1 #include <petsc/private/taoimpl.h> /*I "petsctao.h" I*/ 2 3 /*@ 4 TaoSetInitialVector - Sets the initial guess for the solve 5 6 Logically collective on Tao 7 8 Input Parameters: 9 + tao - the Tao context 10 - x0 - the initial guess 11 12 Level: beginner 13 .seealso: TaoCreate(), TaoSolve() 14 @*/ 15 16 PetscErrorCode TaoSetInitialVector(Tao tao, Vec x0) 17 { 18 PetscErrorCode ierr; 19 20 PetscFunctionBegin; 21 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 22 if (x0) { 23 PetscValidHeaderSpecific(x0,VEC_CLASSID,2); 24 PetscObjectReference((PetscObject)x0); 25 } 26 ierr = VecDestroy(&tao->solution);CHKERRQ(ierr); 27 tao->solution = x0; 28 PetscFunctionReturn(0); 29 } 30 31 PetscErrorCode TaoTestGradient(Tao tao,Vec x,Vec g1) 32 { 33 Vec g2,g3; 34 PetscBool complete_print = PETSC_FALSE,test = PETSC_FALSE; 35 PetscReal hcnorm,fdnorm,hcmax,fdmax,diffmax,diffnorm; 36 PetscScalar dot; 37 MPI_Comm comm; 38 PetscViewer viewer,mviewer; 39 PetscViewerFormat format; 40 PetscInt tabs; 41 static PetscBool directionsprinted = PETSC_FALSE; 42 PetscErrorCode ierr; 43 44 PetscFunctionBegin; 45 ierr = PetscObjectOptionsBegin((PetscObject)tao);CHKERRQ(ierr); 46 ierr = PetscOptionsName("-tao_test_gradient","Compare hand-coded and finite difference Gradients","None",&test);CHKERRQ(ierr); 47 ierr = PetscOptionsViewer("-tao_test_gradient_view","View difference between hand-coded and finite difference Gradients element entries","None",&mviewer,&format,&complete_print);CHKERRQ(ierr); 48 ierr = PetscOptionsEnd();CHKERRQ(ierr); 49 if (!test) PetscFunctionReturn(0); 50 51 ierr = PetscObjectGetComm((PetscObject)tao,&comm);CHKERRQ(ierr); 52 ierr = PetscViewerASCIIGetStdout(comm,&viewer);CHKERRQ(ierr); 53 ierr = PetscViewerASCIIGetTab(viewer, &tabs);CHKERRQ(ierr); 54 ierr = PetscViewerASCIISetTab(viewer, ((PetscObject)tao)->tablevel);CHKERRQ(ierr); 55 ierr = PetscViewerASCIIPrintf(viewer," ---------- Testing Gradient -------------\n");CHKERRQ(ierr); 56 if (!complete_print && !directionsprinted) { 57 ierr = PetscViewerASCIIPrintf(viewer," Run with -tao_test_gradient_view and optionally -tao_test_gradient <threshold> to show difference\n");CHKERRQ(ierr); 58 ierr = PetscViewerASCIIPrintf(viewer," of hand-coded and finite difference gradient entries greater than <threshold>.\n");CHKERRQ(ierr); 59 } 60 if (!directionsprinted) { 61 ierr = PetscViewerASCIIPrintf(viewer," Testing hand-coded Gradient, if (for double precision runs) ||G - Gfd||_F/||G||_F is\n");CHKERRQ(ierr); 62 ierr = PetscViewerASCIIPrintf(viewer," O(1.e-8), the hand-coded Hessian is probably correct.\n");CHKERRQ(ierr); 63 directionsprinted = PETSC_TRUE; 64 } 65 if (complete_print) { 66 ierr = PetscViewerPushFormat(mviewer,format);CHKERRQ(ierr); 67 } 68 69 ierr = VecDuplicate(x,&g2);CHKERRQ(ierr); 70 ierr = VecDuplicate(x,&g3);CHKERRQ(ierr); 71 72 /* Compute finite difference gradient, assume the gradient is already computed by TaoComputeGradient() and put into g1 */ 73 ierr = TaoDefaultComputeGradient(tao,x,g2,NULL);CHKERRQ(ierr); 74 75 ierr = VecNorm(g2,NORM_2,&fdnorm);CHKERRQ(ierr); 76 ierr = VecNorm(g1,NORM_2,&hcnorm);CHKERRQ(ierr); 77 ierr = VecNorm(g2,NORM_INFINITY,&fdmax);CHKERRQ(ierr); 78 ierr = VecNorm(g1,NORM_INFINITY,&hcmax);CHKERRQ(ierr); 79 ierr = VecDot(g1,g2,&dot);CHKERRQ(ierr); 80 ierr = VecCopy(g1,g3);CHKERRQ(ierr); 81 ierr = VecAXPY(g3,-1.0,g2);CHKERRQ(ierr); 82 ierr = VecNorm(g3,NORM_2,&diffnorm);CHKERRQ(ierr); 83 ierr = VecNorm(g3,NORM_INFINITY,&diffmax);CHKERRQ(ierr); 84 ierr = PetscViewerASCIIPrintf(viewer," ||Gfd|| %g, ||G|| = %g, angle cosine = (Gfd'G)/||Gfd||||G|| = %g\n", (double)fdnorm, (double)hcnorm, (double)(PetscRealPart(dot)/(fdnorm*hcnorm)));CHKERRQ(ierr); 85 ierr = PetscViewerASCIIPrintf(viewer," 2-norm ||G - Gfd||/||G|| = %g, ||G - Gfd|| = %g\n",(double)(diffnorm/PetscMax(hcnorm,fdnorm)),(double)diffnorm);CHKERRQ(ierr); 86 ierr = PetscViewerASCIIPrintf(viewer," max-norm ||G - Gfd||/||G|| = %g, ||G - Gfd|| = %g\n",(double)(diffmax/PetscMax(hcmax,fdmax)),(double)diffmax);CHKERRQ(ierr); 87 88 if (complete_print) { 89 ierr = PetscViewerASCIIPrintf(viewer," Hand-coded gradient ----------\n");CHKERRQ(ierr); 90 ierr = VecView(g1,mviewer);CHKERRQ(ierr); 91 ierr = PetscViewerASCIIPrintf(viewer," Finite difference gradient ----------\n");CHKERRQ(ierr); 92 ierr = VecView(g2,mviewer);CHKERRQ(ierr); 93 ierr = PetscViewerASCIIPrintf(viewer," Hand-coded minus finite-difference gradient ----------\n");CHKERRQ(ierr); 94 ierr = VecView(g3,mviewer);CHKERRQ(ierr); 95 } 96 ierr = VecDestroy(&g2);CHKERRQ(ierr); 97 ierr = VecDestroy(&g3);CHKERRQ(ierr); 98 99 if (complete_print) { 100 ierr = PetscViewerPopFormat(mviewer);CHKERRQ(ierr); 101 } 102 ierr = PetscViewerASCIISetTab(viewer,tabs);CHKERRQ(ierr); 103 PetscFunctionReturn(0); 104 } 105 106 /*@ 107 TaoComputeGradient - Computes the gradient of the objective function 108 109 Collective on Tao 110 111 Input Parameters: 112 + tao - the Tao context 113 - X - input vector 114 115 Output Parameter: 116 . G - gradient vector 117 118 Options Database Keys: 119 + -tao_test_gradient - compare the user provided gradient with one compute via finite differences to check for errors 120 - -tao_test_gradient_view - display the user provided gradient, the finite difference gradient and the difference between them to help users detect the location of errors in the user provided gradient 121 122 Notes: 123 TaoComputeGradient() is typically used within minimization implementations, 124 so most users would not generally call this routine themselves. 125 126 Level: advanced 127 128 .seealso: TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetGradientRoutine() 129 @*/ 130 PetscErrorCode TaoComputeGradient(Tao tao, Vec X, Vec G) 131 { 132 PetscErrorCode ierr; 133 PetscReal dummy; 134 135 PetscFunctionBegin; 136 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 137 PetscValidHeaderSpecific(X,VEC_CLASSID,2); 138 PetscValidHeaderSpecific(G,VEC_CLASSID,2); 139 PetscCheckSameComm(tao,1,X,2); 140 PetscCheckSameComm(tao,1,G,3); 141 ierr = VecLockPush(X);CHKERRQ(ierr); 142 if (tao->ops->computegradient) { 143 ierr = PetscLogEventBegin(TAO_GradientEval,tao,X,G,NULL);CHKERRQ(ierr); 144 PetscStackPush("Tao user gradient evaluation routine"); 145 ierr = (*tao->ops->computegradient)(tao,X,G,tao->user_gradP);CHKERRQ(ierr); 146 PetscStackPop; 147 ierr = PetscLogEventEnd(TAO_GradientEval,tao,X,G,NULL);CHKERRQ(ierr); 148 tao->ngrads++; 149 } else if (tao->ops->computeobjectiveandgradient) { 150 ierr = PetscLogEventBegin(TAO_ObjGradEval,tao,X,G,NULL);CHKERRQ(ierr); 151 PetscStackPush("Tao user objective/gradient evaluation