1 #define PETSCMAT_DLL 2 3 /* 4 This is where the abstract matrix operations are defined that are 5 used for finite difference computations of Jacobians using coloring. 6 */ 7 8 #include "private/matimpl.h" /*I "petscmat.h" I*/ 9 10 #undef __FUNCT__ 11 #define __FUNCT__ "MatFDColoringSetF" 12 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringSetF(MatFDColoring fd,Vec F) 13 { 14 PetscFunctionBegin; 15 fd->F = F; 16 PetscFunctionReturn(0); 17 } 18 19 #undef __FUNCT__ 20 #define __FUNCT__ "MatFDColoringView_Draw_Zoom" 21 static PetscErrorCode MatFDColoringView_Draw_Zoom(PetscDraw draw,void *Aa) 22 { 23 MatFDColoring fd = (MatFDColoring)Aa; 24 PetscErrorCode ierr; 25 PetscInt i,j; 26 PetscReal x,y; 27 28 PetscFunctionBegin; 29 30 /* loop over colors */ 31 for (i=0; i<fd->ncolors; i++) { 32 for (j=0; j<fd->nrows[i]; j++) { 33 y = fd->M - fd->rows[i][j] - fd->rstart; 34 x = fd->columnsforrow[i][j]; 35 ierr = PetscDrawRectangle(draw,x,y,x+1,y+1,i+1,i+1,i+1,i+1);CHKERRQ(ierr); 36 } 37 } 38 PetscFunctionReturn(0); 39 } 40 41 #undef __FUNCT__ 42 #define __FUNCT__ "MatFDColoringView_Draw" 43 static PetscErrorCode MatFDColoringView_Draw(MatFDColoring fd,PetscViewer viewer) 44 { 45 PetscErrorCode ierr; 46 PetscTruth isnull; 47 PetscDraw draw; 48 PetscReal xr,yr,xl,yl,h,w; 49 50 PetscFunctionBegin; 51 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 52 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); if (isnull) PetscFunctionReturn(0); 53 54 ierr = PetscObjectCompose((PetscObject)fd,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 55 56 xr = fd->N; yr = fd->M; h = yr/10.0; w = xr/10.0; 57 xr += w; yr += h; xl = -w; yl = -h; 58 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 59 ierr = PetscDrawZoom(draw,MatFDColoringView_Draw_Zoom,fd);CHKERRQ(ierr); 60 ierr = PetscObjectCompose((PetscObject)fd,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 61 PetscFunctionReturn(0); 62 } 63 64 #undef __FUNCT__ 65 #define __FUNCT__ "MatFDColoringView" 66 /*@C 67 MatFDColoringView - Views a finite difference coloring context. 68 69 Collective on MatFDColoring 70 71 Input Parameters: 72 + c - the coloring context 73 - viewer - visualization context 74 75 Level: intermediate 76 77 Notes: 78 The available visualization contexts include 79 + PETSC_VIEWER_STDOUT_SELF - standard output (default) 80 . PETSC_VIEWER_STDOUT_WORLD - synchronized standard 81 output where only the first processor opens 82 the file. All other processors send their 83 data to the first processor to print. 84 - PETSC_VIEWER_DRAW_WORLD - graphical display of nonzero structure 85 86 Notes: 87 Since PETSc uses only a small number of basic colors (currently 33), if the coloring 88 involves more than 33 then some seemingly identical colors are displayed making it look 89 like an illegal coloring. This is just a graphical artifact. 90 91 .seealso: MatFDColoringCreate() 92 93 .keywords: Mat, finite differences, coloring, view 94 @*/ 95 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringView(MatFDColoring c,PetscViewer viewer) 96 { 97 PetscErrorCode ierr; 98 PetscInt i,j; 99 PetscTruth isdraw,iascii; 100 PetscViewerFormat format; 101 102 PetscFunctionBegin; 103 PetscValidHeaderSpecific(c,MAT_FDCOLORING_CLASSID,1); 104 if (!viewer) { 105 ierr = PetscViewerASCIIGetStdout(((PetscObject)c)->comm,&viewer);CHKERRQ(ierr); 106 } 107 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 108 PetscCheckSameComm(c,1,viewer,2); 109 110 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 111 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); 112 if (isdraw) { 113 ierr = MatFDColoringView_Draw(c,viewer);CHKERRQ(ierr); 114 } else if (iascii) { 115 ierr = PetscViewerASCIIPrintf(viewer,"MatFDColoring Object:\n");CHKERRQ(ierr); 116 ierr = PetscViewerASCIIPrintf(viewer," Error tolerance=%G\n",c->error_rel);CHKERRQ(ierr); 117 ierr = PetscViewerASCIIPrintf(viewer," Umin=%G\n",c->umin);CHKERRQ(ierr); 118 ierr = PetscViewerASCIIPrintf(viewer," Number of colors=%D\n",c->ncolors);CHKERRQ(ierr); 119 