1 #define TAO_DLL 2 3 #include <petsc/private/taoimpl.h> /*I "petsctao.h" I*/ 4 5 PetscBool TaoRegisterAllCalled = PETSC_FALSE; 6 PetscFunctionList TaoList = NULL; 7 8 PetscClassId TAO_CLASSID; 9 PetscLogEvent Tao_Solve, Tao_ObjectiveEval, Tao_GradientEval, Tao_ObjGradientEval, Tao_HessianEval, Tao_ConstraintsEval, Tao_JacobianEval; 10 11 const char *TaoSubSetTypes[] = { "subvec","mask","matrixfree","TaoSubSetType","TAO_SUBSET_",0}; 12 13 #undef __FUNCT__ 14 #define __FUNCT__ "TaoCreate" 15 /*@ 16 TaoCreate - Creates a TAO solver 17 18 Collective on MPI_Comm 19 20 Input Parameter: 21 . comm - MPI communicator 22 23 Output Parameter: 24 . newtao - the new Tao context 25 26 Available methods include: 27 + nls - Newton's method with line search for unconstrained minimization 28 . ntr - Newton's method with trust region for unconstrained minimization 29 . ntl - Newton's method with trust region, line search for unconstrained minimization 30 . lmvm - Limited memory variable metric method for unconstrained minimization 31 . cg - Nonlinear conjugate gradient method for unconstrained minimization 32 . nm - Nelder-Mead algorithm for derivate-free unconstrained minimization 33 . tron - Newton Trust Region method for bound constrained minimization 34 . gpcg - Newton Trust Region method for quadratic bound constrained minimization 35 . blmvm - Limited memory variable metric method for bound constrained minimization 36 . lcl - Linearly constrained Lagrangian method for pde-constrained minimization 37 - pounders - Model-based algorithm for nonlinear least squares 38 39 Options Database Keys: 40 . -tao_type - select which method TAO should use 41 42 Level: beginner 43 44 .seealso: TaoSolve(), TaoDestroy() 45 @*/ 46 PetscErrorCode TaoCreate(MPI_Comm comm, Tao *newtao) 47 { 48 PetscErrorCode ierr; 49 Tao tao; 50 51 PetscFunctionBegin; 52 PetscValidPointer(newtao,2); 53 *newtao = NULL; 54 55 ierr = TaoInitializePackage();CHKERRQ(ierr); 56 ierr = TaoLineSearchInitializePackage();CHKERRQ(ierr); 57 58 ierr = PetscHeaderCreate(tao,TAO_CLASSID,"Tao","Optimization solver","Tao",comm,TaoDestroy,TaoView);CHKERRQ(ierr); 59 tao->ops->computeobjective=0; 60 tao->ops->computeobjectiveandgradient=0; 61 tao->ops->computegradient=0; 62 tao->ops->computehessian=0; 63 tao->ops->computeseparableobjective=0; 64 tao->ops->computeconstraints=0; 65 tao->ops->computejacobian=0; 66 tao->ops->computejacobianequality=0; 67 tao->ops->computejacobianinequality=0; 68 tao->ops->computeequalityconstraints=0; 69 tao->ops->computeinequalityconstraints=0; 70 tao->ops->convergencetest=TaoDefaultConvergenceTest; 71 tao->ops->convergencedestroy=0; 72 tao->ops->computedual=0; 73 tao->ops->setup=0; 74 tao->ops->solve=0; 75 tao->ops->view=0; 76 tao->ops->setfromoptions=0; 77 tao->ops->destroy=0; 78 79 tao->solution=NULL; 80 tao->gradient=NULL; 81 tao->sep_objective = NULL; 82 tao->constraints=NULL; 83 tao->constraints_equality=NULL; 84 tao->constraints_inequality=NULL; 85 tao->stepdirection=NULL; 86 tao->niter=0; 87 tao->ntotalits=0; 88 tao->XL = NULL; 89 tao->XU = NULL; 90 tao->IL = NULL; 91 tao->IU = NULL; 92 tao->DI = NULL; 93 tao->DE = NULL; 94 tao->gradient_norm = NULL; 95 tao->gradient_norm_tmp = NULL; 96 tao->hessian = NULL; 97 tao->hessian_pre = NULL; 98 tao->jacobian = NULL; 99 tao->jacobian_pre = NULL; 100 tao->jacobian_state = NULL; 101 tao->jacobian_state_pre = NULL; 102 tao->jacobian_state_inv = NULL; 103 tao->jacobian_design = NULL; 104 tao->jacobian_design_pre = NULL; 105 tao->jacobian_equality = NULL; 106 tao->jacobian_equality_pre = NULL; 107 tao->jacobian_inequality = NULL; 108 tao->jacobian_inequality_pre = NULL; 109 tao->state_is = NULL; 110 tao->design_is = NULL; 111 112 tao->max_it = 10000; 113 tao->max_funcs = 10000; 114 #if defined(PETSC_USE_REAL_SINGLE) 115 tao->fatol = 1e-5; 116 tao->frtol = 1e-5; 117 tao->gatol = 1e-5; 118 tao->grtol = 1e-5; 119 #else 120 tao->fatol = 1e-8; 121 tao->frtol = 1e-8; 122 tao->gatol = 1e-8; 123 tao->grtol = 1e-8; 124 #endif 125 tao->crtol = 0.0; 126 tao->catol = 0.0; 127 tao->steptol = 0.0; 128 tao->gttol = 0.0; 129 tao->trust0 = PETSC_INFINITY; 130 tao->fmin = PETSC_NINFINITY; 131 tao->hist_malloc = PETSC_FALSE; 132 tao->hist_reset = PETSC_TRUE; 133 tao->hist_max = 0; 134 tao->hist_len = 0; 135 tao->hist_obj = NULL; 136 tao->hist_resid = NULL; 137 tao->hist_cnorm = NULL; 138 tao->hist_lits = NULL; 139 140 tao->numbermonitors=0; 141 tao->viewsolution=PETSC_FALSE; 142 tao->viewhessian=PETSC_FALSE; 143 tao->viewgradient=PETSC_FALSE; 144 tao->viewjacobian=PETSC_FALSE; 145 tao->viewconstraints = PETSC_FALSE; 146 147 /* These flags prevents algorithms from overriding user options */ 148 tao->max_it_changed =PETSC_FALSE; 149 tao->max_funcs_changed=PETSC_FALSE; 150 tao->fatol_changed =PETSC_FALSE; 151 tao->frtol_changed =PETSC_FALSE; 152 tao->gatol_changed =PETSC_FALSE; 153 tao->grtol_changed =PETSC_FALSE; 154 tao->gttol_changed =PETSC_FALSE; 155 tao->steptol_changed =PETSC_FALSE; 156 tao->trust0_changed =PETSC_FALSE; 157 tao->fmin_changed =PETSC_FALSE; 158 tao->catol_changed =PETSC_FALSE; 159 tao->crtol_changed =PETSC_FALSE; 160 ierr = TaoResetStatistics(tao);CHKERRQ(ierr); 161 *newtao = tao; 162 PetscFunctionReturn(0); 163 } 164 165 #undef __FUNCT__ 166 #define __FUNCT__ "TaoSolve" 167 /*@ 168 TaoSolve - Solves an optimization problem min F(x) s.t. l <= x <= u 169 170 Collective on Tao 171 172 Input Parameters: 173 . tao - the Tao context 174 175 Notes: 176 The user must set up the Tao with calls to TaoSetInitialVector(), 177 TaoSetObjectiveRoutine(), 178 TaoSetGradientRoutine(), and (if using 2nd order method) TaoSetHessianRoutine(). 179 180 Level: beginner 181 182 .seealso: TaoCreate(), TaoSetObjectiveRoutine(), TaoSetGradientRoutine(), TaoSetHessianRoutine() 183 @*/ 184 PetscErrorCode TaoSolve(Tao tao) 185 { 186 PetscErrorCode ierr; 187 static PetscBool set = PETSC_FALSE; 188 189 PetscFunctionBegin; 190 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 191 ierr = PetscCitationsRegister("@TechReport{tao-user-ref,\n" 192 "title = {Toolkit for Advanced Optimization (TAO) Users Manual},\n" 193 "author = {Todd Munson and Jason Sarich and Stefan Wild and Steve Benson and Lois Curfman McInnes},\n" 194 "Institution = {Argonne National Laboratory},\n" 195 "Year = 2014,\n" 196 "Number = {ANL/MCS-TM-322 - Revision 3.5},\n" 197 "url = {http://www.mcs.anl.gov/tao}\n}\n",&set);CHKERRQ(ierr); 198 199 ierr = TaoSetUp(tao);CHKERRQ(ierr); 200 ierr = TaoResetStatistics(tao);CHKERRQ(ierr); 201 if (tao->linesearch) { 202 ierr = TaoLineSearchReset(tao->linesearch);CHKERRQ(ierr); 203 } 204 205 ierr = PetscLogEventBegin(Tao_Solve,tao,0,0,0);CHKERRQ(ierr); 206 if (tao->ops->solve){ ierr = (*tao->ops->solve)(tao);CHKERRQ(ierr); } 207 ierr = PetscLogEventEnd(Tao_Solve,tao,0,0,0);CHKERRQ(ierr); 208 209 tao->ntotalits += tao->niter; 210 ierr = TaoViewFromOptions(tao,NULL,"-tao_view");CHKERRQ(ierr); 211 212 if (tao->printreason) { 213 if (tao->reason > 0) { 214 ierr = PetscPrintf(((PetscObject)tao)->comm,"TAO solve converged due to %s iterations %D\n",TaoConvergedReasons[tao->reason],tao->niter);CHKERRQ(ierr); 215 } else { 216 ierr = PetscPrintf(((PetscObject)tao)->comm,"TAO solve did not converge due to %s iteration %D\n",TaoConvergedReasons[tao->reason],tao->niter);CHKERRQ(ierr); 217 } 218 } 219 PetscFunctionReturn(0); 220 } 221 222 #undef __FUNCT__ 223 #define __FUNCT__ "TaoSetUp" 224 /*@ 225 TaoSetUp - Sets up the internal data structures for the later use 226 of a Tao solver 227 228 Collective on tao 229 230 Input Parameters: 231 . tao - the TAO context 232 233 Notes: 234 The user will not need to explicitly call TaoSetUp(), as it will 235 automatically be called in TaoSolve(). However, if the user 236 desires to call it explicitly, it should come after TaoCreate() 237 and any TaoSetSomething() routines, but before TaoSolve(). 238 239 Level: advanced 240 241 .seealso: TaoCreate(), TaoSolve() 242 @*/ 243 PetscErrorCode TaoSetUp(Tao tao) 244 { 245 PetscErrorCode ierr; 246 247 PetscFunctionBegin; 248 PetscValidHeaderSpecific(tao, TAO_CLASSID,1); 249 if (tao->setupcalled) PetscFunctionReturn(0); 250 251 if (!tao->solution) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetInitialVector"); 252 if (tao->ops->setup) { 253 ierr = (*tao->ops->setup)(tao);CHKERRQ(ierr); 254 } 255 tao->setupcalled = PETSC_TRUE; 256 PetscFunctionReturn(0); 257 } 258 259 #undef __FUNCT__ 260 #define __FUNCT__ "TaoDestroy" 261 /*@ 262 TaoDestroy - Destroys the TAO context that was created with 263 TaoCreate() 264 265 Collective on Tao 266 267 Input Parameter: 268 . tao - the Tao context 269 270 Level: beginner 271 272 .seealso: TaoCreate(), TaoSolve() 273 @*/ 274 PetscErrorCode TaoDestroy(Tao *tao) 275 { 276 PetscErrorCode ierr; 277 278 PetscFunctionBegin; 279 if (!