1 /*$Id: fdmatrix.c,v 1.69 2000/05/18 20:36:53 balay Exp balay $*/ 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 "petsc.h" 9 #include "src/mat/matimpl.h" /*I "petscmat.h" I*/ 10 11 #undef __FUNC__ 12 #define __FUNC__ /*<a name="MatFDColoringView_Draw_Zoom"></a>*/"MatFDColoringView_Draw_Zoom" 13 static int MatFDColoringView_Draw_Zoom(Draw draw,void *Aa) 14 { 15 MatFDColoring fd = (MatFDColoring)Aa; 16 int ierr,i,j; 17 PetscReal x,y; 18 19 PetscFunctionBegin; 20 21 /* loop over colors */ 22 for (i=0; i<fd->ncolors; i++) { 23 for (j=0; j<fd->nrows[i]; j++) { 24 y = fd->M - fd->rows[i][j] - fd->rstart; 25 x = fd->columnsforrow[i][j]; 26 ierr = DrawRectangle(draw,x,y,x+1,y+1,i+1,i+1,i+1,i+1);CHKERRQ(ierr); 27 } 28 } 29 PetscFunctionReturn(0); 30 } 31 32 #undef __FUNC__ 33 #define __FUNC__ /*<a name="MatFDColoringView_Draw"></a>*/"MatFDColoringView_Draw" 34 static int MatFDColoringView_Draw(MatFDColoring fd,Viewer viewer) 35 { 36 int ierr; 37 PetscTruth isnull; 38 Draw draw; 39 PetscReal xr,yr,xl,yl,h,w; 40 41 PetscFunctionBegin; 42 ierr = ViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 43 ierr = DrawIsNull(draw,&isnull);CHKERRQ(ierr); if (isnull) PetscFunctionReturn(0); 44 45 ierr = PetscObjectCompose((PetscObject)fd,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 46 47 xr = fd->N; yr = fd->M; h = yr/10.0; w = xr/10.0; 48 xr += w; yr += h; xl = -w; yl = -h; 49 ierr = DrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 50 ierr = DrawZoom(draw,MatFDColoringView_Draw_Zoom,fd);CHKERRQ(ierr); 51 ierr = PetscObjectCompose((PetscObject)fd,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 52 PetscFunctionReturn(0); 53 } 54 55 #undef __FUNC__ 56 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringView" 57 /*@C 58 MatFDColoringView - Views a finite difference coloring context. 59 60 Collective on MatFDColoring 61 62 Input Parameters: 63 + c - the coloring context 64 - viewer - visualization context 65 66 Level: intermediate 67 68 Notes: 69 The available visualization contexts include 70 + VIEWER_STDOUT_SELF - standard output (default) 71 . VIEWER_STDOUT_WORLD - synchronized standard 72 output where only the first processor opens 73 the file. All other processors send their 74 data to the first processor to print. 75 - VIEWER_DRAW_WORLD - graphical display of nonzero structure 76 77 Notes: 78 Since PETSc uses only a small number of basic colors (currently 33), if the coloring 79 involves moe than 33 then some seemingly identical colors are displayed making it look 80 like an illegal coloring. This is just a graphical artifact. 81 82 .seealso: MatFDColoringCreate() 83 84 .keywords: Mat, finite differences, coloring, view 85 @*/ 86 int MatFDColoringView(MatFDColoring c,Viewer viewer) 87 { 88 int i,j,format,ierr; 89 PetscTruth isdraw,isascii; 90 91 PetscFunctionBegin; 92 PetscValidHeaderSpecific(c,MAT_FDCOLORING_COOKIE); 93 if (!viewer) viewer = VIEWER_STDOUT_(c->comm); 94 PetscValidHeaderSpecific(viewer,VIEWER_COOKIE); 95 PetscCheckSameComm(c,viewer); 96 97 ierr = PetscTypeCompare((PetscObject)viewer,DRAW_VIEWER,&isdraw);CHKERRQ(ierr); 98 ierr = PetscTypeCompare((PetscObject)viewer,ASCII_VIEWER,&isascii);CHKERRQ(ierr); 99 if (isdraw) { 100 ierr = MatFDColoringView_Draw(c,viewer);CHKERRQ(ierr); 101 } else if (isascii) { 102 ierr = ViewerASCIIPrintf(viewer,"MatFDColoring Object:\n");CHKERRQ(ierr); 103 ierr = ViewerASCIIPrintf(viewer," Error tolerance=%g\n",c->error_rel);CHKERRQ(ierr); 104 ierr = ViewerASCIIPrintf(viewer," Umin=%g\n",c->umin);CHKERRQ(ierr); 105 ierr = ViewerASCIIPrintf(viewer," Number of colors=%d\n",c->ncolors);CHKERRQ(ierr); 106 107 ierr = ViewerGetFormat(viewer,&format);CHKERRQ(ierr); 108 if (format != VIEWER_FORMAT_ASCII_INFO) { 109 for (i=0; i<c->ncolors; i++) { 110 ierr = ViewerASCIIPrintf(viewer," Information for color %d\n",i);CHKERRQ(ierr); 111 ierr = ViewerASCIIPrintf(viewer," Number of columns %d\n",c->ncolumns[i]);CHKERRQ(ierr); 