120 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 121 if (format != PETSC_VIEWER_ASCII_INFO) { 122 for (i=0; i<c->ncolors; i++) { 123 ierr = PetscViewerASCIIPrintf(viewer," Information for color %D\n",i);CHKERRQ(ierr); 124 ierr = PetscViewerASCIIPrintf(viewer," Number of columns %D\n",c->ncolumns[i]);CHKERRQ(ierr); 125 for (j=0; j<c->ncolumns[i]; j++) { 126 ierr = PetscViewerASCIIPrintf(viewer," %D\n",c->columns[i][j]);CHKERRQ(ierr); 127 } 128 ierr = PetscViewerASCIIPrintf(viewer," Number of rows %D\n",c->nrows[i]);CHKERRQ(ierr); 129 for (j=0; j<c->nrows[i]; j++) { 130 ierr = PetscViewerASCIIPrintf(viewer," %D %D \n",c->rows[i][j],c->columnsforrow[i][j]);CHKERRQ(ierr); 131 } 132 } 133 } 134 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 135 } else { 136 SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported for MatFDColoring",((PetscObject)viewer)->type_name); 137 } 138 PetscFunctionReturn(0); 139 } 140 141 #undef __FUNCT__ 142 #define __FUNCT__ "MatFDColoringSetParameters" 143 /*@ 144 MatFDColoringSetParameters - Sets the parameters for the sparse approximation of 145 a Jacobian matrix using finite differences. 146 147 Collective on MatFDColoring 148 149 The Jacobian is estimated with the differencing approximation 150 .vb 151 F'(u)_{:,i} = [F(u+h*dx_{i}) - F(u)]/h where 152 h = error_rel*u[i] if abs(u[i]) > umin 153 = +/- error_rel*umin otherwise, with +/- determined by the sign of u[i] 154 dx_{i} = (0, ... 1, .... 0) 155 .ve 156 157 Input Parameters: 158 + coloring - the coloring context 159 . error_rel - relative error 160 - umin - minimum allowable u-value magnitude 161 162 Level: advanced 163 164 .keywords: Mat, finite differences, coloring, set, parameters 165 166 .seealso: MatFDColoringCreate(), MatFDColoringSetFromOptions() 167 168 @*/ 169 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringSetParameters(MatFDColoring matfd,PetscReal error,PetscReal umin) 170 { 171 PetscFunctionBegin; 172 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_CLASSID,1); 173 174 if (error != PETSC_DEFAULT) matfd->error_rel = error; 175 if (umin != PETSC_DEFAULT) matfd->umin = umin; 176 PetscFunctionReturn(0); 177 } 178 179 180 181 #undef __FUNCT__ 182 #define __FUNCT__ "MatFDColoringGetFunction" 183 /*@C 184 MatFDColoringGetFunction - Gets the function to use for computing the Jacobian. 185 186 Collective on MatFDColoring 187 188 Input Parameters: 189 . coloring - the coloring context 190 191 Output Parameters: 192 + f - the function 193 - fctx - the optional user-defined function context 194 195 Level: intermediate 196 197 .keywords: Mat, Jacobian, finite differences, set, function 198 199 .seealso: MatFDColoringCreate(), MatFDColoringSetFunction(), MatFDColoringSetFromOptions() 200 201 @*/ 202 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringGetFunction(MatFDColoring matfd,PetscErrorCode (**f)(void),void **fctx) 203 { 204 PetscFunctionBegin; 205 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_CLASSID,1); 206 if (f) *f = matfd->f; 207 if (fctx) *fctx = matfd->fctx; 208 PetscFunctionReturn(0); 209 } 210 211 #undef __FUNCT__ 212 #define __FUNCT__ "MatFDColoringSetFunction" 213 /*@C 214 MatFDColoringSetFunction - Sets the function to use for computing the Jacobian. 215 216 Collective on MatFDColoring 217 218 Input Parameters: 219 + coloring - the coloring context 220 . f - the function 221 - fctx - the optional user-defined function context 222 223 Calling sequence of (*f) function: 224 For SNES: PetscErrorCode (*f)(SNES,Vec,Vec,void*) 225 For TS: PetscErrorCode (*f)(TS,PetscReal,Vec,Vec,void*) 226 If not using SNES or TS: PetscErrorCode (*f)(void *dummy,Vec,Vec,void*) and dummy is ignored 227 228 Level: advanced 229 230 Notes: This function is usually used automatically by SNES or TS (when one uses SNESSetJacobian() with the argument 231 SNESDefaultComputeJacobianColor() or TSSetRHSJacobian() with the argument TSDefaultComputeJacobianColor()) and only needs to be used 232 by someone computing a matrix via coloring directly by calling MatFDColoringApply() 233 234 Fortran Notes: 235 In Fortran you must call MatFDColoringSetFunction() for a coloring object to 236 be used without SNES or TS or within the SNES solvers and MatFDColoringSetFunctionTS() if it is to be used 237 within the TS solvers. 