routine"); 152 ierr = (*tao->ops->computeobjectiveandgradient)(tao,X,&dummy,G,tao->user_objgradP);CHKERRQ(ierr); 153 PetscStackPop; 154 ierr = PetscLogEventEnd(TAO_ObjGradEval,tao,X,G,NULL);CHKERRQ(ierr); 155 tao->nfuncgrads++; 156 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetGradientRoutine() has not been called"); 157 ierr = VecLockPop(X);CHKERRQ(ierr); 158 159 ierr = TaoTestGradient(tao,X,G);CHKERRQ(ierr); 160 PetscFunctionReturn(0); 161 } 162 163 /*@ 164 TaoComputeObjective - Computes the objective function value at a given point 165 166 Collective on Tao 167 168 Input Parameters: 169 + tao - the Tao context 170 - X - input vector 171 172 Output Parameter: 173 . f - Objective value at X 174 175 Notes: 176 TaoComputeObjective() is typically used within minimization implementations, 177 so most users would not generally call this routine themselves. 178 179 Level: advanced 180 181 .seealso: TaoComputeGradient(), TaoComputeObjectiveAndGradient(), TaoSetObjectiveRoutine() 182 @*/ 183 PetscErrorCode TaoComputeObjective(Tao tao, Vec X, PetscReal *f) 184 { 185 PetscErrorCode ierr; 186 Vec temp; 187 188 PetscFunctionBegin; 189 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 190 PetscValidHeaderSpecific(X,VEC_CLASSID,2); 191 PetscCheckSameComm(tao,1,X,2); 192 ierr = VecLockPush(X);CHKERRQ(ierr); 193 if (tao->ops->computeobjective) { 194 ierr = PetscLogEventBegin(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 195 PetscStackPush("Tao user objective evaluation routine"); 196 ierr = (*tao->ops->computeobjective)(tao,X,f,tao->user_objP);CHKERRQ(ierr); 197 PetscStackPop; 198 ierr = PetscLogEventEnd(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 199 tao->nfuncs++; 200 } else if (tao->ops->computeobjectiveandgradient) { 201 ierr = PetscInfo(tao,"Duplicating variable vector in order to call func/grad routine\n");CHKERRQ(ierr); 202 ierr = VecDuplicate(X,&temp);CHKERRQ(ierr); 203 ierr = PetscLogEventBegin(TAO_ObjGradEval,tao,X,NULL,NULL);CHKERRQ(ierr); 204 PetscStackPush("Tao user objective/gradient evaluation routine"); 205 ierr = (*tao->ops->computeobjectiveandgradient)(tao,X,f,temp,tao->user_objgradP);CHKERRQ(ierr); 206 PetscStackPop; 207 ierr = PetscLogEventEnd(TAO_ObjGradEval,tao,X,NULL,NULL);CHKERRQ(ierr); 208 ierr = VecDestroy(&temp);CHKERRQ(ierr); 209 tao->nfuncgrads++; 210 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetObjectiveRoutine() has not been called"); 211 ierr = PetscInfo1(tao,"TAO Function evaluation: %20.19e\n",(double)(*f));CHKERRQ(ierr); 212 ierr = VecLockPop(X);CHKERRQ(ierr); 213 PetscFunctionReturn(0); 214 } 215 216 /*@ 217 TaoComputeObjectiveAndGradient - Computes the objective function value at a given point 218 219 Collective on Tao 220 221 Input Parameters: 222 + tao - the Tao context 223 - X - input vector 224 225 Output Parameter: 226 + f - Objective value at X 227 - g - Gradient vector at X 228 229 Notes: 230 TaoComputeObjectiveAndGradient() is typically used within minimization implementations, 231 so most users would not generally call this routine themselves. 