*tao) PetscFunctionReturn(0); 280 PetscValidHeaderSpecific(*tao,TAO_CLASSID,1); 281 if (--((PetscObject)*tao)->refct > 0) {*tao=0;PetscFunctionReturn(0);} 282 283 if ((*tao)->ops->destroy) { 284 ierr = (*((*tao))->ops->destroy)(*tao);CHKERRQ(ierr); 285 } 286 ierr = KSPDestroy(&(*tao)->ksp);CHKERRQ(ierr); 287 ierr = TaoLineSearchDestroy(&(*tao)->linesearch);CHKERRQ(ierr); 288 289 if ((*tao)->ops->convergencedestroy) { 290 ierr = (*(*tao)->ops->convergencedestroy)((*tao)->cnvP);CHKERRQ(ierr); 291 if ((*tao)->jacobian_state_inv) { 292 ierr = MatDestroy(&(*tao)->jacobian_state_inv);CHKERRQ(ierr); 293 } 294 } 295 ierr = VecDestroy(&(*tao)->solution);CHKERRQ(ierr); 296 ierr = VecDestroy(&(*tao)->gradient);CHKERRQ(ierr); 297 298 if ((*tao)->gradient_norm) { 299 ierr = PetscObjectDereference((PetscObject)(*tao)->gradient_norm);CHKERRQ(ierr); 300 ierr = VecDestroy(&(*tao)->gradient_norm_tmp);CHKERRQ(ierr); 301 } 302 303 ierr = VecDestroy(&(*tao)->XL);CHKERRQ(ierr); 304 ierr = VecDestroy(&(*tao)->XU);CHKERRQ(ierr); 305 ierr = VecDestroy(&(*tao)->IL);CHKERRQ(ierr); 306 ierr = VecDestroy(&(*tao)->IU);CHKERRQ(ierr); 307 ierr = VecDestroy(&(*tao)->DE);CHKERRQ(ierr); 308 ierr = VecDestroy(&(*tao)->DI);CHKERRQ(ierr); 309 ierr = VecDestroy(&(*tao)->constraints_equality);CHKERRQ(ierr); 310 ierr = VecDestroy(&(*tao)->constraints_inequality);CHKERRQ(ierr); 311 ierr = VecDestroy(&(*tao)->stepdirection);CHKERRQ(ierr); 312 ierr = MatDestroy(&(*tao)->hessian_pre);CHKERRQ(ierr); 313 ierr = MatDestroy(&(*tao)->hessian);CHKERRQ(ierr); 314 ierr = MatDestroy(&(*tao)->jacobian_pre);CHKERRQ(ierr); 315 ierr = MatDestroy(&(*tao)->jacobian);CHKERRQ(ierr); 316 ierr = MatDestroy(&(*tao)->jacobian_state_pre);CHKERRQ(ierr); 317 ierr = MatDestroy(&(*tao)->jacobian_state);CHKERRQ(ierr); 318 ierr = MatDestroy(&(*tao)->jacobian_state_inv);CHKERRQ(ierr); 319 ierr = MatDestroy(&(*tao)->jacobian_design);CHKERRQ(ierr); 320 ierr = MatDestroy(&(*tao)->jacobian_equality);CHKERRQ(ierr); 321 ierr = MatDestroy(&(*tao)->jacobian_equality_pre);CHKERRQ(ierr); 322 ierr = MatDestroy(&(*tao)->jacobian_inequality);CHKERRQ(ierr); 323 ierr = MatDestroy(&(*tao)->jacobian_inequality_pre);CHKERRQ(ierr); 324 ierr = ISDestroy(&(*tao)->state_is);CHKERRQ(ierr); 325 ierr = ISDestroy(&(*tao)->design_is);CHKERRQ(ierr); 326 ierr = TaoCancelMonitors(*tao);CHKERRQ(ierr); 327 if ((*tao)->hist_malloc) { 328 ierr = PetscFree((*tao)->hist_obj);CHKERRQ(ierr); 329 ierr = PetscFree((*tao)->hist_resid);CHKERRQ(ierr); 330 ierr = PetscFree((*tao)->hist_cnorm);CHKERRQ(ierr); 331 ierr = PetscFree((*tao)->hist_lits);CHKERRQ(ierr); 332 } 333 ierr = PetscHeaderDestroy(tao);CHKERRQ(ierr); 334 PetscFunctionReturn(0); 335 } 336 337 #undef __FUNCT__ 338 #define __FUNCT__ "TaoSetFromOptions" 339 /*@ 340 TaoSetFromOptions - Sets various Tao parameters from user 341 options. 342 343 Collective on Tao 344 345 Input Paremeter: 346 . tao - the Tao solver context 347 348 options Database Keys: 349 + -tao_type <type> - The algorithm that TAO uses (lmvm, nls, etc.) 350 . -tao_fatol <fatol> - absolute error tolerance in function value 351 . -tao_frtol <frtol> - relative error tolerance in function value 352 . -tao_gatol <gatol> - absolute error tolerance for ||gradient|| 353 . -tao_grtol <grtol> - relative error tolerance for ||gradient|| 354 . -tao_gttol <gttol> - reduction of ||gradient|| relative to initial gradient 355 . -tao_max_it <max> - sets maximum number of iterations 356 . -tao_max_funcs <max> - sets maximum number of function evaluations 357 . -tao_fmin <fmin> - stop if function value reaches fmin 358 . -tao_steptol <tol> - stop if trust region radius less than <tol> 359 . -tao_trust0 <t> - initial trust region radius 360 . -tao_monitor - prints function value and residual at each iteration 361 . -tao_smonitor - same as tao_monitor, but truncates very small values 362 . -tao_cmonitor - prints function value, residual, and constraint norm at each iteration 363 . -tao_view_solution - prints solution vector at each iteration 364 . -tao_view_separableobjective - prints separable objective vector at each iteration 365 . -tao_view_step - prints step direction vector at each iteration 366 . -tao_view_gradient - prints gradient vector at each iteration 367 . -tao_draw_solution - graphically view solution vector at each iteration 368 . -tao_draw_step - graphically view step vector at each iteration 369 . -tao_draw_gradient - graphically view gradient at each iteration 370 . -tao_fd_gradient - use gradient computed with finite differences 371 . -tao_cancelmonitors - cancels all monitors (except those set with command line) 372 . -tao_view - prints information about the Tao after solving 373 - -tao_converged_reason - prints the reason TAO stopped iterating 374 375 Notes: 376 To see all options, run your program with the -help option or consult the 377 user's manual. Should be called after TaoCreate() but before TaoSolve() 378 379 Level: beginner 380 @*/ 381 PetscErrorCode TaoSetFromOptions(Tao tao) 382 { 383 PetscErrorCode ierr; 384 const TaoType default_type = TAOLMVM; 385 const char *prefix; 386 char type[256], monfilename[PETSC_MAX_PATH_LEN]; 387 PetscViewer monviewer; 388 PetscBool flg; 389 MPI_Comm comm; 390 391 PetscFunctionBegin; 392 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 393 ierr = PetscObjectGetComm((PetscObject)tao,&comm);CHKERRQ(ierr); 394 ierr = TaoGetOptionsPrefix(tao,&prefix);CHKERRQ(ierr); 395 /* So no warnings are given about unused options */ 396 ierr = PetscOptionsHasName(prefix,"-tao_ls_type",&flg);CHKERRQ(ierr); 397 398 ierr = PetscObjectOptionsBegin((PetscObject)tao);CHKERRQ(ierr); 399 { 400 ierr = TaoRegisterAll();CHKERRQ(ierr); 401 if (((PetscObject)tao)->type_name) { 402 default_type = ((PetscObject)tao)->type_name; 403 } 404 /* Check for type from options */ 405 ierr = PetscOptionsFList("-tao_type","Tao Solver type","TaoSetType",TaoList,default_type,type,256,&flg);CHKERRQ(ierr); 406 if (flg) { 407 ierr = TaoSetType(tao,type);CHKERRQ(ierr); 408 } else if (!((PetscObject)tao)->type_name) { 409 ierr = TaoSetType(tao,default_type);CHKERRQ(ierr); 410 } 411 412 ierr = PetscOptionsReal("-tao_fatol","Stop if solution within","TaoSetTolerances",tao->fatol,&tao->fatol,&flg);CHKERRQ(ierr); 413 if (flg) tao->fatol_changed=PETSC_TRUE; 414 ierr = PetscOptionsReal("-tao_frtol","Stop if relative solution within","TaoSetTolerances",tao->frtol,&tao->frtol,&flg);CHKERRQ(ierr); 415 if (flg) tao->frtol_changed=PETSC_TRUE; 416 ierr = PetscOptionsReal("-tao_catol","Stop if constraints violations within","TaoSetConstraintTolerances",tao->catol,&tao->catol,&flg);CHKERRQ(ierr); 417 if (flg) tao->catol_changed=PETSC_TRUE; 418 ierr = PetscOptionsReal("-tao_crtol","Stop if relative contraint violations within","TaoSetConstraintTolerances",tao->crtol,&tao->crtol,&flg);CHKERRQ(ierr); 419 if (flg) tao->crtol_changed=PETSC_TRUE; 420 ierr = PetscOptionsReal("-tao_gatol","Stop if norm of gradient less than","TaoSetTolerances",tao->gatol,&tao->gatol,&flg);CHKERRQ(ierr); 421 if (flg) tao->gatol_changed=PETSC_TRUE; 422 ierr = PetscOptionsReal("-tao_grtol","Stop if norm of gradient divided by the function value is less than","TaoSetTolerances",tao->grtol,&tao->grtol,&flg);CHKERRQ(ierr); 423 if (flg) tao->grtol_changed=PETSC_TRUE; 424 ierr = PetscOptionsReal("-tao_gttol","Stop if the norm of the gradient is less than the norm of the initial gradient times tol","TaoSetTolerances",tao->gttol,&tao->gttol,&flg);CHKERRQ(ierr); 425 if (flg) tao->gttol_changed=PETSC_TRUE; 426 ierr = PetscOptionsInt("-tao_max_it","Stop if iteration number exceeds","TaoSetMaximumIterations",tao->max_it,&tao->max_it,&flg);CHKERRQ(ierr); 427 if (flg) tao->max_it_changed=PETSC_TRUE; 428 ierr = PetscOptionsInt("-tao_max_funcs","Stop if number of function evaluations exceeds","TaoSetMaximumFunctionEvaluations",tao->max_funcs,&tao->max_funcs,&flg);CHKERRQ(ierr); 429 if (flg) tao->max_funcs_changed=PETSC_TRUE; 430 ierr = PetscOptionsReal("-tao_fmin","Stop if function less than","TaoSetFunctionLowerBound",tao->fmin,&tao->fmin,&flg);CHKERRQ(ierr); 431 if (flg) tao->fmin_changed=PETSC_TRUE; 432 ierr = PetscOptionsReal("-tao_steptol","Stop if step size or trust region radius less than","",tao->steptol,&tao->steptol,&flg);CHKERRQ(ierr); 433 if (flg) tao->steptol_changed=PETSC_TRUE; 434 ierr = PetscOptionsReal("-tao_trust0","Initial trust region radius","TaoSetTrustRegionRadius",tao->trust0,&tao->trust0,&flg);CHKERRQ(ierr); 435 if (flg) tao->trust0_changed=PETSC_TRUE; 436 ierr = PetscOptionsString("-tao_view_solution","view solution vector after each