238 239 .keywords: Mat, Jacobian, finite differences, set, function 240 241 .seealso: MatFDColoringCreate(), MatFDColoringGetFunction(), MatFDColoringSetFromOptions() 242 243 @*/ 244 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringSetFunction(MatFDColoring matfd,PetscErrorCode (*f)(void),void *fctx) 245 { 246 PetscFunctionBegin; 247 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_CLASSID,1); 248 matfd->f = f; 249 matfd->fctx = fctx; 250 PetscFunctionReturn(0); 251 } 252 253 #undef __FUNCT__ 254 #define __FUNCT__ "MatFDColoringSetFromOptions" 255 /*@ 256 MatFDColoringSetFromOptions - Sets coloring finite difference parameters from 257 the options database. 258 259 Collective on MatFDColoring 260 261 The Jacobian, F'(u), is estimated with the differencing approximation 262 .vb 263 F'(u)_{:,i} = [F(u+h*dx_{i}) - F(u)]/h where 264 h = error_rel*u[i] if abs(u[i]) > umin 265 = +/- error_rel*umin otherwise, with +/- determined by the sign of u[i] 266 dx_{i} = (0, ... 1, .... 0) 267 .ve 268 269 Input Parameter: 270 . coloring - the coloring context 271 272 Options Database Keys: 273 + -mat_fd_coloring_err <err> - Sets <err> (square root 274 of relative error in the function) 275 . -mat_fd_coloring_umin <umin> - Sets umin, the minimum allowable u-value magnitude 276 . -mat_fd_type - "wp" or "ds" (see MATMFFD_WP or MATMFFD_DS) 277 . -mat_fd_coloring_view - Activates basic viewing 278 . -mat_fd_coloring_view_info - Activates viewing info 279 - -mat_fd_coloring_view_draw - Activates drawing 280 281 Level: intermediate 282 283 .keywords: Mat, finite differences, parameters 284 285 .seealso: MatFDColoringCreate(), MatFDColoringView(), MatFDColoringSetParameters() 286 287 @*/ 288 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringSetFromOptions(MatFDColoring matfd) 289 { 290 PetscErrorCode ierr; 291 PetscTruth flg; 292 char value[3]; 293 294 PetscFunctionBegin; 295 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_CLASSID,1); 296 297 ierr = PetscOptionsBegin(((PetscObject)matfd)->comm,((PetscObject)matfd)->prefix,"Jacobian computation via finite differences option","MatFD");CHKERRQ(ierr); 298 ierr = PetscOptionsReal("-mat_fd_coloring_err","Square root of relative error in function","MatFDColoringSetParameters",matfd->error_rel,&matfd->error_rel,0);CHKERRQ(ierr); 299 ierr = PetscOptionsReal("-mat_fd_coloring_umin","Minimum allowable u magnitude","MatFDColoringSetParameters",matfd->umin,&matfd->umin,0);CHKERRQ(ierr); 300 ierr = PetscOptionsString("-mat_fd_type","Algorithm to compute h, wp or ds","MatFDColoringCreate",matfd->htype,value,2,&flg);CHKERRQ(ierr); 301 if (flg) { 302 if (value[0] == 'w' && value[1] == 'p') matfd->htype = "wp"; 303 else if (value[0] == 'd' && value[1] == 's') matfd->htype = "ds"; 304 else SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Unknown finite differencing type %s",value); 305 } 306 /* not used here; but so they are presented in the GUI */ 307 ierr = PetscOptionsName("-mat_fd_coloring_view","Print entire datastructure used for Jacobian","None",0);CHKERRQ(ierr); 308 ierr = PetscOptionsName("-mat_fd_coloring_view_info","Print number of colors etc for Jacobian","None",0);CHKERRQ(ierr); 309 ierr = PetscOptionsName("-mat_fd_coloring_view_draw","Plot nonzero structure ofJacobian","None",0);CHKERRQ(ierr); 310 PetscOptionsEnd();CHKERRQ(ierr); 311 PetscFunctionReturn(0); 312 } 313 314 #undef __FUNCT__ 315 #define __FUNCT__ "MatFDColoringView_Private" 316 PetscErrorCode MatFDColoringView_Private(MatFDColoring fd) 317 { 318 PetscErrorCode ierr; 319 PetscTruth flg = PETSC_FALSE; 320 PetscViewer viewer; 321 322 PetscFunctionBegin; 323 ierr = PetscViewerASCIIGetStdout(((PetscObject)fd)->comm,&viewer);CHKERRQ(ierr); 324 