232 233 Level: advanced 234 235 .seealso: TaoComputeGradient(), TaoComputeObjectiveAndGradient(), TaoSetObjectiveRoutine() 236 @*/ 237 PetscErrorCode TaoComputeObjectiveAndGradient(Tao tao, Vec X, PetscReal *f, Vec G) 238 { 239 PetscErrorCode ierr; 240 241 PetscFunctionBegin; 242 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 243 PetscValidHeaderSpecific(X,VEC_CLASSID,2); 244 PetscValidHeaderSpecific(G,VEC_CLASSID,4); 245 PetscCheckSameComm(tao,1,X,2); 246 PetscCheckSameComm(tao,1,G,4); 247 ierr = VecLockPush(X);CHKERRQ(ierr); 248 if (tao->ops->computeobjectiveandgradient) { 249 ierr = PetscLogEventBegin(TAO_ObjGradEval,tao,X,G,NULL);CHKERRQ(ierr); 250 if (tao->ops->computegradient == TaoDefaultComputeGradient) { 251 ierr = TaoComputeObjective(tao,X,f);CHKERRQ(ierr); 252 ierr = TaoDefaultComputeGradient(tao,X,G,NULL);CHKERRQ(ierr); 253 } else { 254 PetscStackPush("Tao user objective/gradient evaluation routine"); 255 ierr = (*tao->ops->computeobjectiveandgradient)(tao,X,f,G,tao->user_objgradP);CHKERRQ(ierr); 256 PetscStackPop; 257 } 258 ierr = PetscLogEventEnd(TAO_ObjGradEval,tao,X,G,NULL);CHKERRQ(ierr); 259 tao->nfuncgrads++; 260 } else if (tao->ops->computeobjective && tao->ops->computegradient) { 261 ierr = PetscLogEventBegin(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 262 PetscStackPush("Tao user objective evaluation routine"); 263 ierr = (*tao->ops->computeobjective)(tao,X,f,tao->user_objP);CHKERRQ(ierr); 264 PetscStackPop; 265 ierr = PetscLogEventEnd(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 266 tao->nfuncs++; 267 ierr = PetscLogEventBegin(TAO_GradientEval,tao,X,G,NULL);CHKERRQ(ierr); 268 PetscStackPush("Tao user gradient evaluation routine"); 269 ierr = (*tao->ops->computegradient)(tao,X,G,tao->user_gradP);CHKERRQ(ierr); 270 PetscStackPop; 271 ierr = PetscLogEventEnd(TAO_GradientEval,tao,X,G,NULL);CHKERRQ(ierr); 272 tao->ngrads++; 273 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetObjectiveRoutine() or TaoSetGradientRoutine() not set"); 274 ierr = PetscInfo1(tao,"TAO Function evaluation: %20.19e\n",(double)(*f));CHKERRQ(ierr); 275 ierr = VecLockPop(X);CHKERRQ(ierr); 276 277 ierr = TaoTestGradient(tao,X,G);CHKERRQ(ierr); 278 PetscFunctionReturn(0); 279 } 280 281 /*@C 282 TaoSetObjectiveRoutine - Sets the function evaluation routine for minimization 283 284 Logically collective on Tao 285 286 Input Parameter: 287 + tao - the Tao context 288 . func - the objective function 289 - ctx - [optional] user-defined context for private data for the function evaluation 290 routine (may be NULL) 291 292 Calling sequence of func: 293 $ func (Tao tao, Vec x, PetscReal *f, void *ctx); 294 295 + x - input vector 296 . f - function value 297 - ctx - [optional] user-defined function context 298 299 Level: beginner 300 301 .seealso: TaoSetGradientRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine() 302 @*/ 303 PetscErrorCode TaoSetObjectiveRoutine(Tao tao, PetscErrorCode (*func)(Tao, Vec, PetscReal*,void*),void *ctx) 304 { 305 PetscFunctionBegin; 306 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 307 tao->user_objP = ctx; 308 tao->ops->computeobjective = func; 309 PetscFunctionReturn(0); 310 } 311 312 /*@C 313 TaoSetResidualRoutine - Sets the residual evaluation routine for least-square applications 314 315 Logically collective on Tao 316 317 Input Parameter: 318 + tao - the Tao context 319 . func - the residual evaluation routine 320 - ctx - [optional] user-defined context for private data for the function evaluation 321 routine (may be NULL) 