriction 5045 5046 .seealso: MatMultAdd(), MatMultTransposeAdd(), MatInterpolate() 5047 5048 @*/ 5049 PetscErrorCode MatRestrict(Mat A,Vec x,Vec y) 5050 { 5051 PetscErrorCode ierr; 5052 PetscInt M,N; 5053 5054 PetscFunctionBegin; 5055 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5056 PetscValidHeaderSpecific(x,VEC_COOKIE,2); 5057 PetscValidHeaderSpecific(y,VEC_COOKIE,3); 5058 PetscValidType(A,1); 5059 MatPreallocated(A); 5060 ierr = MatGetSize(A,&M,&N);CHKERRQ(ierr); 5061 if (N > M) { 5062 ierr = MatMult(A,x,y);CHKERRQ(ierr); 5063 } else { 5064 ierr = MatMultTranspose(A,x,y);CHKERRQ(ierr); 5065 } 5066 PetscFunctionReturn(0); 5067 } 5068 5069 #undef __FUNCT__ 5070 #define __FUNCT__ "MatNullSpaceAttach" 5071 /*@C 5072 MatNullSpaceAttach - attaches a null space to a matrix. 5073 This null space will be removed from the resulting vector whenever 5074 MatMult() is called 5075 5076 Collective on Mat 5077 5078 Input Parameters: 5079 + mat - the matrix 5080 - nullsp - the null space object 5081 5082 Level: developer 5083 5084 Notes: 5085 Overwrites any previous null space that may have been attached 5086 5087 Concepts: null space^attaching to matrix 5088 5089 .seealso: MatCreate(), MatNullSpaceCreate() 5090 @*/ 5091 PetscErrorCode MatNullSpaceAttach(Mat mat,MatNullSpace nullsp) 5092 { 5093 PetscErrorCode ierr; 5094 5095 PetscFunctionBegin; 5096 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5097 PetscValidType(mat,1); 5098 MatPreallocated(mat); 5099 PetscValidHeaderSpecific(nullsp,MAT_NULLSPACE_COOKIE,2); 5100 5101 if (mat->nullsp) { 5102 ierr = MatNullSpaceDestroy(mat->nullsp);CHKERRQ(ierr); 5103 } 5104 mat->nullsp = nullsp; 5105 ierr = PetscObjectReference((PetscObject)nullsp);CHKERRQ(ierr); 5106 PetscFunctionReturn(0); 5107 } 5108 5109 #undef __FUNCT__ 5110 #define __FUNCT__ "MatICCFactor" 5111 /*@ 5112 MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix. 5113 5114 Collective on Mat 5115 5116 Input Parameters: 5117 + mat - the matrix 5118 . row - row/column permutation 5119 . fill - expected fill factor >= 1.0 5120 - level - level of fill, for ICC(k) 5121 5122 Notes: 5123 Probably really in-place only when level of fill is zero, otherwise allocates 5124 new space to store factored matrix and deletes previous memory. 5125 5126 Most users should employ the simplified KSP interface for linear solvers 5127 instead of working directly with matrix algebra routines such as this. 5128 See, e.g., KSPCreate(). 5129 5130 Level: developer 5131 5132 Concepts: matrices^incomplete Cholesky factorization 5133 Concepts: Cholesky factorization 5134 5135 .seealso: MatICCFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor() 5136 @*/ 5137 PetscErrorCode MatICCFactor(Mat mat,IS row,MatFactorInfo* info) 5138 { 5139 PetscErrorCode ierr; 5140 5141 PetscFunctionBegin; 5142 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5143 PetscValidType(mat,1); 5144 MatPreallocated(mat); 5145 if (row) PetscValidHeaderSpecific(row,IS_COOKIE,2); 5146 PetscValidPointer(info,3); 5147 if (mat->M != mat->N) SETERRQ(PETSC_ERR_ARG_WRONG,"matrix must be square"); 5148 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 5149 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 5150 if (!mat->ops->iccfactor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5151 ierr = (*mat->ops->iccfactor)(mat,row,info);CHKERRQ(ierr); 5152 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5153 PetscFunctionReturn(0); 5154 } 5155 5156 #undef __FUNCT__ 5157 #define __FUNCT__ "MatSetValuesAdic" 5158 /*@ 5159 MatSetValuesAdic - Sets values computed with ADIC automatic differentiation into a matrix. 