evaluation","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 437 if (flg) { 438 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 439 ierr = TaoSetMonitor(tao,TaoSolutionMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 440 } 441 442 ierr = PetscOptionsBool("-tao_converged_reason","Print reason for TAO converged","TaoSolve",tao->printreason,&tao->printreason,NULL);CHKERRQ(ierr); 443 ierr = PetscOptionsString("-tao_view_gradient","view gradient vector after each evaluation","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 444 if (flg) { 445 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 446 ierr = TaoSetMonitor(tao,TaoGradientMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 447 } 448 449 ierr = PetscOptionsString("-tao_view_stepdirection","view step direction vector after each iteration","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 450 if (flg) { 451 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 452 ierr = TaoSetMonitor(tao,TaoStepDirectionMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 453 } 454 455 ierr = PetscOptionsString("-tao_view_separableobjective","view separable objective vector after each evaluation","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 456 if (flg) { 457 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 458 ierr = TaoSetMonitor(tao,TaoSeparableObjectiveMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 459 } 460 461 ierr = PetscOptionsString("-tao_monitor","Use the default convergence monitor","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 462 if (flg) { 463 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 464 ierr = TaoSetMonitor(tao,TaoDefaultMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 465 } 466 467 ierr = PetscOptionsString("-tao_smonitor","Use the short convergence monitor","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 468 if (flg) { 469 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 470 ierr = TaoSetMonitor(tao,TaoDefaultSMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 471 } 472 473 ierr = PetscOptionsString("-tao_cmonitor","Use the default convergence monitor with constraint norm","TaoSetMonitor","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr); 474 if (flg) { 475 ierr = PetscViewerASCIIOpen(comm,monfilename,&monviewer);CHKERRQ(ierr); 476 ierr = TaoSetMonitor(tao,TaoDefaultCMonitor,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr); 477 } 478 479 480 flg = PETSC_FALSE; 481 ierr = PetscOptionsBool("-tao_cancelmonitors","cancel all monitors and call any registered destroy routines","TaoCancelMonitors",flg,&flg,NULL);CHKERRQ(ierr); 482 if (flg) {ierr = TaoCancelMonitors(tao);CHKERRQ(ierr);} 483 484 flg = PETSC_FALSE; 485 ierr = PetscOptionsBool("-tao_draw_solution","Plot solution vector at each iteration","TaoSetMonitor",flg,&flg,NULL);CHKERRQ(ierr); 486 if (flg) { 487 ierr = TaoSetMonitor(tao,TaoDrawSolutionMonitor,NULL,NULL);CHKERRQ(ierr); 488 } 489 490 flg = PETSC_FALSE; 491 ierr = PetscOptionsBool("-tao_draw_step","plots step direction at each iteration","TaoSetMonitor",flg,&flg,NULL);CHKERRQ(ierr); 492 if (flg) { 493 ierr = TaoSetMonitor(tao,TaoDrawStepMonitor,NULL,NULL);CHKERRQ(ierr); 494 } 495 496 flg = PETSC_FALSE; 497 ierr = PetscOptionsBool("-tao_draw_gradient","plots gradient at each iteration","TaoSetMonitor",flg,&flg,NULL);CHKERRQ(ierr); 498 if (flg) { 499 ierr = TaoSetMonitor(tao,TaoDrawGradientMonitor,NULL,NULL);CHKERRQ(ierr); 500 } 501 flg = PETSC_FALSE; 502 ierr = PetscOptionsBool("-tao_fd_gradient","compute gradient using finite differences","TaoDefaultComputeGradient",flg,&flg,NULL);CHKERRQ(ierr); 503 if (flg) { 504 ierr = TaoSetGradientRoutine(tao,TaoDefaultComputeGradient,NULL);CHKERRQ(ierr); 505 } 506 ierr = PetscOptionsEnum("-tao_subset_type","subset type", "", TaoSubSetTypes,(PetscEnum)tao->subset_type, (PetscEnum*)&tao->subset_type, 0);CHKERRQ(ierr); 507 508 if (tao->ops->setfromoptions) { 509 ierr = (*tao->ops->setfromoptions)(PetscOptionsObject,tao);CHKERRQ(ierr); 510 } 511 } 512 ierr = PetscOptionsEnd();CHKERRQ(ierr); 513 PetscFunctionReturn(0); 514 } 515 516 #undef __FUNCT__ 517 #define __FUNCT__ "TaoView" 518 /*@C 519 TaoView - Prints information about the Tao 520 521 Collective on Tao 522 523 InputParameters: 524 + tao - the Tao context 525 - viewer - visualization context 526 527 Options Database Key: 528 . -tao_view - Calls TaoView() at the end of TaoSolve() 529 530 Notes: 531 The available visualization contexts include 532 + PETSC_VIEWER_STDOUT_SELF - standard output (default) 533 - PETSC_VIEWER_STDOUT_WORLD - synchronized standard 534 output where only the first processor opens 535 the file. All other processors send their 536 data to the first processor to print. 537 538 Level: beginner 539 540 .seealso: PetscViewerASCIIOpen() 541 @*/ 542 PetscErrorCode TaoView(Tao tao, PetscViewer viewer) 543 { 544 PetscErrorCode ierr; 545 PetscBool isascii,isstring; 546 const TaoType type; 547 548 PetscFunctionBegin; 549 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 550 if (!viewer) { 551 ierr = PetscViewerASCIIGetStdout(((PetscObject)tao)->comm,&viewer);CHKERRQ(ierr); 552 } 553 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 554 PetscCheckSameComm(tao,1,viewer,2); 555 556 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);CHKERRQ(ierr); 557 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSTRING,&isstring);CHKERRQ(ierr); 558 if (isascii) { 559 ierr = PetscObjectPrintClassNamePrefixType((PetscObject)tao,viewer);CHKERRQ(ierr); 560 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 561 562 if (tao->ops->view) { 563 ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr); 564 ierr = (*tao->ops->view)(tao,viewer);CHKERRQ(ierr); 565 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 566 } 567 if (tao->linesearch) { 568 ierr = PetscObjectPrintClassNamePrefixType((PetscObject)(tao->linesearch),viewer);CHKERRQ(ierr); 569 } 570 if (tao->ksp) { 571 ierr = PetscObjectPrintClassNamePrefixType((PetscObject)(tao->ksp),viewer);CHKERRQ(ierr); 572 ierr = PetscViewerASCIIPrintf(viewer,"total KSP iterations: %D\n",tao->ksp_tot_its);CHKERRQ(ierr); 573 } 574 if (tao->XL || tao->XU) { 575 ierr = PetscViewerASCIIPrintf(viewer,"Active Set subset type: %s\n",TaoSubSetTypes[tao->subset_type]);CHKERRQ(ierr); 576 } 577 578 ierr=PetscViewerASCIIPrintf(viewer,"convergence tolerances: fatol=%g,",(double)tao->fatol);CHKERRQ(ierr); 579 ierr=PetscViewerASCIIPrintf(viewer," frtol=%g\n",(double)tao->frtol);CHKERRQ(ierr); 580 581 ierr=PetscViewerASCIIPrintf(viewer,"convergence tolerances: gatol=%g,",(double)tao->gatol);CHKERRQ(ierr); 582 ierr=PetscViewerASCIIPrintf(viewer," steptol=%g,",(double)tao->steptol);CHKERRQ(ierr); 583 ierr=PetscViewerASCIIPrintf(viewer," gttol=%g\n",(double)tao->gttol);CHKERRQ(ierr); 584 585 ierr = PetscViewerASCIIPrintf(viewer,"Residual in Function/Gradient:=%g\n",(double)tao->residual);CHKERRQ(ierr); 586 587 if (tao->cnorm>0 || tao->catol>0 || tao->crtol>0){ 588 ierr=PetscViewerASCIIPrintf(viewer,"convergence tolerances:");CHKERRQ(ierr); 589 ierr=PetscViewerASCIIPrintf(viewer," catol=%g,",(double)tao->catol);CHKERRQ(ierr); 590 ierr=PetscViewerASCIIPrintf(viewer," crtol=%g\n",(double)tao->crtol);CHKERRQ(ierr); 591 ierr = PetscViewerASCIIPrintf(viewer,"Residual in Constraints:=%g\n",(double)tao->cnorm);CHKERRQ(ierr); 592 } 593 594 if (tao->trust < tao->steptol){ 595 ierr=PetscViewerASCIIPrintf(viewer,"convergence tolerances: steptol=%g\n",(double)tao->steptol);CHKERRQ(ierr); 596 ierr=PetscViewerASCIIPrintf(viewer,"Final trust region radius:=%g\n",(double)tao->trust);CHKERRQ(ierr); 597 } 598 599 if (tao->fmin>-1.e25){ 600 ierr=PetscViewerASCIIPrintf(viewer,"convergence tolerances: function minimum=%g\n",(double)tao->fmin);CHKERRQ(ierr); 601 } 602 ierr = PetscViewerASCIIPrintf(viewer,"Objective value=%g\n",(double)tao->fc);CHKERRQ(ierr); 603 604 ierr = PetscViewerASCIIPrintf(viewer,"total number of iterations=%D, ",tao->niter);CHKERRQ(ierr); 605 ierr = PetscViewerASCIIPrintf(viewer," (max: %D)\n",tao->max_it);CHKERRQ(ierr); 606 607 if (tao->nfuncs>0){ 608 ierr = PetscViewerASCIIPrintf(viewer,"total number of function evaluations=%D,",tao->nfuncs);CHKERRQ(ierr); 609 ierr = PetscViewerASCIIPrintf(viewer," max: %D\n",tao->max_funcs);CHKERRQ(ierr); 610 } 611 if (tao->ngrads>0){ 612 ierr = PetscViewerASCIIPrintf(viewer,"total number of gradient evaluations=%D,",tao->ngrads);CHKERRQ(ierr); 613 ierr = PetscViewerASCIIPrintf(viewer," max: %D\n",tao->max_funcs);CHKERRQ(ierr); 614 } 615 if (tao->nfuncgrads>0){ 616 ierr = PetscViewerASCIIPrintf(viewer,"total number of function/gradient evaluations=%D,",tao->nfuncgrads);CHKERRQ(ierr); 617 ierr = PetscViewerASCIIPrintf(viewer," (max: %D)\n",tao->max_funcs);CHKERRQ(ierr); 618 } 619 if (tao->nhess>0){ 620 ierr = PetscViewerASCIIPrintf(viewer,"total number of Hessian evaluations=%D\n",tao->nhess);CHKERRQ(ierr); 621 } 622 /* if (tao->linear_its>0){ 623 ierr = PetscViewerASCIIPrintf(viewer," total Krylov method iterations=%D\n",tao->linear_its);CHKERRQ(ierr); 624 }*/ 625 if (tao->nconstraints>0){ 626 ierr = PetscViewerASCIIPrintf(viewer,"total number of constraint function evaluations=%D\n",tao->nconstraints);CHKERRQ(ierr); 627 } 628 if (tao->njac>0){ 629 ierr = PetscViewerASCIIPrintf(viewer,"total number of Jacobian evaluations=%D\n",tao->njac);CHKERRQ(ierr); 630 } 631 632 if (tao->reason>0){ 633 ierr = PetscViewerASCIIPrintf(viewer, "Solution converged: ");CHKERRQ(ierr); 634 switch (tao->reason) { 635 case TAO_CONVERGED_FATOL: 636 ierr = PetscViewerASCIIPrintf(viewer,"estimated f(x)-f(X*) <= fatol\n");CHKERRQ(ierr); 637 break; 638 case TAO_CONVERGED_FRTOL: 639 ierr = PetscViewerASCIIPrintf(viewer,"estimated |f(x)-f(X*)|/|f(X*)| <= frtol\n");CHKERRQ(ierr); 640 break; 641 case TAO_CONVERGED_GATOL: 642 ierr = PetscViewerASCIIPrintf(viewer," ||g(X)|| <= gatol\n");CHKERRQ(ierr); 643 break; 644 case TAO_CONVERGED_GRTOL: 645 ierr = PetscViewerASCIIPrintf(viewer," ||g(X)||/|f(X)| <= grtol\n");CHKERRQ(ierr); 646 break; 647 case TAO_CONVERGED_GTTOL: 648 ierr = PetscViewerASCIIPrintf(viewer," ||g(X)||/||g(X0)|| <= gttol\n");CHKERRQ(ierr); 649 break; 650 case TAO_CONVERGED_STEPTOL: 651 ierr = PetscViewerASCIIPrintf(viewer," Steptol -- step size small\n");CHKERRQ(ierr); 652 break; 653 case TAO_CONVERGED_MINF: 654 ierr = PetscViewerASCIIPrintf(viewer," Minf -- f < fmin\n");CHKERRQ(ierr); 655 break; 656 case TAO_CONVERGED_USER: 657 ierr = PetscViewerASCIIPrintf(viewer," User Terminated\n");CHKERRQ(ierr); 658 break; 659 default: 660 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 661 break; 662 } 663 664 } else { 665 ierr = PetscViewerASCIIPrintf(viewer,"Solver terminated: %D",tao->reason);CHKERRQ(ierr); 666 switch (tao->reason) { 667 case TAO_DIVERGED_MAXITS: 668 ierr = PetscViewerASCIIPrintf(viewer," Maximum Iterations\n");CHKERRQ(ierr); 669 break; 670 case TAO_DIVERGED_NAN: 671 ierr = PetscViewerASCIIPrintf(viewer," NAN or Inf encountered\n");CHKERRQ(ierr); 672 break; 673 case TAO_DIVERGED_MAXFCN: 674 ierr = PetscViewerASCIIPrintf(viewer," Maximum Function Evaluations\n");CHKERRQ(ierr); 675 break; 676 case TAO_DIVERGED_LS_FAILURE: 677 ierr = PetscViewerASCIIPrintf(viewer," Line Search Failure\n");CHKERRQ(ierr); 678 break; 679 case TAO_DIVERGED_TR_REDUCTION: 680 ierr = PetscViewerASCIIPrintf(viewer," Trust Region too small\n");CHKERRQ(ierr); 681 break; 682 case TAO_DIVERGED_USER: 683 ierr = PetscViewerASCIIPrintf(viewer," User Terminated\n");CHKERRQ(ierr); 684 break; 685 default: 686 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 687 break; 688 } 689 } 690 ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr); 691 } else if (isstring) { 692 ierr = TaoGetType(tao,&type);CHKERRQ(ierr); 693 ierr = PetscViewerStringSPrintf(viewer," %-3.3s",type);CHKERRQ(ierr); 694 } 695 PetscFunctionReturn(0); 696 } 697 698 #undef __FUNCT__ 699 #define __FUNCT__ "TaoSetTolerances" 700 /*@ 701 TaoSetTolerances - Sets parameters used in TAO convergence tests 702 703 Logically collective on Tao 704 705 Input Parameters: 706 + tao - the Tao context 707 . fatol - absolute convergence tolerance 708 . frtol - relative convergence tolerance 709 . gatol - stop if norm of gradient is less than this 710 . grtol - stop if relative norm of gradient is less than this 711 - gttol - stop if norm of gradient is reduced by this factor 712 713 Options Database Keys: 714 + -tao_fatol <fatol> - Sets fatol 715 . -tao_frtol <frtol> - Sets frtol 716 . -tao_gatol <gatol> - Sets gatol 717 . -tao_grtol <grtol> - Sets grtol 718 - -tao_gttol <gttol> - Sets gttol 719 720 Stopping Criteria: 721 $ f(X) - f(X*) (estimated) <= fatol 722 $ |f(X) - f(X*)| (estimated) / |f(X)| <= frtol 723 $ ||g(X)|| <= gatol 724 $ ||g(X)|| / |f(X)| <= grtol 725 $ ||g(X)|| / ||g(X0)|| <= gttol 726 727 Notes: 728 Use PETSC_DEFAULT to leave one or more tolerances unchanged. 729 730 Level: beginner 731 732 .seealso: TaoGetTolerances() 733 734 @*/ 735 PetscErrorCode TaoSetTolerances(Tao tao, PetscReal fatol, PetscReal frtol, PetscReal gatol, PetscReal grtol, PetscReal gttol) 736 { 737 PetscErrorCode ierr; 738 739 PetscFunctionBegin; 740 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 741 742 if (fatol != PETSC_DEFAULT) { 743 if (fatol<0) { 744 ierr = PetscInfo(tao,"Tried to set negative fatol -- ignored.\n");CHKERRQ(ierr); 745 } else { 746 tao->fatol = PetscMax(0,fatol); 747 tao->fatol_changed=PETSC_TRUE; 748 } 749 } 750 if (frtol != PETSC_DEFAULT) { 751 if (frtol<0) { 752 ierr = PetscInfo(tao,"Tried to set negative frtol -- ignored.\n");CHKERRQ(ierr); 753 } else { 754 tao->frtol = PetscMax(0,frtol); 755 tao->frtol_changed=PETSC_TRUE; 756 } 757 } 758 759 if (gatol != PETSC_DEFAULT) { 760 if (gatol<0) { 761 ierr = PetscInfo(tao,"Tried to set negative gatol -- ignored.\n");CHKERRQ(ierr); 762 } else { 763 tao->gatol = PetscMax(0,gatol); 764 tao->gatol_changed=PETSC_TRUE; 765 } 766 } 767 768 if (grtol != PETSC_DEFAULT) { 769 if (grtol<0) { 770 ierr = PetscInfo(tao,"Tried to set negative grtol -- ignored.\n");CHKERRQ(ierr); 771 } else { 772 tao->grtol = PetscMax(0,grtol); 773 tao->grtol_changed=PETSC_TRUE; 774 } 775 } 776 777 if (gttol != PETSC_DEFAULT) { 778 if (gttol<0) { 779 ierr = PetscInfo(tao,"Tried to set negative gttol -- ignored.\n");CHKERRQ(ierr); 780 } else { 781 tao->gttol = PetscMax(0,gttol); 782 tao->gttol_changed=PETSC_TRUE; 783 } 784 } 785 PetscFunctionReturn(0); 786 } 787 788 #undef __FUNCT__ 789 #define __FUNCT__ "TaoSetConstraintTolerances" 790 /*@ 791 TaoSetConstraintTolerances - Sets constraint tolerance parameters used in TAO convergence tests 792 793 Logically collective on Tao 794 795 Input Parameters: 796 + tao - the Tao context 797 . catol - absolute constraint tolerance, constraint norm must be less than catol for used for fatol, gatol convergence criteria 798 - crtol - relative contraint tolerance, constraint norm must be less than crtol for used for fatol, gatol, gttol convergence criteria 799 800 Options Database Keys: 801 + -tao_catol <catol> - Sets catol 802 - -tao_crtol <crtol> - Sets crtol 803 804 Notes: 805 Use PETSC_DEFAULT to leave any tolerance unchanged. 806 807 Level: intermediate 808 809 .seealso: TaoGetTolerances(), TaoGetConstraintTolerances(), TaoSetTolerances() 810 811 @*/ 812 PetscErrorCode TaoSetConstraintTolerances(Tao tao, PetscReal catol, PetscReal crtol) 813 { 814 PetscErrorCode ierr; 815 816 PetscFunctionBegin; 817 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 818 819 if (catol != PETSC_DEFAULT) { 820 if (catol<0) { 821 ierr = PetscInfo(tao,"Tried to set negative catol -- ignored.\n");CHKERRQ(ierr); 822 } else { 823 tao->catol = PetscMax(0,catol); 824 tao->catol_changed=PETSC_TRUE; 825 } 826 } 827 828 if (crtol != PETSC_DEFAULT) { 829 if (crtol<0) { 830 ierr = PetscInfo(tao,"Tried to set negative crtol -- ignored.\n");CHKERRQ(ierr); 831 } else { 832 tao->crtol = PetscMax(0,crtol); 833 tao->crtol_changed=PETSC_TRUE; 834 } 835 } 836 PetscFunctionReturn(0); 837 } 838 839 #undef __FUNCT__ 840 #define __FUNCT__ "TaoGetConstraintTolerances" 841 /*@ 842 TaoGetConstraintTolerances - Gets constraint tolerance parameters used in TAO convergence tests 843 844 Not ollective 845 846 Input Parameter: 847 . tao - the Tao context 848 849 Output Parameter: 850 + catol - absolute constraint tolerance, constraint norm must be less than catol for used for fatol, gatol convergence criteria 851 - crtol - relative contraint tolerance, constraint norm must be less than crtol for used for fatol, gatol, gttol convergence criteria 852 853 Level: intermediate 854 855 .seealso: TaoGetTolerances(), TaoSetTolerances(), TaoSetConstraintTolerances() 856 857 @*/ 858 PetscErrorCode TaoGetConstraintTolerances(Tao tao, PetscReal *catol, PetscReal *crtol) 859 { 860 PetscFunctionBegin; 861 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 862 if (catol) *catol = tao->catol; 863 if (crtol) *crtol = tao->crtol; 864 PetscFunctionReturn(0); 865 } 866 867 #undef __FUNCT__ 868 #define __FUNCT__ "TaoSetFunctionLowerBound" 869 /*@ 870 TaoSetFunctionLowerBound - Sets a bound on the solution objective value. 871 When an approximate solution with an objective value below this number 872 has been found, the solver will terminate. 