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_fd_coloring_view",&flg,PETSC_NULL);CHKERRQ(ierr); 325 if (flg) { 326 ierr = MatFDColoringView(fd,viewer);CHKERRQ(ierr); 327 } 328 flg = PETSC_FALSE; 329 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_fd_coloring_view_info",&flg,PETSC_NULL);CHKERRQ(ierr); 330 if (flg) { 331 ierr = PetscViewerPushFormat(viewer,PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr); 332 ierr = MatFDColoringView(fd,viewer);CHKERRQ(ierr); 333 ierr = PetscViewerPopFormat(viewer);CHKERRQ(ierr); 334 } 335 flg = PETSC_FALSE; 336 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_fd_coloring_view_draw",&flg,PETSC_NULL);CHKERRQ(ierr); 337 if (flg) { 338 ierr = MatFDColoringView(fd,PETSC_VIEWER_DRAW_(((PetscObject)fd)->comm));CHKERRQ(ierr); 339 ierr = PetscViewerFlush(PETSC_VIEWER_DRAW_(((PetscObject)fd)->comm));CHKERRQ(ierr); 340 } 341 PetscFunctionReturn(0); 342 } 343 344 #undef __FUNCT__ 345 #define __FUNCT__ "MatFDColoringCreate" 346 /*@ 347 MatFDColoringCreate - Creates a matrix coloring context for finite difference 348 computation of Jacobians. 349 350 Collective on Mat 351 352 Input Parameters: 353 + mat - the matrix containing the nonzero structure of the Jacobian 354 - iscoloring - the coloring of the matrix 355 356 Output Parameter: 357 . color - the new coloring context 358 359 Level: intermediate 360 361 .seealso: MatFDColoringDestroy(),SNESDefaultComputeJacobianColor(), ISColoringCreate(), 362 MatFDColoringSetFunction(), MatFDColoringSetFromOptions(), MatFDColoringApply(), 363 MatFDColoringView(), MatFDColoringSetParameters() 364 @*/ 365 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringCreate(Mat mat,ISColoring iscoloring,MatFDColoring *color) 366 { 367 MatFDColoring c; 368 MPI_Comm comm; 369 PetscErrorCode ierr; 370 PetscInt M,N; 371 PetscMPIInt size; 372 373 PetscFunctionBegin; 374 ierr = PetscLogEventBegin(MAT_FDColoringCreate,mat,0,0,0);CHKERRQ(ierr); 375 ierr = MatGetSize(mat,&M,&N);CHKERRQ(ierr); 376 if (M != N) SETERRQ(PETSC_ERR_SUP,"Only for square matrices"); 377 378 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 379 ierr = PetscHeaderCreate(c,_p_MatFDColoring,int,MAT_FDCOLORING_CLASSID,0,"MatFDColoring",comm,MatFDColoringDestroy,MatFDColoringView);CHKERRQ(ierr); 380 381 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 382 c->ctype = iscoloring->ctype; 383 384 if (mat->ops->fdcoloringcreate) { 385 ierr = (*mat->ops->fdcoloringcreate)(mat,iscoloring,c);CHKERRQ(ierr); 386 } else { 387 SETERRQ(PETSC_ERR_SUP,"Code not yet written for this matrix type"); 388 } 389 390 ierr = MatGetVecs(mat,PETSC_NULL,&c->w1);CHKERRQ(ierr); 391 ierr = PetscLogObjectParent(c,c->w1);CHKERRQ(ierr); 392 ierr = VecDuplicate(c->w1,&c->w2);CHKERRQ(ierr); 393 ierr = PetscLogObjectParent(c,c->w2);CHKERRQ(ierr); 394 395 c->error_rel = PETSC_SQRT_MACHINE_EPSILON; 396 c->umin = 100.0*PETSC_SQRT_MACHINE_EPSILON; 397 c->currentcolor = -1; 398 c->htype = "wp"; 399 400 *color = c; 401 ierr = PetscLogEventEnd(MAT_FDColoringCreate,mat,0,0,0);CHKERRQ(ierr); 402 PetscFunctionReturn(0); 403 } 404 405 #undef __FUNCT__ 406 #define __FUNCT__ "MatFDColoringDestroy" 407 /*@ 408 MatFDColoringDestroy - Destroys a matrix coloring context that was created 409 via MatFDColoringCreate(). 410 411 Collective on MatFDColoring 412 413 Input Parameter: 414 . c - coloring context 415 416 Level: intermediate 417 418 .seealso: MatFDColoringCreate() 419 @*/ 420 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringDestroy(MatFDColoring c) 421 { 422 PetscErrorCode ierr; 423 PetscInt i; 424 425 PetscFunctionBegin; 426 if (--((PetscObject)c)->refct > 0) PetscFunctionReturn(0); 427 428 for (i=0; i<c->ncolors; i++) { 429 ierr = PetscFree(c->columns[i]);CHKERRQ(ierr); 430 ierr = PetscFree(c->rows[i]);CHKERRQ(ierr); 431 ierr = PetscFree(c->columnsforrow[i]);CHKERRQ(ierr); 432 if (c->vscaleforrow) {ierr = PetscFree(c->vscaleforrow[i]);CHKERRQ(ierr);} 433 } 434 ierr = PetscFree(c->ncolumns);CHKERRQ(ierr); 435 ierr = PetscFree(c->columns);CHKERRQ(ierr); 436 ierr = PetscFree(c->nrows);CHKERRQ(ierr); 437 ierr = PetscFree(c->rows);CHKERRQ(ierr); 438 ierr = PetscFree(c->columnsforrow);CHKERRQ(ierr); 439 ierr = PetscFree(c->vscaleforrow);CHKERRQ(ierr); 440 if (c->vscale) {ierr = VecDestroy(c->vscale);CHKERRQ(ierr);} 441 if (c->w1) { 442 ierr = VecDestroy(c->w1);CHKERRQ(ierr); 443 ierr = VecDestroy(c->w2);CHKERRQ(ierr); 444 } 445 if (c->w3){ 446 ierr = VecDestroy(c->w3);CHKERRQ(ierr); 447 } 448 ierr = PetscHeaderDestroy(c);CHKERRQ(ierr); 449 PetscFunctionReturn(0); 450 } 451 452 #undef __FUNCT__ 453 #define __FUNCT__ "MatFDColoringGetPerturbedColumns" 454 /*@C 455 MatFDColoringGetPerturbedColumns - Returns the indices of the columns that 456 that are currently being perturbed. 457 458 Not Collective 459 460 Input Parameters: 461 . coloring - coloring context created with MatFDColoringCreate() 462 463 Output Parameters: 464 + n - the number of local columns being perturbed 465 - cols - the column indices, in global numbering 466 467 Level: intermediate 468 469 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringView(), MatFDColoringApply() 470 471 .keywords: coloring, Jacobian, finite differences 472 @*/ 473 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringGetPerturbedColumns(MatFDColoring coloring,PetscInt *n,PetscInt *cols[]) 474 { 475 PetscFunctionBegin; 476 if (coloring->currentcolor >= 0) { 477 *n = coloring->ncolumns[coloring->currentcolor]; 478 *cols = coloring->columns[coloring->currentcolor]; 479 } else { 480 *n = 0; 481 } 482 PetscFunctionReturn(0); 483 } 484 485 #undef __FUNCT__ 486 #define __FUNCT__ "MatFDColoringApply" 487 /*@ 488 MatFDColoringApply - Given a matrix for which a MatFDColoring context 489 has been created, computes the Jacobian for a function via finite differences. 490 491 Collective on MatFDColoring 492 493 Input Parameters: 494 + mat - location to store Jacobian 495 . coloring - coloring context created with MatFDColoringCreate() 496 . x1 - location at which Jacobian is to be computed 497 - sctx - context required by function, if this is being used with the SNES solver then it is SNES object, otherwise it is null 498 499 Options Database Keys: 500 + -mat_fd_type - "wp" or "ds" (see MATMFFD_WP or MATMFFD_DS) 501 . -mat_fd_coloring_view - Activates basic viewing or coloring 502 . -mat_fd_coloring_view_draw - Activates drawing of coloring 503 - -mat_fd_coloring_view_info - Activates viewing of coloring info 504 505 Level: intermediate 506 507 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringView(), MatFDColoringSetFunction() 508 509 .keywords: coloring, Jacobian, finite differences 510 @*/ 511 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringApply(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 512 { 513 PetscErrorCode ierr; 514 515 PetscFunctionBegin; 516 PetscValidHeaderSpecific(J,MAT_CLASSID,1); 517 PetscValidHeaderSpecific(coloring,MAT_FDCOLORING_CLASSID,2); 518 PetscValidHeaderSpecific(x1,VEC_CLASSID,3); 519 if (!coloring->f) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Must call MatFDColoringSetFunction()"); 520 if (!J->ops->fdcoloringapply) SETERRQ1(PETSC_ERR_SUP,"Not supported for this matrix type %s",((PetscObject)J)->type_name); 521 ierr = (*J->ops->fdcoloringapply)(J,coloring,x1,flag,sctx);CHKERRQ(ierr); 522 PetscFunctionReturn(0); 523 } 524 525 #undef __FUNCT__ 526 #define __FUNCT__ "MatFDColoringApply_AIJ" 527 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringApply_AIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 528 { 529 PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f; 530 PetscErrorCode ierr; 531 PetscInt k,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 532 PetscScalar dx,*y,*xx,*w3_array; 533 PetscScalar *vscale_array; 534 PetscReal epsilon = coloring->error_rel,umin = coloring->umin,unorm; 535 Vec w1=coloring->w1,w2=coloring->w2,w3; 536 void *fctx = coloring->fctx; 537 PetscTruth flg = PETSC_FALSE; 538 PetscInt ctype=coloring->ctype,N,col_start=0,col_end=0; 539 Vec x1_tmp; 540 541 PetscFunctionBegin; 