5160 5161 Not Collective 5162 5163 Input Parameters: 5164 + mat - the matrix 5165 - v - the values compute with ADIC 5166 5167 Level: developer 5168 5169 Notes: 5170 Must call MatSetColoring() before using this routine. Also this matrix must already 5171 have its nonzero pattern determined. 5172 5173 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5174 MatSetValues(), MatSetColoring(), MatSetValuesAdifor() 5175 @*/ 5176 PetscErrorCode MatSetValuesAdic(Mat mat,void *v) 5177 { 5178 PetscErrorCode ierr; 5179 5180 PetscFunctionBegin; 5181 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5182 PetscValidType(mat,1); 5183 PetscValidPointer(mat,2); 5184 5185 if (!mat->assembled) { 5186 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5187 } 5188 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5189 if (!mat->ops->setvaluesadic) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5190 ierr = (*mat->ops->setvaluesadic)(mat,v);CHKERRQ(ierr); 5191 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5192 ierr = MatView_Private(mat);CHKERRQ(ierr); 5193 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5194 PetscFunctionReturn(0); 5195 } 5196 5197 5198 #undef __FUNCT__ 5199 #define __FUNCT__ "MatSetColoring" 5200 /*@ 5201 MatSetColoring - Sets a coloring used by calls to MatSetValuesAdic() 5202 5203 Not Collective 5204 5205 Input Parameters: 5206 + mat - the matrix 5207 - coloring - the coloring 5208 5209 Level: developer 5210 5211 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5212 MatSetValues(), MatSetValuesAdic() 5213 @*/ 5214 PetscErrorCode MatSetColoring(Mat mat,ISColoring coloring) 5215 { 5216 PetscErrorCode ierr; 5217 5218 PetscFunctionBegin; 5219 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5220 PetscValidType(mat,1); 5221 PetscValidPointer(coloring,2); 5222 5223 if (!mat->assembled) { 5224 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5225 } 5226 if (!mat->ops->setcoloring) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5227 ierr = (*mat->ops->setcoloring)(mat,coloring);CHKERRQ(ierr); 5228 PetscFunctionReturn(0); 5229 } 5230 5231 #undef __FUNCT__ 5232 #define __FUNCT__ "MatSetValuesAdifor" 5233 /*@ 5234 MatSetValuesAdifor - Sets values computed with automatic differentiation into a matrix. 5235 5236 Not Collective 5237 5238 Input Parameters: 5239 + mat - the matrix 5240 . nl - leading dimension of v 5241 - v - the values compute with ADIFOR 5242 5243 Level: developer 5244 5245 Notes: 5246 Must call MatSetColoring() before using this routine. Also this matrix must already 5247 have its nonzero pattern determined. 