873 874 Logically Collective on Tao 875 876 Input Parameters: 877 + tao - the Tao solver context 878 - fmin - the tolerance 879 880 Options Database Keys: 881 . -tao_fmin <fmin> - sets the minimum function value 882 883 Level: intermediate 884 885 .seealso: TaoSetTolerances() 886 @*/ 887 PetscErrorCode TaoSetFunctionLowerBound(Tao tao,PetscReal fmin) 888 { 889 PetscFunctionBegin; 890 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 891 tao->fmin = fmin; 892 tao->fmin_changed=PETSC_TRUE; 893 PetscFunctionReturn(0); 894 } 895 896 #undef __FUNCT__ 897 #define __FUNCT__ "TaoGetFunctionLowerBound" 898 /*@ 899 TaoGetFunctionLowerBound - Sets a bound on the solution objective value. 900 When an approximate solution with an objective value below this number 901 has been found, the solver will terminate. 902 903 Not collective on Tao 904 905 Input Parameters: 906 . tao - the Tao solver context 907 908 OutputParameters: 909 . fmin - the minimum function value 910 911 Level: intermediate 912 913 .seealso: TaoSetFunctionLowerBound() 914 @*/ 915 PetscErrorCode TaoGetFunctionLowerBound(Tao tao,PetscReal *fmin) 916 { 917 PetscFunctionBegin; 918 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 919 *fmin = tao->fmin; 920 PetscFunctionReturn(0); 921 } 922 923 #undef __FUNCT__ 924 #define __FUNCT__ "TaoSetMaximumFunctionEvaluations" 925 /*@ 926 TaoSetMaximumFunctionEvaluations - Sets a maximum number of 927 function evaluations. 928 929 Logically Collective on Tao 930 931 Input Parameters: 932 + tao - the Tao solver context 933 - nfcn - the maximum number of function evaluations (>=0) 934 935 Options Database Keys: 936 . -tao_max_funcs <nfcn> - sets the maximum number of function evaluations 937 938 Level: intermediate 939 940 .seealso: TaoSetTolerances(), TaoSetMaximumIterations() 941 @*/ 942 943 PetscErrorCode TaoSetMaximumFunctionEvaluations(Tao tao,PetscInt nfcn) 944 { 945 PetscFunctionBegin; 946 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 947 tao->max_funcs = PetscMax(0,nfcn); 948 tao->max_funcs_changed=PETSC_TRUE; 949 PetscFunctionReturn(0); 950 } 951 952 #undef __FUNCT__ 953 #define __FUNCT__ "TaoGetMaximumFunctionEvaluations" 954 /*@ 955 TaoGetMaximumFunctionEvaluations - Sets a maximum number of 956 function evaluations. 957 958 Not Collective 959 960 Input Parameters: 961 . tao - the Tao solver context 962 963 Output Parameters: 964 . nfcn - the maximum number of function evaluations 965 966 Level: intermediate 967 968 .seealso: TaoSetMaximumFunctionEvaluations(), TaoGetMaximumIterations() 969 @*/ 970 971 PetscErrorCode TaoGetMaximumFunctionEvaluations(Tao tao,PetscInt *nfcn) 972 { 973 PetscFunctionBegin; 974 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 975 *nfcn = tao->max_funcs; 976 PetscFunctionReturn(0); 977 } 978 979 #undef __FUNCT__ 980 #define __FUNCT__ "TaoGetCurrentFunctionEvaluations" 981 /*@ 982 TaoGetCurrentFunctionEvaluations - Get current number of 983 function evaluations. 984 985 Not Collective 986 987 Input Parameters: 988 . tao - the Tao solver context 989 990 Output Parameters: 991 . nfuncs - the current number of function evaluations 992 993 Level: intermediate 994 995 .seealso: TaoSetMaximumFunctionEvaluations(), TaoGetMaximumFunctionEvaluations(), TaoGetMaximumIterations() 996 @*/ 997 998 PetscErrorCode TaoGetCurrentFunctionEvaluations(Tao tao,PetscInt *nfuncs) 999 { 1000 PetscFunctionBegin; 1001 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1002 *nfuncs=PetscMax(tao->nfuncs,tao->nfuncgrads); 1003 PetscFunctionReturn(0); 1004 } 1005 1006 #undef __FUNCT__ 1007 #define __FUNCT__ "TaoSetMaximumIterations" 1008 /*@ 1009 TaoSetMaximumIterations - Sets a maximum number of iterates. 1010 1011 Logically Collective on Tao 1012 1013 Input Parameters: 1014 + tao - the Tao solver context 1015 - maxits - the maximum number of iterates (>=0) 1016 1017 Options Database Keys: 1018 . -tao_max_it <its> - sets the maximum number of iterations 1019 1020 Level: intermediate 1021 1022 .seealso: TaoSetTolerances(), TaoSetMaximumFunctionEvaluations() 1023 @*/ 1024 PetscErrorCode TaoSetMaximumIterations(Tao tao,PetscInt maxits) 1025 { 1026 PetscFunctionBegin; 1027 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1028 tao->max_it = PetscMax(0,maxits); 1029 tao->max_it_changed=PETSC_TRUE; 1030 PetscFunctionReturn(0); 1031 } 1032 1033 #undef __FUNCT__ 1034 #define __FUNCT__ "TaoGetMaximumIterations" 1035 /*@ 1036 TaoGetMaximumIterations - Sets a maximum number of iterates. 1037 1038 Not Collective 1039 1040 Input Parameters: 1041 . tao - the Tao solver context 1042 1043 Output Parameters: 1044 . maxits - the maximum number of iterates 1045 1046 Level: intermediate 1047 1048 .seealso: TaoSetMaximumIterations(), TaoGetMaximumFunctionEvaluations() 1049 @*/ 1050 PetscErrorCode TaoGetMaximumIterations(Tao tao,PetscInt *maxits) 1051 { 1052 PetscFunctionBegin; 1053 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1054 *maxits = tao->max_it; 1055 PetscFunctionReturn(0); 1056 } 1057 1058 #undef __FUNCT__ 1059 #define __FUNCT__ "TaoSetInitialTrustRegionRadius" 1060 /*@ 1061 TaoSetInitialTrustRegionRadius - Sets the initial trust region radius. 1062 1063 Logically collective on Tao 1064 1065 Input Parameter: 1066 + tao - a TAO optimization solver 1067 - radius - the trust region radius 1068 1069 Level: intermediate 1070 1071 Options Database Key: 1072 . -tao_trust0 <t0> - sets initial trust region radius 1073 1074 .seealso: TaoGetTrustRegionRadius(), TaoSetTrustRegionTolerance() 1075 @*/ 1076 PetscErrorCode TaoSetInitialTrustRegionRadius(Tao tao, PetscReal radius) 1077 { 1078 PetscFunctionBegin; 1079 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1080 tao->trust0 = PetscMax(0.0,radius); 1081 tao->trust0_changed=PETSC_TRUE; 1082 PetscFunctionReturn(0); 1083 } 1084 1085 #undef __FUNCT__ 1086 #define __FUNCT__ "TaoGetInitialTrustRegionRadius" 1087 /*@ 1088 TaoGetInitialTrustRegionRadius - Sets the initial trust region radius. 1089 1090 Not Collective 1091 1092 Input Parameter: 1093 . tao - a TAO optimization solver 1094 1095 Output Parameter: 1096 . radius - the trust region radius 1097 1098 Level: intermediate 1099 1100 .seealso: TaoSetInitialTrustRegionRadius(), TaoGetCurrentTrustRegionRadius() 1101 @*/ 1102 PetscErrorCode TaoGetInitialTrustRegionRadius(Tao tao, PetscReal *radius) 1103 { 1104 PetscFunctionBegin; 1105 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1106 *radius = tao->trust0; 1107 PetscFunctionReturn(0); 1108 } 1109 1110 #undef __FUNCT__ 1111 #define __FUNCT__ "TaoGetCurrentTrustRegionRadius" 1112 /*@ 1113 TaoGetCurrentTrustRegionRadius - Gets the current trust region radius. 1114 1115 Not Collective 1116 1117 Input Parameter: 1118 . tao - a TAO optimization solver 1119 1120 Output Parameter: 1121 . radius - the trust region radius 1122 1123 Level: intermediate 1124 1125 .seealso: TaoSetInitialTrustRegionRadius(), TaoGetInitialTrustRegionRadius() 1126 @*/ 1127 PetscErrorCode TaoGetCurrentTrustRegionRadius(Tao tao, PetscReal *radius) 1128 { 1129 PetscFunctionBegin; 1130 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1131 *radius = tao->trust; 1132 PetscFunctionReturn(0); 1133 } 1134 1135 #undef __FUNCT__ 1136 #define __FUNCT__ "TaoGetTolerances" 1137 /*@ 1138 TaoGetTolerances - gets the current values of tolerances 1139 1140 Not Collective 1141 1142 Input Parameters: 1143 . tao - the Tao context 1144 1145 Output Parameters: 1146 + fatol - absolute convergence tolerance 1147 . frtol - relative convergence tolerance 1148 . gatol - stop if norm of gradient is less than this 1149 . grtol - stop if relative norm of gradient is less than this 1150 - gttol - stop if norm of gradient is reduced by a this factor 1151 1152 Note: NULL can be used as an argument if not all tolerances values are needed 1153 1154 .seealso TaoSetTolerances() 1155 1156 Level: intermediate 1157 @*/ 1158 PetscErrorCode TaoGetTolerances(Tao tao, PetscReal *fatol, PetscReal *frtol, PetscReal *gatol, PetscReal *grtol, PetscReal *gttol) 1159 { 1160 PetscFunctionBegin; 1161 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1162 if (fatol) *fatol=tao->fatol; 1163 if (frtol) *frtol=tao->frtol; 1164 if (gatol) *gatol=tao->gatol; 1165 if (grtol) *grtol=tao->grtol; 1166 if (gttol) *gttol=tao->gttol; 1167 PetscFunctionReturn(0); 1168 } 1169 1170 #undef __FUNCT__ 1171 #define __FUNCT__ "TaoGetKSP" 1172 /*@ 1173 TaoGetKSP - Gets the linear solver used by the optimization solver. 1174 Application writers should use TaoGetKSP if they need direct access 1175 to the PETSc KSP object. 