542 PetscValidHeaderSpecific(J,MAT_CLASSID,1); 543 PetscValidHeaderSpecific(coloring,MAT_FDCOLORING_CLASSID,2); 544 PetscValidHeaderSpecific(x1,VEC_CLASSID,3); 545 if (!f) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Must call MatFDColoringSetFunction()"); 546 547 ierr = PetscLogEventBegin(MAT_FDColoringApply,coloring,J,x1,0);CHKERRQ(ierr); 548 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 549 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);CHKERRQ(ierr); 550 if (flg) { 551 ierr = PetscInfo(coloring,"Not calling MatZeroEntries()\n");CHKERRQ(ierr); 552 } else { 553 PetscTruth assembled; 554 ierr = MatAssembled(J,&assembled);CHKERRQ(ierr); 555 if (assembled) { 556 ierr = MatZeroEntries(J);CHKERRQ(ierr); 557 } 558 } 559 560 x1_tmp = x1; 561 if (!coloring->vscale){ 562 ierr = VecDuplicate(x1_tmp,&coloring->vscale);CHKERRQ(ierr); 563 } 564 565 /* 566 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 567 coloring->F for the coarser grids from the finest 568 */ 569 if (coloring->F) { 570 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 571 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 572 if (m1 != m2) { 573 coloring->F = 0; 574 } 575 } 576 577 if (coloring->htype[0] == 'w') { /* tacky test; need to make systematic if we add other approaches to computing h*/ 578 ierr = VecNorm(x1_tmp,NORM_2,&unorm);CHKERRQ(ierr); 579 } 580 ierr = VecGetOwnershipRange(w1,&start,&end);CHKERRQ(ierr); /* OwnershipRange is used by ghosted x! */ 581 582 /* Set w1 = F(x1) */ 583 if (coloring->F) { 584 w1 = coloring->F; /* use already computed value of function */ 585 coloring->F = 0; 586 } else { 587 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 588 ierr = (*f)(sctx,x1_tmp,w1,fctx);CHKERRQ(ierr); 589 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 590 } 591 592 if (!coloring->w3) { 593 ierr = VecDuplicate(x1_tmp,&coloring->w3);CHKERRQ(ierr); 594 ierr = PetscLogObjectParent(coloring,coloring->w3);CHKERRQ(ierr); 595 } 596 w3 = coloring->w3; 597 598 /* Compute all the local scale factors, including ghost points */ 599 ierr = VecGetLocalSize(x1_tmp,&N);CHKERRQ(ierr); 600 ierr = VecGetArray(x1_tmp,&xx);CHKERRQ(ierr); 601 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 602 if (ctype == IS_COLORING_GHOSTED){ 603 col_start = 0; col_end = N; 604 } else if (ctype == IS_COLORING_GLOBAL){ 605 xx = xx - start; 606 vscale_array = vscale_array - start; 607 col_start = start; col_end = N + start; 608 } 609 for (col=col_start; col<col_end; col++){ 610 /* Loop over each local column, vscale[col] = 1./(epsilon*dx[col]) */ 611 if (coloring->htype[0] == 'w') { 612 dx = 1.0 + unorm; 613 } else { 614 dx = xx[col]; 615 } 616 if (dx == 0.0) dx = 1.0; 617 #if !defined(PETSC_USE_COMPLEX) 618 if (dx < umin && dx >= 0.0) dx = umin; 619 else if (dx < 0.0 && dx > -umin) dx = -umin; 620 #else 621 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 622 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 623 #endif 624 dx *= epsilon; 625 vscale_array[col] = 1.0/dx; 626 } 627 if (ctype == IS_COLORING_GLOBAL) vscale_array = vscale_array + start; 628 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 629 if (ctype == IS_COLORING_GLOBAL){ 630 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 631 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 632 } 633 634 if (coloring->vscaleforrow) { 635 vscaleforrow = coloring->vscaleforrow; 636 } else { 637 SETERRQ(PETSC_ERR_ARG_NULL,"Null Object: coloring->vscaleforrow"); 638 } 639 640 /* 641 Loop over each color 642 */ 643 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 644 for (k=0; k<coloring->ncolors; k++) { 645 coloring->currentcolor = k; 646 ierr = VecCopy(x1_tmp,w3);CHKERRQ(ierr); 647 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr); 648 if (ctype == IS_COLORING_GLOBAL) w3_array = w3_array - start; 649 /* 650 Loop over each column associated with color 651 adding the perturbation to the vector w3. 