322 323 Calling sequence of func: 324 $ func (Tao tao, Vec x, Vec f, void *ctx); 325 326 + x - input vector 327 . f - function value vector 328 - ctx - [optional] user-defined function context 329 330 Level: beginner 331 332 .seealso: TaoSetObjectiveRoutine(), TaoSetJacobianRoutine() 333 @*/ 334 PetscErrorCode TaoSetResidualRoutine(Tao tao, Vec res, PetscErrorCode (*func)(Tao, Vec, Vec, void*),void *ctx) 335 { 336 PetscFunctionBegin; 337 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 338 PetscValidHeaderSpecific(res, VEC_CLASSID,2); 339 tao->user_lsresP = ctx; 340 tao->ls_res = res; 341 tao->ops->computeresidual = func; 342 PetscFunctionReturn(0); 343 } 344 345 /*@ 346 TaoComputeResidual - Computes a least-squares residual vector at a given point 347 348 Collective on Tao 349 350 Input Parameters: 351 + tao - the Tao context 352 - X - input vector 353 354 Output Parameter: 355 . f - Objective vector at X 356 357 Notes: 358 TaoComputeResidual() is typically used within minimization implementations, 359 so most users would not generally call this routine themselves. 360 361 Level: advanced 362 363 .seealso: TaoSetResidualRoutine() 364 @*/ 365 PetscErrorCode TaoComputeResidual(Tao tao, Vec X, Vec F) 366 { 367 PetscErrorCode ierr; 368 369 PetscFunctionBegin; 370 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 371 PetscValidHeaderSpecific(X,VEC_CLASSID,2); 372 PetscValidHeaderSpecific(F,VEC_CLASSID,3); 373 PetscCheckSameComm(tao,1,X,2); 374 PetscCheckSameComm(tao,1,F,3); 375 if (tao->ops->computeresidual) { 376 ierr = PetscLogEventBegin(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 377 PetscStackPush("Tao user least-squares residual evaluation routine"); 378 ierr = (*tao->ops->computeresidual)(tao,X,F,tao->user_lsresP);CHKERRQ(ierr); 379 PetscStackPop; 380 ierr = PetscLogEventEnd(TAO_ObjectiveEval,tao,X,NULL,NULL);CHKERRQ(ierr); 381 tao->nfuncs++; 382 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"TaoSetResidualRoutine() has not been called"); 383 ierr = PetscInfo(tao,"TAO least-squares residual evaluation.\n");CHKERRQ(ierr); 384 PetscFunctionReturn(0); 385 } 386 387 /*@C 388 TaoSetGradientRoutine - Sets the gradient evaluation routine for minimization 389 390 Logically collective on Tao 391 392 Input Parameter: 393 + tao - the Tao context 394 . func - the gradient function 395 - ctx - [optional] user-defined context for private data for the gradient evaluation 396 routine (may be NULL) 397 398 Calling sequence of func: 399 $ func (Tao tao, Vec x, Vec g, void *ctx); 400 401 + x - input vector 402 . g - gradient value (output) 403 - ctx - [optional] user-defined function context 404 405 Level: beginner 406 407 .seealso: TaoSetObjectiveRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine() 408 @*/ 409 PetscErrorCode TaoSetGradientRoutine(Tao tao, PetscErrorCode (*func)(Tao, Vec, Vec, void*),void *ctx) 410 { 411 PetscFunctionBegin; 412 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 413 tao->user_gradP = ctx; 414 tao->ops->computegradient = func; 415 PetscFunctionReturn(0); 416 } 417 418 /*@C 419 TaoSetObjectiveAndGradientRoutine - Sets a combined objective function and gradient evaluation routine for minimization 420 421 Logically collective on Tao 422 423 Input Parameter: 424 + tao - the Tao context 425 . func - the gradient function 426 - ctx - [optional] user-defined context for private data for the gradient evaluation 427 routine (may be NULL) 428 429 Calling sequence of func: 430 $ func (Tao tao, Vec x, PetscReal *f, Vec g, void *ctx); 431 432 + x - input vector 433 . f - objective value (output) 434 . g - gradient value (output) 435 - ctx - [optional] user-defined function context 436 437 Level: beginner 438 439 .seealso: TaoSetObjectiveRoutine(), TaoSetHessianRoutine() TaoSetObjectiveAndGradientRoutine() 440 @*/ 441 PetscErrorCode TaoSetObjectiveAndGradientRoutine(Tao tao, PetscErrorCode (*func)(Tao, Vec, PetscReal *, Vec, void*), void *ctx) 442 { 443 PetscFunctionBegin; 444 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 445 tao->user_objgradP = ctx; 446 tao->ops->computeobjectiveandgradient = func; 447 PetscFunctionReturn(0); 448 } 449 450 /*@ 451 TaoIsObjectiveDefined -- Checks to see if the user has 452 declared an objective-only routine. Useful for determining when 453 it is appropriate to call TaoComputeObjective() or 454 TaoComputeObjectiveAndGradient() 455 456 Collective on Tao 457 458 Input Parameter: 459 + tao - the Tao context 460 - ctx - PETSC_TRUE if objective function routine is set by user, 461 PETSC_FALSE otherwise 462 Level: developer 463 464 .seealso: TaoSetObjectiveRoutine(), TaoIsGradientDefined(), TaoIsObjectiveAndGradientDefined() 465 @*/ 466 PetscErrorCode TaoIsObjectiveDefined(Tao tao, PetscBool *flg) 467 { 468 PetscFunctionBegin; 469 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 470 if (tao->ops->computeobjective == 0) *flg = PETSC_FALSE; 471 else *flg = PETSC_TRUE; 472 PetscFunctionReturn(0); 473 } 474 475 /*@ 476 TaoIsGradientDefined -- Checks to see if the user has 477 declared an objective-only routine. Useful for determining when 478 it is appropriate to call TaoComputeGradient() or 479 TaoComputeGradientAndGradient() 480 481 Not Collective 482 483 Input Parameter: 484 + tao - the Tao context 485 - ctx - PETSC_TRUE if gradient routine is set by user, PETSC_FALSE otherwise 486 Level: developer 487 488 .seealso: TaoSetGradientRoutine(), TaoIsObjectiveDefined(), TaoIsObjectiveAndGradientDefined() 489 @*/ 490 PetscErrorCode TaoIsGradientDefined(Tao tao, PetscBool *flg) 491 { 492 PetscFunctionBegin; 493 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 494 if (tao->ops->computegradient == 0) *flg = PETSC_FALSE; 495 else *flg = PETSC_TRUE; 496 PetscFunctionReturn(0); 497 } 498 499 /*@ 500 TaoIsObjectiveAndGradientDefined -- Checks to see if the user has 501 declared a joint objective/gradient routine. Useful for determining when 502 it is appropriate to call TaoComputeObjective() or 503 TaoComputeObjectiveAndGradient() 504 505 Not Collective 506 507 Input Parameter: 508 + tao - the Tao context 509 - ctx - PETSC_TRUE if objective/gradient routine is set by user, PETSC_FALSE otherwise 510 Level: developer 511 512 .seealso: TaoSetObjectiveAndGradientRoutine(), TaoIsObjectiveDefined(), TaoIsGradientDefined() 513 @*/ 514 PetscErrorCode TaoIsObjectiveAndGradientDefined(Tao tao, PetscBool *flg) 515 { 516 PetscFunctionBegin; 517 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 518 if (tao->ops->computeobjectiveandgradient == 0) *flg = PETSC_FALSE; 519 else *flg = PETSC_TRUE; 520 PetscFunctionReturn(0); 521 } 522