1176 1177 Not Collective 1178 1179 Input Parameters: 1180 . tao - the TAO solver 1181 1182 Output Parameters: 1183 . ksp - the KSP linear solver used in the optimization solver 1184 1185 Level: intermediate 1186 1187 @*/ 1188 PetscErrorCode TaoGetKSP(Tao tao, KSP *ksp) 1189 { 1190 PetscFunctionBegin; 1191 *ksp = tao->ksp; 1192 PetscFunctionReturn(0); 1193 } 1194 1195 #undef __FUNCT__ 1196 #define __FUNCT__ "TaoGetLinearSolveIterations" 1197 /*@ 1198 TaoGetLinearSolveIterations - Gets the total number of linear iterations 1199 used by the TAO solver 1200 1201 Not Collective 1202 1203 Input Parameter: 1204 . tao - TAO context 1205 1206 Output Parameter: 1207 . lits - number of linear iterations 1208 1209 Notes: 1210 This counter is reset to zero for each successive call to TaoSolve() 1211 1212 Level: intermediate 1213 1214 .keywords: TAO 1215 1216 .seealso: TaoGetKSP() 1217 @*/ 1218 PetscErrorCode TaoGetLinearSolveIterations(Tao tao,PetscInt *lits) 1219 { 1220 PetscFunctionBegin; 1221 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1222 PetscValidIntPointer(lits,2); 1223 *lits = tao->ksp_tot_its; 1224 PetscFunctionReturn(0); 1225 } 1226 1227 #undef __FUNCT__ 1228 #define __FUNCT__ "TaoGetLineSearch" 1229 /*@ 1230 TaoGetLineSearch - Gets the line search used by the optimization solver. 1231 Application writers should use TaoGetLineSearch if they need direct access 1232 to the TaoLineSearch object. 1233 1234 Not Collective 1235 1236 Input Parameters: 1237 . tao - the TAO solver 1238 1239 Output Parameters: 1240 . ls - the line search used in the optimization solver 1241 1242 Level: intermediate 1243 1244 @*/ 1245 PetscErrorCode TaoGetLineSearch(Tao tao, TaoLineSearch *ls) 1246 { 1247 PetscFunctionBegin; 1248 *ls = tao->linesearch; 1249 PetscFunctionReturn(0); 1250 } 1251 1252 #undef __FUNCT__ 1253 #define __FUNCT__ "TaoAddLineSearchCounts" 1254 /*@ 1255 TaoAddLineSearchCounts - Adds the number of function evaluations spent 1256 in the line search to the running total. 1257 1258 Input Parameters: 1259 + tao - the TAO solver 1260 - ls - the line search used in the optimization solver 1261 1262 Level: developer 1263 1264 .seealso: TaoLineSearchApply() 1265 @*/ 1266 PetscErrorCode TaoAddLineSearchCounts(Tao tao) 1267 { 1268 PetscErrorCode ierr; 1269 PetscBool flg; 1270 PetscInt nfeval,ngeval,nfgeval; 1271 1272 PetscFunctionBegin; 1273 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1274 if (tao->linesearch) { 1275 ierr = TaoLineSearchIsUsingTaoRoutines(tao->linesearch,&flg);CHKERRQ(ierr); 1276 if (!flg) { 1277 ierr = TaoLineSearchGetNumberFunctionEvaluations(tao->linesearch,&nfeval,&ngeval,&nfgeval);CHKERRQ(ierr); 1278 tao->nfuncs+=nfeval; 1279 tao->ngrads+=ngeval; 1280 tao->nfuncgrads+=nfgeval; 1281 } 1282 } 1283 PetscFunctionReturn(0); 1284 } 1285 1286 #undef __FUNCT__ 1287 #define __FUNCT__ "TaoGetSolutionVector" 1288 /*@ 1289 TaoGetSolutionVector - Returns the vector with the current TAO solution 1290 1291 Not Collective 1292 1293 Input Parameter: 1294 . tao - the Tao context 1295 1296 Output Parameter: 1297 . X - the current solution 1298 1299 Level: intermediate 1300 1301 Note: The returned vector will be the same object that was passed into TaoSetInitialVector() 1302 @*/ 1303 PetscErrorCode TaoGetSolutionVector(Tao tao, Vec *X) 1304 { 1305 PetscFunctionBegin; 1306 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1307 *X = tao->solution; 1308 PetscFunctionReturn(0); 1309 } 1310 1311 #undef __FUNCT__ 1312 #define __FUNCT__ "TaoGetGradientVector" 1313 /*@ 1314 TaoGetGradientVector - Returns the vector with the current TAO gradient 1315 1316 Not Collective 1317 1318 Input Parameter: 1319 . tao - the Tao context 1320 1321 Output Parameter: 1322 . G - the current solution 1323 1324 Level: intermediate 1325 @*/ 1326 PetscErrorCode TaoGetGradientVector(Tao tao, Vec *G) 1327 { 1328 PetscFunctionBegin; 1329 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1330 *G = tao->gradient; 1331 PetscFunctionReturn(0); 1332 } 1333 1334 #undef __FUNCT__ 1335 #define __FUNCT__ "TaoResetStatistics" 1336 /*@ 1337 TaoResetStatistics - Initialize the statistics used by TAO for all of the solvers. 1338 These statistics include the iteration number, residual norms, and convergence status. 1339 This routine gets called before solving each optimization problem. 1340 1341 Collective on Tao 1342 1343 Input Parameters: 1344 . solver - the Tao context 1345 1346 Level: developer 1347 1348 .seealso: TaoCreate(), TaoSolve() 1349 @*/ 1350 PetscErrorCode TaoResetStatistics(Tao tao) 1351 { 1352 PetscFunctionBegin; 1353 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1354 tao->niter = 0; 1355 tao->nfuncs = 0; 1356 tao->nfuncgrads = 0; 1357 tao->ngrads = 0; 1358 tao->nhess = 0; 1359 tao->njac = 0; 1360 tao->nconstraints = 0; 1361 tao->ksp_its = 0; 1362 tao->ksp_tot_its = 0; 1363 tao->reason = TAO_CONTINUE_ITERATING; 1364 tao->residual = 0.0; 1365 tao->cnorm = 0.0; 1366 tao->step = 0.0; 1367 tao->lsflag = PETSC_FALSE; 1368 if (tao->hist_reset) tao->hist_len=0; 1369 PetscFunctionReturn(0); 1370 } 1371 1372 #undef __FUNCT__ 1373 #define __FUNCT__ "TaoSetConvergenceTest" 1374 /*@C 1375 TaoSetConvergenceTest - Sets the function that is to be used to test 1376 for convergence o fthe iterative minimization solution. The new convergence 1377 testing routine will replace TAO's default convergence test. 1378 1379 Logically Collective on Tao 1380 1381 Input Parameters: 1382 + tao - the Tao object 1383 . conv - the routine to test for convergence 1384 - ctx - [optional] context for private data for the convergence routine 1385 (may be NULL) 1386 1387 Calling sequence of conv: 1388 $ PetscErrorCode conv(Tao tao, void *ctx) 1389 1390 + tao - the Tao object 1391 - ctx - [optional] convergence context 1392 1393 Note: The new convergence testing routine should call TaoSetConvergedReason(). 1394 1395 Level: advanced 1396 1397 .seealso: TaoSetConvergedReason(), TaoGetSolutionStatus(), TaoGetTolerances(), TaoSetMonitor 1398 1399 @*/ 1400 PetscErrorCode TaoSetConvergenceTest(Tao tao, PetscErrorCode (*conv)(Tao,void*), void *ctx) 1401 { 1402 PetscFunctionBegin; 1403 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1404 (tao)->ops->convergencetest = conv; 1405 (tao)->cnvP = ctx; 1406 PetscFunctionReturn(0); 1407 } 1408 1409 #undef __FUNCT__ 1410 #define __FUNCT__ "TaoSetMonitor" 1411 /*@C 1412 TaoSetMonitor - Sets an ADDITIONAL function that is to be used at every 1413 iteration of the solver to display the iteration's 1414 progress. 1415 1416 Logically Collective on Tao 1417 1418 Input Parameters: 1419 + tao - the Tao solver context 1420 . mymonitor - monitoring routine 1421 - mctx - [optional] user-defined context for private data for the 1422 monitor routine (may be NULL) 1423 1424 Calling sequence of mymonitor: 1425 $ int mymonitor(Tao tao,void *mctx) 1426 1427 + tao - the Tao solver context 1428 - mctx - [optional] monitoring context 1429 1430 1431 Options Database Keys: 1432 + -tao_monitor - sets TaoDefaultMonitor() 1433 . -tao_smonitor - sets short monitor 1434 . -tao_cmonitor - same as smonitor plus constraint norm 1435 . -tao_view_solution - view solution at each iteration 1436 . -tao_view_gradient - view gradient at each iteration 1437 . -tao_view_separableobjective - view separable objective function at each iteration 1438 - -tao_cancelmonitors - cancels all monitors that have been hardwired into a code by calls to TaoSetMonitor(), but does not cancel those set via the options database. 1439 1440 1441 Notes: 1442 Several different monitoring routines may be set by calling 1443 TaoSetMonitor() multiple times; all will be called in the 1444 order in which they were set. 1445 1446 Fortran Notes: Only one monitor function may be set 1447 1448 Level: intermediate 1449 1450 .seealso: TaoDefaultMonitor(), TaoCancelMonitors(), TaoSetDestroyRoutine() 1451 @*/ 1452 PetscErrorCode TaoSetMonitor(Tao tao, PetscErrorCode (*func)(Tao, void*), void *ctx,PetscErrorCode (*dest)(void**)) 1453 { 1454 PetscErrorCode ierr; 1455 PetscInt i; 1456 1457 PetscFunctionBegin; 1458 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1459 if (tao->numbermonitors >= MAXTAOMONITORS) SETERRQ1(PETSC_COMM_SELF,1,"Cannot attach another monitor -- max=",MAXTAOMONITORS); 1460 1461 for (i=0; i<tao->numbermonitors;i++) { 1462 if (func == tao->monitor[i] && dest == tao->monitordestroy[i] && ctx == tao->monitorcontext[i]) { 1463 if (dest) { 1464 ierr = (*dest)(&ctx);CHKERRQ(ierr); 1465 } 1466 PetscFunctionReturn(0); 1467 } 1468 } 1469 tao->monitor[tao->numbermonitors] = func; 1470 tao->monitorcontext[tao->numbermonitors] = ctx; 1471 tao->monitordestroy[tao->numbermonitors] = dest; 1472 ++tao->numbermonitors; 1473 PetscFunctionReturn(0); 1474 } 1475 1476 #undef __FUNCT__ 1477 #define __FUNCT__ "TaoCancelMonitors" 1478 /*@ 1479 TaoCancelMonitors - Clears all the monitor functions for a Tao object. 