112 for (j=0; j<c->ncolumns[i]; j++) { 113 ierr = ViewerASCIIPrintf(viewer," %d\n",c->columns[i][j]);CHKERRQ(ierr); 114 } 115 ierr = ViewerASCIIPrintf(viewer," Number of rows %d\n",c->nrows[i]);CHKERRQ(ierr); 116 for (j=0; j<c->nrows[i]; j++) { 117 ierr = ViewerASCIIPrintf(viewer," %d %d \n",c->rows[i][j],c->columnsforrow[i][j]);CHKERRQ(ierr); 118 } 119 } 120 } 121 ierr = ViewerFlush(viewer);CHKERRQ(ierr); 122 } else { 123 SETERRQ1(1,1,"Viewer type %s not supported for MatFDColoring",((PetscObject)viewer)->type_name); 124 } 125 PetscFunctionReturn(0); 126 } 127 128 #undef __FUNC__ 129 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringSetParameters" 130 /*@ 131 MatFDColoringSetParameters - Sets the parameters for the sparse approximation of 132 a Jacobian matrix using finite differences. 133 134 Collective on MatFDColoring 135 136 The Jacobian is estimated with the differencing approximation 137 .vb 138 F'(u)_{:,i} = [F(u+h*dx_{i}) - F(u)]/h where 139 h = error_rel*u[i] if abs(u[i]) > umin 140 = +/- error_rel*umin otherwise, with +/- determined by the sign of u[i] 141 dx_{i} = (0, ... 1, .... 0) 142 .ve 143 144 Input Parameters: 145 + coloring - the coloring context 146 . error_rel - relative error 147 - umin - minimum allowable u-value magnitude 148 149 Level: advanced 150 151 .keywords: Mat, finite differences, coloring, set, parameters 152 153 .seealso: MatFDColoringCreate() 154 @*/ 155 int MatFDColoringSetParameters(MatFDColoring matfd,PetscReal error,PetscReal umin) 156 { 157 PetscFunctionBegin; 158 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_COOKIE); 159 160 if (error != PETSC_DEFAULT) matfd->error_rel = error; 161 if (umin != PETSC_DEFAULT) matfd->umin = umin; 162 PetscFunctionReturn(0); 163 } 164 165 #undef __FUNC__ 166 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringSetFrequency" 167 /*@ 168 MatFDColoringSetFrequency - Sets the frequency for computing new Jacobian 169 matrices. 170 171 Collective on MatFDColoring 172 173 Input Parameters: 174 + coloring - the coloring context 175 - freq - frequency (default is 1) 176 177 Options Database Keys: 178 . -mat_fd_coloring_freq <freq> - Sets coloring frequency 179 180 Level: advanced 181 182 Notes: 183 Using a modified Newton strategy, where the Jacobian remains fixed for several 184 iterations, can be cost effective in terms of overall nonlinear solution 185 efficiency. This parameter indicates that a new Jacobian will be computed every 186 <freq> nonlinear iterations. 187 188 .keywords: Mat, finite differences, coloring, set, frequency 189 190 .seealso: MatFDColoringCreate(), MatFDColoringGetFrequency() 191 @*/ 192 int MatFDColoringSetFrequency(MatFDColoring matfd,int freq) 193 { 194 PetscFunctionBegin; 195 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_COOKIE); 196 197 matfd->freq = freq; 198 PetscFunctionReturn(0); 199 } 200 201 #undef __FUNC__ 202 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringGetFrequency" 203 /*@ 204 MatFDColoringGetFrequency - Gets the frequency for computing new Jacobian 205 matrices. 206 207 Not Collective 208 209 Input Parameters: 210 . coloring - the coloring context 211 212 Output Parameters: 213 . freq - frequency (default is 1) 214 215 Options Database Keys: 216 . -mat_fd_coloring_freq <freq> - Sets coloring frequency 217 218 Level: advanced 219 220 Notes: 221 Using a modified Newton strategy, where the Jacobian remains fixed for several 222 iterations, can be cost effective in terms of overall nonlinear solution 223 efficiency. This parameter indicates that a new Jacobian will be computed every 224 <freq> nonlinear iterations. 225 226 .keywords: Mat, finite differences, coloring, get, frequency 227 228 .seealso: MatFDColoringSetFrequency() 229 @*/ 230 int MatFDColoringGetFrequency(MatFDColoring matfd,int *freq) 231 { 232 PetscFunctionBegin; 233 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_COOKIE); 234 235 *freq = matfd->freq; 236 PetscFunctionReturn(0); 237 } 238 239 #undef __FUNC__ 240 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringSetFunction" 241 /*@C 242 MatFDColoringSetFunction - Sets the function to use for computing the Jacobian. 