5248 5249 .seealso: MatSetOption(), MatAssemblyBegin(), MatAssemblyEnd(), MatSetValuesBlocked(), MatSetValuesLocal(), 5250 MatSetValues(), MatSetColoring() 5251 @*/ 5252 PetscErrorCode MatSetValuesAdifor(Mat mat,PetscInt nl,void *v) 5253 { 5254 PetscErrorCode ierr; 5255 5256 PetscFunctionBegin; 5257 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5258 PetscValidType(mat,1); 5259 PetscValidPointer(v,3); 5260 5261 if (!mat->assembled) { 5262 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5263 } 5264 ierr = PetscLogEventBegin(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5265 if (!mat->ops->setvaluesadifor) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5266 ierr = (*mat->ops->setvaluesadifor)(mat,nl,v);CHKERRQ(ierr); 5267 ierr = PetscLogEventEnd(MAT_SetValues,mat,0,0,0);CHKERRQ(ierr); 5268 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5269 PetscFunctionReturn(0); 5270 } 5271 5272 EXTERN PetscErrorCode MatMPIAIJDiagonalScaleLocal(Mat,Vec); 5273 EXTERN PetscErrorCode MatMPIBAIJDiagonalScaleLocal(Mat,Vec); 5274 5275 #undef __FUNCT__ 5276 #define __FUNCT__ "MatDiagonalScaleLocal" 5277 /*@ 5278 MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the 5279 ghosted ones. 5280 5281 Not Collective 5282 5283 Input Parameters: 5284 + mat - the matrix 5285 - diag = the diagonal values, including ghost ones 5286 5287 Level: developer 5288 5289 Notes: Works only for MPIAIJ and MPIBAIJ matrices 5290 5291 .seealso: MatDiagonalScale() 5292 @*/ 5293 PetscErrorCode MatDiagonalScaleLocal(Mat mat,Vec diag) 5294 { 5295 PetscErrorCode ierr; 5296 PetscMPIInt size; 5297 5298 PetscFunctionBegin; 5299 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5300 PetscValidHeaderSpecific(diag,VEC_COOKIE,2); 5301 PetscValidType(mat,1); 5302 5303 if (!mat->assembled) { 5304 SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be already assembled"); 5305 } 5306 ierr = PetscLogEventBegin(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 5307 ierr = MPI_Comm_size(mat->comm,&size);CHKERRQ(ierr); 5308 if (size == 1) { 5309 PetscInt n,m; 5310 ierr = VecGetSize(diag,&n);CHKERRQ(ierr); 5311 ierr = MatGetSize(mat,0,&m);CHKERRQ(ierr); 5312 if (m == n) { 5313 ierr = MatDiagonalScale(mat,0,diag);CHKERRQ(ierr); 5314 } else { 5315 SETERRQ(PETSC_ERR_SUP,"Only supported for sequential matrices when no ghost points/periodic conditions"); 5316 } 5317 } else { 5318 PetscErrorCode (*f)(Mat,Vec); 5319 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",(void (**)(void))&f);CHKERRQ(ierr); 5320 if (f) { 5321 ierr = (*f)(mat,diag);CHKERRQ(ierr); 5322 } else { 5323 SETERRQ(PETSC_ERR_SUP,"Only supported for MPIAIJ and MPIBAIJ parallel matrices"); 5324 } 5325 } 5326 ierr = PetscLogEventEnd(MAT_Scale,mat,0,0,0);CHKERRQ(ierr); 5327 ierr = PetscObjectIncreaseState((PetscObject)mat);CHKERRQ(ierr); 5328 PetscFunctionReturn(0); 5329 } 5330 5331 #undef __FUNCT__ 5332 #define __FUNCT__ "MatGetInertia" 5333 /*@ 5334 MatGetInertia - Gets the inertia from a factored matrix 5335 5336 Collective on Mat 5337 5338 Input Parameter: 5339 . mat - the matrix 5340 5341 Output Parameters: 5342 + nneg - number of negative eigenvalues 5343 . nzero - number of zero eigenvalues 5344 - npos - number of positive eigenvalues 5345 5346 Level: advanced 5347 5348 Notes: Matrix must have been factored by MatCholeskyFactor() 5349 5350 5351 @*/ 