652 */ 653 for (l=0; l<coloring->ncolumns[k]; l++) { 654 col = coloring->columns[k][l]; /* local column of the matrix we are probing for */ 655 if (coloring->htype[0] == 'w') { 656 dx = 1.0 + unorm; 657 } else { 658 dx = xx[col]; 659 } 660 if (dx == 0.0) dx = 1.0; 661 #if !defined(PETSC_USE_COMPLEX) 662 if (dx < umin && dx >= 0.0) dx = umin; 663 else if (dx < 0.0 && dx > -umin) dx = -umin; 664 #else 665 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 666 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 667 #endif 668 dx *= epsilon; 669 if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter"); 670 w3_array[col] += dx; 671 } 672 if (ctype == IS_COLORING_GLOBAL) w3_array = w3_array + start; 673 ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 674 675 /* 676 Evaluate function at w3 = x1 + dx (here dx is a vector of perturbations) 677 w2 = F(x1 + dx) - F(x1) 678 */ 679 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 680 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 681 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 682 ierr = VecAXPY(w2,-1.0,w1);CHKERRQ(ierr); 683 684 /* 685 Loop over rows of vector, putting results into Jacobian matrix 686 */ 687 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 688 for (l=0; l<coloring->nrows[k]; l++) { 689 row = coloring->rows[k][l]; /* local row index */ 690 col = coloring->columnsforrow[k][l]; /* global column index */ 691 y[row] *= vscale_array[vscaleforrow[k][l]]; 692 srow = row + start; 693 ierr = MatSetValues(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 694 } 695 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 696 } /* endof for each color */ 697 if (ctype == IS_COLORING_GLOBAL) xx = xx + start; 698 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 699 ierr = VecRestoreArray(x1_tmp,&xx);CHKERRQ(ierr); 700 701 coloring->currentcolor = -1; 702 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 703 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 704 ierr = PetscLogEventEnd(MAT_FDColoringApply,coloring,J,x1,0);CHKERRQ(ierr); 705 706 flg = PETSC_FALSE; 707 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_null_space_test",&flg,PETSC_NULL);CHKERRQ(ierr); 708 if (flg) { 709 ierr = MatNullSpaceTest(J->nullsp,J,PETSC_NULL);CHKERRQ(ierr); 710 } 711 ierr = MatFDColoringView_Private(coloring);CHKERRQ(ierr); 712 PetscFunctionReturn(0); 713 } 714 715 #undef __FUNCT__ 716 #define __FUNCT__ "MatFDColoringApplyTS" 717 /*@ 718 MatFDColoringApplyTS - Given a matrix for which a MatFDColoring context 719 has been created, computes the Jacobian for a function via finite differences. 720 721 Collective on Mat, MatFDColoring, and Vec 722 723 Input Parameters: 724 + mat - location to store Jacobian 725 . coloring - coloring context created with MatFDColoringCreate() 726 . x1 - location at which Jacobian is to be computed 727 - sctx - context required by function, if this is being used with the TS solver then it is TS object, otherwise it is null 728 729 Level: intermediate 730 731 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringView(), MatFDColoringSetFunction() 732 733 .keywords: coloring, Jacobian, finite differences 734 @*/ 735 PetscErrorCode PETSCMAT_DLLEXPORT MatFDColoringApplyTS(Mat J,MatFDColoring coloring,PetscReal t,Vec x1,MatStructure *flag,void *sctx) 736 { 737 PetscErrorCode (*f)(void*,PetscReal,Vec,Vec,void*)=(PetscErrorCode (*)(void*,PetscReal,Vec,Vec,void *))coloring->f; 738 PetscErrorCode ierr; 739 PetscInt k,N,start,end,l,row,col,srow,**vscaleforrow; 740 PetscScalar dx,*y,*xx,*w3_array; 741 PetscScalar *vscale_array; 742 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 743 Vec w1=coloring->w1,w2=coloring->w2,w3; 744 void *fctx = coloring->fctx; 745 PetscTruth flg; 746 747 PetscFunctionBegin; 748 PetscValidHeaderSpecific(J,MAT_CLASSID,1); 749 PetscValidHeaderSpecific(coloring,MAT_FDCOLORING_CLASSID,2); 750 PetscValidHeaderSpecific(x1,VEC_CLASSID,4); 751 752 ierr = PetscLogEventBegin(MAT_FDColoringApply,coloring,J,x1,0);CHKERRQ(ierr); 753 if (!coloring->w3) { 754 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 