243 244 Collective on MatFDColoring 245 246 Input Parameters: 247 + coloring - the coloring context 248 . f - the function 249 - fctx - the optional user-defined function context 250 251 Level: intermediate 252 253 Notes: 254 In Fortran you must call MatFDColoringSetFunctionSNES() for a coloring object to 255 be used with the SNES solvers and MatFDColoringSetFunctionTS() if it is to be used 256 with the TS solvers. 257 258 .keywords: Mat, Jacobian, finite differences, set, function 259 @*/ 260 int MatFDColoringSetFunction(MatFDColoring matfd,int (*f)(void),void *fctx) 261 { 262 PetscFunctionBegin; 263 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_COOKIE); 264 265 matfd->f = f; 266 matfd->fctx = fctx; 267 268 PetscFunctionReturn(0); 269 } 270 271 #undef __FUNC__ 272 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringSetFromOptions" 273 /*@ 274 MatFDColoringSetFromOptions - Sets coloring finite difference parameters from 275 the options database. 276 277 Collective on MatFDColoring 278 279 The Jacobian, F'(u), is estimated with the differencing approximation 280 .vb 281 F'(u)_{:,i} = [F(u+h*dx_{i}) - F(u)]/h where 282 h = error_rel*u[i] if abs(u[i]) > umin 283 = +/- error_rel*umin otherwise, with +/- determined by the sign of u[i] 284 dx_{i} = (0, ... 1, .... 0) 285 .ve 286 287 Input Parameter: 288 . coloring - the coloring context 289 290 Options Database Keys: 291 + -mat_fd_coloring_err <err> - Sets <err> (square root 292 of relative error in the function) 293 . -mat_fd_coloring_umin <umin> - Sets umin, the minimum allowable u-value magnitude 294 . -mat_fd_coloring_freq <freq> - Sets frequency of computing a new Jacobian 295 . -mat_fd_coloring_view - Activates basic viewing 296 . -mat_fd_coloring_view_info - Activates viewing info 297 - -mat_fd_coloring_view_draw - Activates drawing 298 299 Level: intermediate 300 301 .keywords: Mat, finite differences, parameters 302 @*/ 303 int MatFDColoringSetFromOptions(MatFDColoring matfd) 304 { 305 int ierr,freq = 1; 306 PetscReal error = PETSC_DEFAULT,umin = PETSC_DEFAULT; 307 PetscTruth flag; 308 309 PetscFunctionBegin; 310 PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_COOKIE); 311 312 ierr = OptionsGetDouble(matfd->prefix,"-mat_fd_coloring_err",&error,PETSC_NULL);CHKERRQ(ierr); 313 ierr = OptionsGetDouble(matfd->prefix,"-mat_fd_coloring_umin",&umin,PETSC_NULL);CHKERRQ(ierr); 314 ierr = MatFDColoringSetParameters(matfd,error,umin);CHKERRQ(ierr); 315 ierr = OptionsGetInt(matfd->prefix,"-mat_fd_coloring_freq",&freq,PETSC_NULL);CHKERRQ(ierr); 316 ierr = MatFDColoringSetFrequency(matfd,freq);CHKERRQ(ierr); 317 ierr = OptionsHasName(PETSC_NULL,"-help",&flag);CHKERRQ(ierr); 318 if (flag) { 319 ierr = MatFDColoringPrintHelp(matfd);CHKERRQ(ierr); 320 } 321 PetscFunctionReturn(0); 322 } 323 324 #undef __FUNC__ 325 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringPrintHelp" 326 /*@ 327 MatFDColoringPrintHelp - Prints help message for matrix finite difference calculations 328 using coloring. 329 330 Collective on MatFDColoring 331 332 Input Parameter: 333 . fdcoloring - the MatFDColoring context 334 335 Level: intermediate 336 337 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringSetFromOptions() 338 @*/ 339 int MatFDColoringPrintHelp(MatFDColoring fd) 340 { 341 int ierr; 342 343 PetscFunctionBegin; 344 PetscValidHeaderSpecific(fd,MAT_FDCOLORING_COOKIE); 345 346 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_err <err>: set sqrt rel tol in function, defaults to %g\n",fd->error_rel);CHKERRQ(ierr); 347 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_umin <umin>: see users manual, defaults to %d\n",fd->umin);CHKERRQ(ierr); 348 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_freq <freq>: frequency that Jacobian is recomputed, defaults to %d\n",fd->freq);CHKERRQ(ierr); 349 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_view\n");CHKERRQ(ierr); 350 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_view_draw\n");CHKERRQ(ierr); 351 ierr = (*PetscHelpPrintf)(fd->comm,"-mat_fd_coloring_view_info\n");CHKERRQ(ierr); 352 PetscFunctionReturn(0); 353 } 354 355 #undef __FUNC__ 356 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringView_Private" 357 int MatFDColoringView_Private(MatFDColoring fd) 358 { 359 int ierr; 360 PetscTruth flg; 361 362 PetscFunctionBegin; 363 ierr = OptionsHasName(PETSC_NULL,"-mat_fd_coloring_view",&flg);CHKERRQ(ierr); 364 if (flg) { 365 ierr = MatFDColoringView(fd,VIEWER_STDOUT_(fd->comm));CHKERRQ(ierr); 366 } 367 ierr = OptionsHasName(PETSC_NULL,"-mat_fd_coloring_view_info",&flg);CHKERRQ(ierr); 368 if (flg) { 369 ierr = ViewerPushFormat(VIEWER_STDOUT_(fd->comm),VIEWER_FORMAT_ASCII_INFO,PETSC_NULL);CHKERRQ(ierr); 370 ierr = MatFDColoringView(fd,VIEWER_STDOUT_(fd->comm));CHKERRQ(ierr); 371 ierr = ViewerPopFormat(VIEWER_STDOUT_(fd->comm));CHKERRQ(ierr); 372 } 373 ierr = OptionsHasName(PETSC_NULL,"-mat_fd_coloring_view_draw",&flg);CHKERRQ(ierr); 374 if (flg) { 375 ierr = MatFDColoringView(fd,VIEWER_DRAW_(fd->comm));CHKERRQ(ierr); 376 ierr = ViewerFlush(VIEWER_DRAW_(fd->comm));CHKERRQ(ierr); 377 } 378 PetscFunctionReturn(0); 379 } 380 381 #undef __FUNC__ 382 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringCreate" 383 /*@C 384 MatFDColoringCreate - Creates a matrix coloring context for finite difference 385 computation of Jacobians. 386 387 Collective on Mat 388 389 Input Parameters: 390 + mat - the matrix containing the nonzero structure of the Jacobian 391 - iscoloring - the coloring of the matrix 392 393 Output Parameter: 394 . color - the new coloring context 395 396 Options Database Keys: 397 + -mat_fd_coloring_view - Activates basic viewing or coloring 398 . -mat_fd_coloring_view_draw - Activates drawing of coloring 399 - -mat_fd_coloring_view_info - Activates viewing of coloring info 400 401 Level: intermediate 402 403 .seealso: MatFDColoringDestroy(),SNESDefaultComputeJacobianColor(), ISColoringCreate(), 404 MatFDColoringSetFunction(), MatFDColoringSetFromOptions() 405 @*/ 406 int MatFDColoringCreate(Mat mat,ISColoring iscoloring,MatFDColoring *color) 407 { 408 MatFDColoring c; 409 MPI_Comm comm; 410 int ierr,M,N; 411 412 PetscFunctionBegin; 413 PLogEventBegin(MAT_FDColoringCreate,mat,0,0,0); 414 ierr = MatGetSize(mat,&M,&N);CHKERRQ(ierr); 415 if (M != N) SETERRQ(PETSC_ERR_SUP,0,"Only for square matrices"); 416 417 ierr = PetscObjectGetComm((PetscObject)mat,&comm);CHKERRQ(ierr); 418 PetscHeaderCreate(c,_p_MatFDColoring,int,MAT_FDCOLORING_COOKIE,0,"MatFDColoring",comm,MatFDColoringDestroy,MatFDColoringView); 419 PLogObjectCreate(c); 420 421 if (mat->ops->fdcoloringcreate) { 422 ierr = (*mat->ops->fdcoloringcreate)(mat,iscoloring,c);CHKERRQ(ierr); 423 } else { 424 SETERRQ(PETSC_ERR_SUP,0,"Code not yet written for this matrix type"); 425 } 426 427 c->error_rel = 1.e-8; 428 c->umin = 1.e-6; 429 c->freq = 1; 430 431 ierr = MatFDColoringView_Private(c);CHKERRQ(ierr); 432 433 *color = c; 434 PLogEventEnd(MAT_FDColoringCreate,mat,0,0,0); 435 PetscFunctionReturn(0); 436 } 437 438 #undef __FUNC__ 439 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringDestroy" 440 /*@C 441 MatFDColoringDestroy - Destroys a matrix coloring context that was created 442 via MatFDColoringCreate(). 