5352 PetscErrorCode MatGetInertia(Mat mat,PetscInt *nneg,PetscInt *nzero,PetscInt *npos) 5353 { 5354 PetscErrorCode ierr; 5355 5356 PetscFunctionBegin; 5357 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5358 PetscValidType(mat,1); 5359 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 5360 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Numeric factor mat is not assembled"); 5361 if (!mat->ops->getinertia) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5362 ierr = (*mat->ops->getinertia)(mat,nneg,nzero,npos);CHKERRQ(ierr); 5363 PetscFunctionReturn(0); 5364 } 5365 5366 /* ----------------------------------------------------------------*/ 5367 #undef __FUNCT__ 5368 #define __FUNCT__ "MatSolves" 5369 /*@ 5370 MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors 5371 5372 Collective on Mat and Vecs 5373 5374 Input Parameters: 5375 + mat - the factored matrix 5376 - b - the right-hand-side vectors 5377 5378 Output Parameter: 5379 . x - the result vectors 5380 5381 Notes: 5382 The vectors b and x cannot be the same. I.e., one cannot 5383 call MatSolves(A,x,x). 5384 5385 Notes: 5386 Most users should employ the simplified KSP interface for linear solvers 5387 instead of working directly with matrix algebra routines such as this. 5388 See, e.g., KSPCreate(). 5389 5390 Level: developer 5391 5392 Concepts: matrices^triangular solves 5393 5394 .seealso: MatSolveAdd(), MatSolveTranspose(), MatSolveTransposeAdd(), MatSolve() 5395 @*/ 5396 PetscErrorCode MatSolves(Mat mat,Vecs b,Vecs x) 5397 { 5398 PetscErrorCode ierr; 5399 5400 PetscFunctionBegin; 5401 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5402 PetscValidType(mat,1); 5403 MatPreallocated(mat); 5404 if (x == b) SETERRQ(PETSC_ERR_ARG_IDN,"x and b must be different vectors"); 5405 if (!mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Unfactored matrix"); 5406 if (!mat->M && !mat->N) PetscFunctionReturn(0); 5407 5408 if (!mat->ops->solves) SETERRQ1(PETSC_ERR_SUP,"Mat type %s",mat->type_name); 5409 ierr = PetscLogEventBegin(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 5410 ierr = (*mat->ops->solves)(mat,b,x);CHKERRQ(ierr); 5411 ierr = PetscLogEventEnd(MAT_Solves,mat,0,0,0);CHKERRQ(ierr); 5412 PetscFunctionReturn(0); 5413 } 5414 5415 #undef __FUNCT__ 5416 #define __FUNCT__ "MatIsSymmetric" 5417 /*@C 5418 MatIsSymmetric - Test whether a matrix is symmetric 5419 5420 Collective on Mat 5421 5422 Input Parameter: 5423 + A - the matrix to test 5424 - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose) 5425 5426 Output Parameters: 5427 . flg - the result 5428 5429 Level: intermediate 5430 5431 Concepts: matrix^symmetry 5432 5433 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetricKnown() 5434 @*/ 5435 PetscErrorCode MatIsSymmetric(Mat A,PetscReal tol,PetscTruth *flg) 5436 { 5437 PetscErrorCode ierr; 5438 5439 PetscFunctionBegin; 5440 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5441 PetscValidPointer(flg,2); 5442 if (!A->symmetric_set) { 5443 if (!A->ops->issymmetric) { 5444 MatType mattype; 5445 ierr = MatGetType(A,&mattype);CHKERRQ(ierr); 5446 SETERRQ1(PETSC_ERR_SUP,"Matrix of type <%s> does not support checking for symmetric",mattype); 5447 } 5448 ierr = (*A->ops->issymmetric)(A,tol,&A->symmetric);CHKERRQ(ierr); 5449 A->symmetric_set = PETSC_TRUE; 5450 if (A->symmetric) { 5451 A->structurally_symmetric_set = PETSC_TRUE; 5452 A->structurally_symmetric = PETSC_TRUE; 5453 } 5454 } 5455 *flg = A->symmetric; 5456 PetscFunctionReturn(0); 5457 } 5458 5459 #undef __FUNCT__ 5460 #define __FUNCT__ "MatIsSymmetricKnown" 5461 /*@C 5462 MatIsSymmetricKnown - Checks the flag on the matrix to see if it is symmetric. 