1480 1481 Logically Collective on Tao 1482 1483 Input Parameters: 1484 . tao - the Tao solver context 1485 1486 Options Database: 1487 . -tao_cancelmonitors - cancels all monitors that have been hardwired 1488 into a code by calls to TaoSetMonitor(), but does not cancel those 1489 set via the options database 1490 1491 Notes: 1492 There is no way to clear one specific monitor from a Tao object. 1493 1494 Level: advanced 1495 1496 .seealso: TaoDefaultMonitor(), TaoSetMonitor() 1497 @*/ 1498 PetscErrorCode TaoCancelMonitors(Tao tao) 1499 { 1500 PetscInt i; 1501 PetscErrorCode ierr; 1502 1503 PetscFunctionBegin; 1504 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 1505 for (i=0;i<tao->numbermonitors;i++) { 1506 if (tao->monitordestroy[i]) { 1507 ierr = (*tao->monitordestroy[i])(&tao->monitorcontext[i]);CHKERRQ(ierr); 1508 } 1509 } 1510 tao->numbermonitors=0; 1511 PetscFunctionReturn(0); 1512 } 1513 1514 #undef __FUNCT__ 1515 #define __FUNCT__ "TaoDefaultMonitor" 1516 /*@ 1517 TaoDefaultMonitor - Default routine for monitoring progress of the 1518 Tao solvers (default). This monitor prints the function value and gradient 1519 norm at each iteration. It can be turned on from the command line using the 1520 -tao_monitor option 1521 1522 Collective on Tao 1523 1524 Input Parameters: 1525 + tao - the Tao context 1526 - ctx - PetscViewer context or NULL 1527 1528 Options Database Keys: 1529 . -tao_monitor 1530 1531 Level: advanced 1532 1533 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1534 @*/ 1535 PetscErrorCode TaoDefaultMonitor(Tao tao, void *ctx) 1536 { 1537 PetscErrorCode ierr; 1538 PetscInt its; 1539 PetscReal fct,gnorm; 1540 PetscViewer viewer; 1541 1542 PetscFunctionBegin; 1543 if (ctx) { 1544 viewer = (PetscViewer)ctx; 1545 } else { 1546 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1547 } 1548 its=tao->niter; 1549 fct=tao->fc; 1550 gnorm=tao->residual; 1551 ierr=PetscViewerASCIIPrintf(viewer,"iter = %3D,",its);CHKERRQ(ierr); 1552 ierr=PetscViewerASCIIPrintf(viewer," Function value: %g,",(double)fct);CHKERRQ(ierr); 1553 if (gnorm >= PETSC_INFINITY) { 1554 ierr=PetscViewerASCIIPrintf(viewer," Residual: Inf \n");CHKERRQ(ierr); 1555 } else { 1556 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g \n",(double)gnorm);CHKERRQ(ierr); 1557 } 1558 PetscFunctionReturn(0); 1559 } 1560 1561 #undef __FUNCT__ 1562 #define __FUNCT__ "TaoDefaultSMonitor" 1563 /*@ 1564 TaoDefaultSMonitor - Default routine for monitoring progress of the 1565 solver. Same as TaoDefaultMonitor() except 1566 it prints fewer digits of the residual as the residual gets smaller. 1567 This is because the later digits are meaningless and are often 1568 different on different machines; by using this routine different 1569 machines will usually generate the same output. It can be turned on 1570 by using the -tao_smonitor option 1571 1572 Collective on Tao 1573 1574 Input Parameters: 1575 + tao - the Tao context 1576 - ctx - PetscViewer context of type ASCII 1577 1578 Options Database Keys: 1579 . -tao_smonitor 1580 1581 Level: advanced 1582 1583 .seealso: TaoDefaultMonitor(), TaoSetMonitor() 1584 @*/ 1585 PetscErrorCode TaoDefaultSMonitor(Tao tao, void *ctx) 1586 { 1587 PetscErrorCode ierr; 1588 PetscInt its; 1589 PetscReal fct,gnorm; 1590 PetscViewer viewer = (PetscViewer)ctx; 1591 1592 PetscFunctionBegin; 1593 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 1594 its=tao->niter; 1595 fct=tao->fc; 1596 gnorm=tao->residual; 1597 ierr=PetscViewerASCIIPrintf(viewer,"iter = %3D,",its);CHKERRQ(ierr); 1598 ierr=PetscViewerASCIIPrintf(viewer," Function value %g,",(double)fct);CHKERRQ(ierr); 1599 if (gnorm >= PETSC_INFINITY/2) { 1600 ierr=PetscViewerASCIIPrintf(viewer," Residual: Inf \n");CHKERRQ(ierr); 1601 } else if (gnorm > 1.e-6) { 1602 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g \n",(double)gnorm);CHKERRQ(ierr); 1603 } else if (gnorm > 1.e-11) { 1604 ierr=PetscViewerASCIIPrintf(viewer," Residual: < 1.0e-6 \n");CHKERRQ(ierr); 1605 } else { 1606 ierr=PetscViewerASCIIPrintf(viewer," Residual: < 1.0e-11 \n");CHKERRQ(ierr); 1607 } 1608 PetscFunctionReturn(0); 1609 } 1610 1611 #undef __FUNCT__ 1612 #define __FUNCT__ "TaoDefaultCMonitor" 1613 /*@ 1614 TaoDefaultCMonitor - same as TaoDefaultMonitor() except 1615 it prints the norm of the constraints function. It can be turned on 1616 from the command line using the -tao_cmonitor option 1617 1618 Collective on Tao 1619 1620 Input Parameters: 1621 + tao - the Tao context 1622 - ctx - PetscViewer context or NULL 1623 1624 Options Database Keys: 1625 . -tao_cmonitor 1626 1627 Level: advanced 1628 1629 .seealso: TaoDefaultMonitor(), TaoSetMonitor() 1630 @*/ 1631 PetscErrorCode TaoDefaultCMonitor(Tao tao, void *ctx) 1632 { 1633 PetscErrorCode ierr; 1634 PetscInt its; 1635 PetscReal fct,gnorm; 1636 PetscViewer viewer; 1637 1638 PetscFunctionBegin; 1639 if (ctx) { 1640 viewer = (PetscViewer)ctx; 1641 } else { 1642 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1643 } 1644 its=tao->niter; 1645 fct=tao->fc; 1646 gnorm=tao->residual; 1647 ierr=PetscViewerASCIIPrintf(viewer,"iter = %D,",its);CHKERRQ(ierr); 1648 ierr=PetscViewerASCIIPrintf(viewer," Function value: %g,",(double)fct);CHKERRQ(ierr); 1649 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g ",(double)gnorm);CHKERRQ(ierr); 1650 ierr = PetscViewerASCIIPrintf(viewer," Constraint: %g \n",(double)tao->cnorm);CHKERRQ(ierr); 1651 PetscFunctionReturn(0); 1652 } 1653 1654 #undef __FUNCT__ 1655 #define __FUNCT__ "TaoSolutionMonitor" 1656 /*@C 1657 TaoSolutionMonitor - Views the solution at each iteration 1658 It can be turned on from the command line using the 1659 -tao_view_solution option 1660 1661 Collective on Tao 1662 1663 Input Parameters: 1664 + tao - the Tao context 1665 - ctx - PetscViewer context or NULL 1666 1667 Options Database Keys: 1668 . -tao_view_solution 1669 1670 Level: advanced 1671 1672 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1673 @*/ 1674 PetscErrorCode TaoSolutionMonitor(Tao tao, void *ctx) 1675 { 1676 PetscErrorCode ierr; 1677 PetscViewer viewer; 1678 1679 PetscFunctionBegin; 1680 if (ctx) { 1681 viewer = (PetscViewer)ctx; 1682 } else { 1683 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1684 } 1685 ierr = VecView(tao->solution, viewer);CHKERRQ(ierr); 1686 PetscFunctionReturn(0); 1687 } 1688 1689 #undef __FUNCT__ 1690 #define __FUNCT__ "TaoGradientMonitor" 1691 /*@C 1692 TaoGradientMonitor - Views the gradient at each iteration 1693 It can be turned on from the command line using the 1694 -tao_view_gradient option 1695 1696 Collective on Tao 1697 1698 Input Parameters: 1699 + tao - the Tao context 1700 - ctx - PetscViewer context or NULL 1701 1702 Options Database Keys: 1703 . -tao_view_gradient 1704 1705 Level: advanced 1706 1707 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1708 @*/ 1709 PetscErrorCode TaoGradientMonitor(Tao tao, void *ctx) 1710 { 1711 PetscErrorCode ierr; 1712 PetscViewer viewer; 1713 1714 PetscFunctionBegin; 1715 if (ctx) { 1716 viewer = (PetscViewer)ctx; 1717 } else { 1718 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1719 } 1720 ierr = VecView(tao->gradient, viewer);CHKERRQ(ierr); 1721 PetscFunctionReturn(0); 1722 } 1723 1724 #undef __FUNCT__ 1725 #define __FUNCT__ "TaoStepDirectionMonitor" 1726 /*@C 1727 TaoStepDirectionMonitor - Views the gradient at each iteration 1728 It can be turned on from the command line using the 1729 -tao_view_gradient option 1730 1731 Collective on Tao 1732 1733 Input Parameters: 1734 + tao - the Tao context 1735 - ctx - PetscViewer context or NULL 1736 1737 Options Database Keys: 1738 . -tao_view_gradient 1739 1740 Level: advanced 1741 1742 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1743 @*/ 1744 PetscErrorCode TaoStepDirectionMonitor(Tao tao, void *ctx) 1745 { 1746 PetscErrorCode ierr; 1747 PetscViewer viewer; 1748 PetscFunctionBegin; 1749 if (ctx) { 1750 viewer = (PetscViewer)ctx; 1751 } else { 1752 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1753 } 1754 ierr = VecView(tao->stepdirection, viewer);CHKERRQ(ierr); 1755 PetscFunctionReturn(0); 1756 } 1757 1758 #undef __FUNCT__ 1759 #define __FUNCT__ "TaoDrawSolutionMonitor" 1760 /*@C 1761 TaoDrawSolutionMonitor - Plots the solution at each iteration 1762 It can be turned on from the command line using the 1763 -tao_draw_solution option 1764 1765 Collective on Tao 1766 1767 Input Parameters: 1768 + tao - the Tao context 1769 - ctx - PetscViewer context 1770 1771 Options Database Keys: 1772 . -tao_draw_solution 1773 1774 Level: advanced 1775 1776 .seealso: TaoSolutionMonitor(), TaoSetMonitor(), TaoDrawGradientMonitor 1777 @*/ 1778 PetscErrorCode TaoDrawSolutionMonitor(Tao tao, void *ctx) 1779 { 1780 PetscErrorCode ierr; 1781 PetscViewer viewer = (PetscViewer) ctx; 