443 444 Collective on MatFDColoring 445 446 Input Parameter: 447 . c - coloring context 448 449 Level: intermediate 450 451 .seealso: MatFDColoringCreate() 452 @*/ 453 int MatFDColoringDestroy(MatFDColoring c) 454 { 455 int i,ierr; 456 457 PetscFunctionBegin; 458 if (--c->refct > 0) PetscFunctionReturn(0); 459 460 for (i=0; i<c->ncolors; i++) { 461 if (c->columns[i]) {ierr = PetscFree(c->columns[i]);CHKERRQ(ierr);} 462 if (c->rows[i]) {ierr = PetscFree(c->rows[i]);CHKERRQ(ierr);} 463 if (c->columnsforrow[i]) {ierr = PetscFree(c->columnsforrow[i]);CHKERRQ(ierr);} 464 if (c->vscaleforrow && c->vscaleforrow[i]) {ierr = PetscFree(c->vscaleforrow[i]);CHKERRQ(ierr);} 465 } 466 ierr = PetscFree(c->ncolumns);CHKERRQ(ierr); 467 ierr = PetscFree(c->columns);CHKERRQ(ierr); 468 ierr = PetscFree(c->nrows);CHKERRQ(ierr); 469 ierr = PetscFree(c->rows);CHKERRQ(ierr); 470 ierr = PetscFree(c->columnsforrow);CHKERRQ(ierr); 471 if (c->vscaleforrow) {ierr = PetscFree(c->vscaleforrow);CHKERRQ(ierr);} 472 if (c->vscale) {ierr = VecDestroy(c->vscale);CHKERRQ(ierr);} 473 if (c->w1) { 474 ierr = VecDestroy(c->w1);CHKERRQ(ierr); 475 ierr = VecDestroy(c->w2);CHKERRQ(ierr); 476 ierr = VecDestroy(c->w3);CHKERRQ(ierr); 477 } 478 PLogObjectDestroy(c); 479 PetscHeaderDestroy(c); 480 PetscFunctionReturn(0); 481 } 482 483 484 #undef __FUNC__ 485 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringApply" 486 /*@ 487 MatFDColoringApply - Given a matrix for which a MatFDColoring context 488 has been created, computes the Jacobian for a function via finite differences. 489 490 Collective on MatFDColoring 491 492 Input Parameters: 493 + mat - location to store Jacobian 494 . coloring - coloring context created with MatFDColoringCreate() 495 . x1 - location at which Jacobian is to be computed 496 - sctx - optional context required by function (actually a SNES context) 497 498 Options Database Keys: 499 . -mat_fd_coloring_freq <freq> - Sets coloring frequency 500 501 Level: intermediate 502 503 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringView() 504 505 .keywords: coloring, Jacobian, finite differences 506 @*/ 507 int MatFDColoringApply(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 508 { 509 int (*f)(void *,Vec,Vec,void*)= (int (*)(void *,Vec,Vec,void *))coloring->f; 510 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow; 511 Scalar dx,mone = -1.0,*y,*xx,*w3_array; 512 Scalar *vscale_array; 513 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 514 MPI_Comm comm = coloring->comm; 515 Vec w1,w2,w3; 516 void *fctx = coloring->fctx; 517 PetscTruth flg; 518 519 PetscFunctionBegin; 520 PetscValidHeaderSpecific(J,MAT_COOKIE); 521 PetscValidHeaderSpecific(coloring,MAT_FDCOLORING_COOKIE); 522 PetscValidHeaderSpecific(x1,VEC_COOKIE); 523 524 PLogEventBegin(MAT_FDColoringApply,coloring,J,x1,0); 525 if (!coloring->w1) { 526 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 527 PLogObjectParent(coloring,coloring->w1); 528 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 529 PLogObjectParent(coloring,coloring->w2); 530 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 531 PLogObjectParent(coloring,coloring->w3); 532 } 533 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 534 535 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 536 ierr = OptionsHasName(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 537 if (flg) { 538 PLogInfo(coloring,"MatFDColoringApply: Not calling MatZeroEntries()\n"); 539 } else { 540 ierr = MatZeroEntries(J);CHKERRQ(ierr); 541 } 542 543 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 544 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 545 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 546 547 /* 548 Compute all the scale factors and share with other processors 549 */ 550 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 551 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 552 for (k=0; k<coloring->ncolors; k++) { 553 /* 554 Loop over each column associated with color adding the 555 perturbation to the vector w3. 