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