755 ierr = PetscLogObjectParent(coloring,coloring->w3);CHKERRQ(ierr); 756 } 757 w3 = coloring->w3; 758 759 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 760 flg = PETSC_FALSE; 761 ierr = PetscOptionsGetTruth(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);CHKERRQ(ierr); 762 if (flg) { 763 ierr = PetscInfo(coloring,"Not calling MatZeroEntries()\n");CHKERRQ(ierr); 764 } else { 765 PetscTruth assembled; 766 ierr = MatAssembled(J,&assembled);CHKERRQ(ierr); 767 if (assembled) { 768 ierr = MatZeroEntries(J);CHKERRQ(ierr); 769 } 770 } 771 772 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 773 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 774 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 775 ierr = (*f)(sctx,t,x1,w1,fctx);CHKERRQ(ierr); 776 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 777 778 /* 779 Compute all the scale factors and share with other processors 780 */ 781 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 782 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 783 for (k=0; k<coloring->ncolors; k++) { 784 /* 785 Loop over each column associated with color adding the 786 perturbation to the vector w3. 787 */ 788 for (l=0; l<coloring->ncolumns[k]; l++) { 789 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 790 dx = xx[col]; 791 if (dx == 0.0) dx = 1.0; 792 #if !defined(PETSC_USE_COMPLEX) 793 if (dx < umin && dx >= 0.0) dx = umin; 794 else if (dx < 0.0 && dx > -umin) dx = -umin; 795 #else 796 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 797 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 798 #endif 799 dx *= epsilon; 800 vscale_array[col] = 1.0/dx; 801 } 802 } 803 vscale_array = vscale_array - start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 804 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 805 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 806 807 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 808 else vscaleforrow = coloring->columnsforrow; 809 810 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 811 /* 812 Loop over each color 813 */ 814 for (k=0; k<coloring->ncolors; k++) { 815 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 816 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 817 /* 818 Loop over each column associated with color adding the 819 perturbation to the vector w3. 820 */ 821 for (l=0; l<coloring->ncolumns[k]; l++) { 822 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 823 dx = xx[col]; 824 if (dx == 0.0) dx = 1.0; 825 #if !defined(PETSC_USE_COMPLEX) 826 if (dx < umin && dx >= 0.0) dx = umin; 827 else if (dx < 0.0 && dx > -umin) dx = -umin; 828 #else 829 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 830 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 831 #endif 832 dx *= epsilon; 833 w3_array[col] += dx; 834 } 835 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 836 837 /* 838 Evaluate function at x1 + dx (here dx is a vector of perturbations) 839 */ 840 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 841 ierr = (*f)(sctx,t,w3,w2,fctx);CHKERRQ(ierr); 842 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 843 ierr = VecAXPY(w2,-1.0,w1);CHKERRQ(ierr); 844 845 /* 846 Loop over rows of vector, putting results into Jacobian matrix 847 */ 848 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 849 for (l=0; l<coloring->nrows[k]; l++) { 850 row = coloring->rows[k][l]; 851 col = coloring->columnsforrow[k][l]; 852 y[row] *= vscale_array[vscaleforrow[k][l]]; 853 srow = row + start; 854 ierr = MatSetValues(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 855 } 856 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 857 } 858 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 859 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 860 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 861 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 862 ierr = PetscLogEventEnd(MAT_FDColoringApply,coloring,J,x1,0);CHKERRQ(ierr); 863 PetscFunctionReturn(0); 864 } 865 866 867 868