5463 5464 Collective on Mat 5465 5466 Input Parameter: 5467 . A - the matrix to check 5468 5469 Output Parameters: 5470 + set - if the symmetric flag is set (this tells you if the next flag is valid) 5471 - flg - the result 5472 5473 Level: advanced 5474 5475 Concepts: matrix^symmetry 5476 5477 Note: Does not check the matrix values directly, so this may return unknown (set = PETSC_FALSE). Use MatIsSymmetric() 5478 if you want it explicitly checked 5479 5480 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsStructurallySymmetric(), MatSetOption(), MatIsSymmetric() 5481 @*/ 5482 PetscErrorCode MatIsSymmetricKnown(Mat A,PetscTruth *set,PetscTruth *flg) 5483 { 5484 PetscFunctionBegin; 5485 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5486 PetscValidPointer(set,2); 5487 PetscValidPointer(flg,3); 5488 if (A->symmetric_set) { 5489 *set = PETSC_TRUE; 5490 *flg = A->symmetric; 5491 } else { 5492 *set = PETSC_FALSE; 5493 } 5494 PetscFunctionReturn(0); 5495 } 5496 5497 #undef __FUNCT__ 5498 #define __FUNCT__ "MatIsStructurallySymmetric" 5499 /*@C 5500 MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric 5501 5502 Collective on Mat 5503 5504 Input Parameter: 5505 . A - the matrix to test 5506 5507 Output Parameters: 5508 . flg - the result 5509 5510 Level: intermediate 5511 5512 Concepts: matrix^symmetry 5513 5514 .seealso: MatTranspose(), MatIsTranspose(), MatIsHermitian(), MatIsSymmetric(), MatSetOption() 5515 @*/ 5516 PetscErrorCode MatIsStructurallySymmetric(Mat A,PetscTruth *flg) 5517 { 5518 PetscErrorCode ierr; 5519 5520 PetscFunctionBegin; 5521 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5522 PetscValidPointer(flg,2); 5523 if (!A->structurally_symmetric_set) { 5524 if (!A->ops->isstructurallysymmetric) SETERRQ(PETSC_ERR_SUP,"Matrix does not support checking for structural symmetric"); 5525 ierr = (*A->ops->isstructurallysymmetric)(A,&A->structurally_symmetric);CHKERRQ(ierr); 5526 A->structurally_symmetric_set = PETSC_TRUE; 5527 } 5528 *flg = A->structurally_symmetric; 5529 PetscFunctionReturn(0); 5530 } 5531 5532 #undef __FUNCT__ 5533 #define __FUNCT__ "MatIsHermitian" 5534 /*@C 5535 MatIsHermitian - Test whether a matrix is Hermitian, i.e. it is the complex conjugate of its transpose. 