556 */ 557 for (l=0; l<coloring->ncolumns[k]; l++) { 558 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 559 dx = xx[col]; 560 if (dx == 0.0) dx = 1.0; 561 #if !defined(PETSC_USE_COMPLEX) 562 if (dx < umin && dx >= 0.0) dx = umin; 563 else if (dx < 0.0 && dx > -umin) dx = -umin; 564 #else 565 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 566 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 567 #endif 568 dx *= epsilon; 569 vscale_array[col] = 1.0/dx; 570 } 571 } 572 vscale_array = vscale_array - start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 573 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 574 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 575 576 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 577 else vscaleforrow = coloring->columnsforrow; 578 579 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 580 /* 581 Loop over each color 582 */ 583 for (k=0; k<coloring->ncolors; k++) { 584 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 585 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 586 /* 587 Loop over each column associated with color adding the 588 perturbation to the vector w3. 589 */ 590 for (l=0; l<coloring->ncolumns[k]; l++) { 591 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 592 dx = xx[col]; 593 if (dx == 0.0) dx = 1.0; 594 #if !defined(PETSC_USE_COMPLEX) 595 if (dx < umin && dx >= 0.0) dx = umin; 596 else if (dx < 0.0 && dx > -umin) dx = -umin; 597 #else 598 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 599 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 600 #endif 601 dx *= epsilon; 602 w3_array[col] += dx; 603 } 604 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 605 606 /* 607 Evaluate function at x1 + dx (here dx is a vector of perturbations) 608 */ 609 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 610 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 611 612 /* 613 Loop over rows of vector, putting results into Jacobian matrix 614 */ 615 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 616 for (l=0; l<coloring->nrows[k]; l++) { 617 row = coloring->rows[k][l]; 618 col = coloring->columnsforrow[k][l]; 619 y[row] *= vscale_array[vscaleforrow[k][l]]; 620 srow = row + start; 621 ierr = MatSetValues(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 622 } 623 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 624 } 625 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 626 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 627 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 628 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 629 PLogEventEnd(MAT_FDColoringApply,coloring,J,x1,0); 630 PetscFunctionReturn(0); 631 } 632 633 #undef __FUNC__ 634 #define __FUNC__ /*<a name=""></a>*/"MatFDColoringApplyTS" 635 /*@ 636 MatFDColoringApplyTS - Given a matrix for which a MatFDColoring context 637 has been created, computes the Jacobian for a function via finite differences. 638 639 Collective on Mat, MatFDColoring, and Vec 640 641 Input Parameters: 642 + mat - location to store Jacobian 643 . coloring - coloring context created with MatFDColoringCreate() 644 . x1 - location at which Jacobian is to be computed 645 - sctx - optional context required by function (actually a SNES context) 646 647 Options Database Keys: 648 . -mat_fd_coloring_freq <freq> - Sets coloring frequency 649 650 Level: intermediate 651 652 .seealso: MatFDColoringCreate(), MatFDColoringDestroy(), MatFDColoringView() 653 654 .keywords: coloring, Jacobian, finite differences 655 @*/ 656 int MatFDColoringApplyTS(Mat J,MatFDColoring coloring,PetscReal t,Vec x1,MatStructure *flag,void *sctx) 657 { 658 int (*f)(void *,PetscReal,Vec,Vec,void*)= (int (*)(void *,PetscReal,Vec,Vec,void *))coloring->f; 659 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow; 660 Scalar dx,mone = -1.0,*y,*xx,*w3_array; 661 Scalar *vscale_array; 662 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 663 MPI_Comm comm = coloring->comm; 664 Vec w1,w2,w3; 665 void *fctx = coloring->fctx; 666 PetscTruth flg; 667 668 PetscFunctionBegin; 669 PetscValidHeaderSpecific(J,MAT_COOKIE); 670 PetscValidHeaderSpecific(coloring,MAT_FDCOLORING_COOKIE); 671 PetscValidHeaderSpecific(x1,VEC_COOKIE); 672 673 PLogEventBegin(MAT_FDColoringApply,coloring,J,x1,0); 