5536 5537 Collective on Mat 5538 5539 Input Parameter: 5540 . A - the matrix to test 5541 5542 Output Parameters: 5543 . flg - the result 5544 5545 Level: intermediate 5546 5547 Concepts: matrix^symmetry 5548 5549 .seealso: MatTranspose(), MatIsTranspose(), MatIsSymmetric(), MatIsStructurallySymmetric(), MatSetOption() 5550 @*/ 5551 PetscErrorCode MatIsHermitian(Mat A,PetscTruth *flg) 5552 { 5553 PetscErrorCode ierr; 5554 5555 PetscFunctionBegin; 5556 PetscValidHeaderSpecific(A,MAT_COOKIE,1); 5557 PetscValidPointer(flg,2); 5558 if (!A->hermitian_set) { 5559 if (!A->ops->ishermitian) SETERRQ(PETSC_ERR_SUP,"Matrix does not support checking for being Hermitian"); 5560 ierr = (*A->ops->ishermitian)(A,&A->hermitian);CHKERRQ(ierr); 5561 A->hermitian_set = PETSC_TRUE; 5562 if (A->hermitian) { 5563 A->structurally_symmetric_set = PETSC_TRUE; 5564 A->structurally_symmetric = PETSC_TRUE; 5565 } 5566 } 5567 *flg = A->hermitian; 5568 PetscFunctionReturn(0); 5569 } 5570 5571 #undef __FUNCT__ 5572 #define __FUNCT__ "MatStashGetInfo" 5573 extern PetscErrorCode MatStashGetInfo_Private(MatStash*,PetscInt*,PetscInt*); 5574 /*@ 5575 MatStashGetInfo - Gets how many values are currently in the vector stash, i.e. need 5576 to be communicated to other processors during the MatAssemblyBegin/End() process 5577 5578 Not collective 5579 5580 Input Parameter: 5581 . vec - the vector 5582 5583 Output Parameters: 5584 + nstash - the size of the stash 5585 . reallocs - the number of additional mallocs incurred. 5586 . bnstash - the size of the block stash 5587 - breallocs - the number of additional mallocs incurred.in the block stash 5588 5589 Level: advanced 5590 5591 .seealso: MatAssemblyBegin(), MatAssemblyEnd(), Mat, MatStashSetInitialSize() 5592 5593 @*/ 5594 PetscErrorCode MatStashGetInfo(Mat mat,PetscInt *nstash,PetscInt *reallocs,PetscInt *bnstash,PetscInt *brealloc) 5595 { 5596 PetscErrorCode ierr; 5597 PetscFunctionBegin; 5598 ierr = MatStashGetInfo_Private(&mat->stash,nstash,reallocs);CHKERRQ(ierr); 5599 ierr = MatStashGetInfo_Private(&mat->bstash,nstash,reallocs);CHKERRQ(ierr); 5600 PetscFunctionReturn(0); 5601 } 5602 5603 #undef __FUNCT__ 5604 #define __FUNCT__ "MatGetVecs" 5605 /*@ 5606 MatGetVecs - Get vector(s) compatible with the matrix, i.e. with the same 5607 parallel layout 5608 5609 Collective on Mat 5610 5611 Input Parameter: 5612 . mat - the matrix 5613 5614 Output Parameter: 5615 + right - (optional) vector that the matrix can be multiplied against 5616 - left - (optional) vector that the matrix vector product can be stored in 5617 5618 Level: advanced 5619 5620 .seealso: MatCreate() 5621 @*/ 5622 PetscErrorCode MatGetVecs(Mat mat,Vec *right,Vec *left) 5623 { 5624 PetscErrorCode ierr; 5625 5626 PetscFunctionBegin; 5627 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 5628 PetscValidType(mat,1); 5629 MatPreallocated(mat); 5630 if (mat->ops->getvecs) { 5631 ierr = (*mat->ops->getvecs)(mat,right,left);CHKERRQ(ierr); 5632 } else { 5633 PetscMPIInt size; 5634 ierr = MPI_Comm_size(mat->comm, &size);CHKERRQ(ierr); 5635 if (right) { 5636 ierr = VecCreate(mat->comm,right);CHKERRQ(ierr); 5637 ierr = VecSetSizes(*right,mat->n,PETSC_DETERMINE);CHKERRQ(ierr); 5638 if (size > 1) {ierr = VecSetType(*right,VECMPI);CHKERRQ(ierr);} 5639 else {ierr = VecSetType(*right,VECSEQ);CHKERRQ(ierr);} 5640 } 5641 if (left) { 5642 ierr = VecCreate(mat->comm,left);CHKERRQ(ierr); 5643 ierr = VecSetSizes(*left,mat->m,PETSC_DETERMINE);CHKERRQ(ierr); 5644 if (size > 1) {ierr = VecSetType(*left,VECMPI);CHKERRQ(ierr);} 5645 else {ierr = VecSetType(*left,VECSEQ);CHKERRQ(ierr);} 5646 } 5647 } 5648 PetscFunctionReturn(0); 5649 } 5650