674 if (!coloring->w1) { 675 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 676 PLogObjectParent(coloring,coloring->w1); 677 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 678 PLogObjectParent(coloring,coloring->w2); 679 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 680 PLogObjectParent(coloring,coloring->w3); 681 } 682 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 683 684 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 685 ierr = OptionsHasName(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 686 if (flg) { 687 PLogInfo(coloring,"MatFDColoringApply: Not calling MatZeroEntries()\n"); 688 } else { 689 ierr = MatZeroEntries(J);CHKERRQ(ierr); 690 } 691 692 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 693 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 694 ierr = (*f)(sctx,t,x1,w1,fctx);CHKERRQ(ierr); 695 696 /* 697 Compute all the scale factors and share with other processors 698 */ 699 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 700 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 701 for (k=0; k<coloring->ncolors; k++) { 702 /* 703 Loop over each column associated with color adding the 704 perturbation to the vector w3. 705 */ 706 for (l=0; l<coloring->ncolumns[k]; l++) { 707 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 708 dx = xx[col]; 709 if (dx == 0.0) dx = 1.0; 710 #if !defined(PETSC_USE_COMPLEX) 711 if (dx < umin && dx >= 0.0) dx = umin; 712 else if (dx < 0.0 && dx > -umin) dx = -umin; 713 #else 714 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 715 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 716 #endif 717 dx *= epsilon; 718 vscale_array[col] = 1.0/dx; 719 } 720 } 721 vscale_array = vscale_array - start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 722 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 723 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 724 725 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 726 else vscaleforrow = coloring->columnsforrow; 727 728 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 729 /* 730 Loop over each color 731 */ 732 for (k=0; k<coloring->ncolors; k++) { 733 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 734 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 735 /* 736 Loop over each column associated with color adding the 737 perturbation to the vector w3. 738 */ 739 for (l=0; l<coloring->ncolumns[k]; l++) { 740 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 741 dx = xx[col]; 742 if (dx == 0.0) dx = 1.0; 743 #if !defined(PETSC_USE_COMPLEX) 744 if (dx < umin && dx >= 0.0) dx = umin; 745 else if (dx < 0.0 && dx > -umin) dx = -umin; 746 #else 747 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 748 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 749 #endif 750 dx *= epsilon; 751 w3_array[col] += dx; 752 } 753 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 754 755 /* 756 Evaluate function at x1 + dx (here dx is a vector of perturbations) 757 */ 758 ierr = (*f)(sctx,t,w3,w2,fctx);CHKERRQ(ierr); 759 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 760 761 /* 762 Loop over rows of vector, putting results into Jacobian matrix 763 */ 764 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 765 for (l=0; l<coloring->nrows[k]; l++) { 766 row = coloring->rows[k][l]; 767 col = coloring->columnsforrow[k][l]; 768 y[row] *= vscale_array[vscaleforrow[k][l]]; 769 srow = row + start; 770 ierr = MatSetValues(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 771 } 772 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 773 } 774 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 775 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 776 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 777 PLogEventEnd(MAT_FDColoringApply,coloring,J,x1,0); 778 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 779 PetscFunctionReturn(0); 780 } 781 782 783 784