1782 1783 PetscFunctionBegin; 1784 ierr = VecView(tao->solution, viewer);CHKERRQ(ierr); 1785 PetscFunctionReturn(0); 1786 } 1787 1788 #undef __FUNCT__ 1789 #define __FUNCT__ "TaoDrawGradientMonitor" 1790 /*@C 1791 TaoDrawGradientMonitor - Plots the gradient at each iteration 1792 It can be turned on from the command line using the 1793 -tao_draw_gradient option 1794 1795 Collective on Tao 1796 1797 Input Parameters: 1798 + tao - the Tao context 1799 - ctx - PetscViewer context 1800 1801 Options Database Keys: 1802 . -tao_draw_gradient 1803 1804 Level: advanced 1805 1806 .seealso: TaoGradientMonitor(), TaoSetMonitor(), TaoDrawSolutionMonitor 1807 @*/ 1808 PetscErrorCode TaoDrawGradientMonitor(Tao tao, void *ctx) 1809 { 1810 PetscErrorCode ierr; 1811 PetscViewer viewer = (PetscViewer)ctx; 1812 1813 PetscFunctionBegin; 1814 ierr = VecView(tao->gradient, viewer);CHKERRQ(ierr); 1815 PetscFunctionReturn(0); 1816 } 1817 1818 #undef __FUNCT__ 1819 #define __FUNCT__ "TaoDrawStepMonitor" 1820 /*@C 1821 TaoDrawStepMonitor - Plots the step direction at each iteration 1822 It can be turned on from the command line using the 1823 -tao_draw_step option 1824 1825 Collective on Tao 1826 1827 Input Parameters: 1828 + tao - the Tao context 1829 - ctx - PetscViewer context 1830 1831 Options Database Keys: 1832 . -tao_draw_step 1833 1834 Level: advanced 1835 1836 .seealso: TaoSetMonitor(), TaoDrawSolutionMonitor 1837 @*/ 1838 PetscErrorCode TaoDrawStepMonitor(Tao tao, void *ctx) 1839 { 1840 PetscErrorCode ierr; 1841 PetscViewer viewer = (PetscViewer)(ctx); 1842 1843 PetscFunctionBegin; 1844 ierr = VecView(tao->stepdirection, viewer);CHKERRQ(ierr); 1845 PetscFunctionReturn(0); 1846 } 1847 1848 #undef __FUNCT__ 1849 #define __FUNCT__ "TaoSeparableObjectiveMonitor" 1850 /*@C 1851 TaoSeparableObjectiveMonitor - Views the separable objective function at each iteration 1852 It can be turned on from the command line using the 1853 -tao_view_separableobjective option 1854 1855 Collective on Tao 1856 1857 Input Parameters: 1858 + tao - the Tao context 1859 - ctx - PetscViewer context or NULL 1860 1861 Options Database Keys: 1862 . -tao_view_separableobjective 1863 1864 Level: advanced 1865 1866 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1867 @*/ 1868 PetscErrorCode TaoSeparableObjectiveMonitor(Tao tao, void *ctx) 1869 { 1870 PetscErrorCode ierr; 1871 PetscViewer viewer; 1872 1873 PetscFunctionBegin; 1874 if (ctx) { 1875 viewer = (PetscViewer)ctx; 1876 } else { 1877 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1878 } 1879 ierr = VecView(tao->sep_objective,viewer);CHKERRQ(ierr); 1880 PetscFunctionReturn(0); 1881 } 1882 1883 #undef __FUNCT__ 1884 #define __FUNCT__ "TaoDefaultConvergenceTest" 1885 /*@ 1886 TaoDefaultConvergenceTest - Determines whether the solver should continue iterating 1887 or terminate. 1888 1889 Collective on Tao 1890 1891 Input Parameters: 1892 + tao - the Tao context 1893 - dummy - unused dummy context 1894 1895 Output Parameter: 1896 . reason - for terminating 1897 1898 Notes: 1899 This routine checks the residual in the optimality conditions, the 1900 relative residual in the optimity conditions, the number of function 1901 evaluations, and the function value to test convergence. Some 1902 solvers may use different convergence routines. 1903 1904 Level: developer 1905 1906 .seealso: TaoSetTolerances(),TaoGetConvergedReason(),TaoSetConvergedReason() 1907 @*/ 1908 1909 PetscErrorCode TaoDefaultConvergenceTest(Tao tao,void *dummy) 1910 { 1911 PetscInt niter=tao->niter, nfuncs=PetscMax(tao->nfuncs,tao->nfuncgrads); 1912 PetscInt max_funcs=tao->max_funcs; 1913 PetscReal gnorm=tao->residual, gnorm0=tao->gnorm0; 1914 PetscReal f=tao->fc, steptol=tao->steptol,trradius=tao->step; 1915 PetscReal gatol=tao->gatol,grtol=tao->grtol,gttol=tao->gttol; 1916 PetscReal fatol=tao->fatol,frtol=tao->frtol,catol=tao->catol,crtol=tao->crtol; 1917 PetscReal fmin=tao->fmin, cnorm=tao->cnorm, cnorm0=tao->cnorm0; 1918 PetscReal gnorm2; 1919 TaoConvergedReason reason=tao->reason; 1920 PetscErrorCode ierr; 1921 1922 PetscFunctionBegin; 1923 PetscValidHeaderSpecific(tao, TAO_CLASSID,1); 1924 if (reason != TAO_CONTINUE_ITERATING) { 1925 PetscFunctionReturn(0); 1926 } 1927 gnorm2=gnorm*gnorm; 1928 1929 if (PetscIsInfOrNanReal(f)) { 1930 ierr = PetscInfo(tao,"Failed to converged, function value is Inf or NaN\n");CHKERRQ(ierr); 1931 reason = TAO_DIVERGED_NAN; 1932 } else if (f <= fmin && cnorm <=catol) { 1933 ierr = PetscInfo2(tao,"Converged due to function value %g < minimum function value %g\n", (double)f,(double)fmin);CHKERRQ(ierr); 1934 reason = TAO_CONVERGED_MINF; 1935 } else if (gnorm2 <= fatol && cnorm <=catol) { 1936 ierr = PetscInfo2(tao,"Converged due to estimated f(X) - f(X*) = %g < %g\n",(double)gnorm2,(double)fatol);CHKERRQ(ierr); 1937 reason = TAO_CONVERGED_FATOL; 1938 } else if (f != 0 && gnorm2 / PetscAbsReal(f)<= frtol && cnorm/PetscMax(cnorm0,1.0) <= crtol) { 1939 ierr = PetscInfo2(tao,"Converged due to estimated |f(X)-f(X*)|/f(X) = %g < %g\n",(double)(gnorm2/PetscAbsReal(f)),(double)frtol);CHKERRQ(ierr); 1940 reason = TAO_CONVERGED_FRTOL; 1941 } else if (gnorm<= gatol && cnorm <=catol) { 1942 ierr = PetscInfo2(tao,"Converged due to residual norm ||g(X)||=%g < %g\n",(double)gnorm,(double)gatol);CHKERRQ(ierr); 1943 reason = TAO_CONVERGED_GATOL; 1944 } else if ( f!=0 && PetscAbsReal(gnorm/f) <= grtol && cnorm <= crtol) { 1945 ierr = PetscInfo2(tao,"Converged due to residual ||g(X)||/|f(X)| =%g < %g\n",(double)(gnorm/f),(double)grtol);CHKERRQ(ierr); 1946 reason = TAO_CONVERGED_GRTOL; 1947 } else if (gnorm0 != 0 && gnorm/gnorm0 <= gttol && cnorm <= crtol) { 1948 ierr = PetscInfo2(tao,"Converged due to relative residual norm ||g(X)||/||g(X0)|| = %g < %g\n",(double)(gnorm/gnorm0),(double)gttol);CHKERRQ(ierr); 1949 reason = TAO_CONVERGED_GTTOL; 1950 } else if (nfuncs > max_funcs){ 1951 ierr = PetscInfo2(tao,"Exceeded maximum number of function evaluations: %D > %D\n", nfuncs,max_funcs);CHKERRQ(ierr); 1952 reason = TAO_DIVERGED_MAXFCN; 1953 } else if ( tao->lsflag != 0 ){ 1954 ierr = PetscInfo(tao,"Tao Line Search failure.\n");CHKERRQ(ierr); 1955 reason = TAO_DIVERGED_LS_FAILURE; 1956 } else if (trradius < steptol && niter > 0){ 1957 ierr = PetscInfo2(tao,"Trust region/step size too small: %g < %g\n", (double)trradius,(double)steptol);CHKERRQ(ierr); 1958 reason = TAO_CONVERGED_STEPTOL; 1959 } else if (niter > tao->max_it) { 1960 ierr = PetscInfo2(tao,"Exceeded maximum number of iterations: %D > %D\n",niter,tao->max_it);CHKERRQ(ierr); 1961 reason = TAO_DIVERGED_MAXITS; 1962 } else { 1963 reason = TAO_CONTINUE_ITERATING; 1964 } 1965 tao->reason = reason; 1966 PetscFunctionReturn(0); 1967 } 1968 1969 #undef __FUNCT__ 1970 #define __FUNCT__ "TaoSetOptionsPrefix" 1971 /*@C 1972 TaoSetOptionsPrefix - Sets the prefix used for searching for all 1973 TAO options in the database. 1974 1975 1976 Logically Collective on Tao 1977 1978 Input Parameters: 1979 + tao - the Tao context 1980 - prefix - the prefix string to prepend to all TAO option requests 1981 1982 Notes: 1983 A hyphen (-) must NOT be given at the beginning of the prefix name. 1984 The first character of all runtime options is AUTOMATICALLY the hyphen. 1985 1986 For example, to distinguish between the runtime options for two 1987 different TAO solvers, one could call 1988 .vb 1989 TaoSetOptionsPrefix(tao1,"sys1_") 1990 TaoSetOptionsPrefix(tao2,"sys2_") 1991 .ve 1992 1993 This would enable use of different options for each system, such as 1994 .vb 1995 -sys1_tao_method blmvm -sys1_tao_gtol 1.e-3 1996 -sys2_tao_method lmvm -sys2_tao_gtol 1.e-4 1997 .ve 1998 1999 2000 Level: advanced 2001 2002 .seealso: TaoAppendOptionsPrefix(), TaoGetOptionsPrefix() 2003 @*/ 2004 2005 PetscErrorCode TaoSetOptionsPrefix(Tao tao, const char p[]) 2006 { 2007 PetscErrorCode ierr; 2008 2009 PetscFunctionBegin; 2010 ierr = PetscObjectSetOptionsPrefix((PetscObject)tao,p);CHKERRQ(ierr); 2011 if (tao->linesearch) { 2012 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,p);CHKERRQ(ierr); 2013 } 2014 if (tao->ksp) { 2015 ierr = KSPSetOptionsPrefix(tao->ksp,p);CHKERRQ(ierr); 2016 } 2017 PetscFunctionReturn(0); 2018 } 2019 2020 #undef __FUNCT__ 2021 #define __FUNCT__ "TaoAppendOptionsPrefix" 2022 /*@C 2023 TaoAppendOptionsPrefix - Appends to the prefix used for searching for all 2024 TAO options in the database. 2025 2026 2027 Logically Collective on Tao 2028 2029 Input Parameters: 2030 + tao - the Tao solver context 2031 - prefix - the prefix string to prepend to all TAO option requests 2032 2033 Notes: 2034 A hyphen (-) must NOT be given at the beginning of the prefix name. 2035 The first character of all runtime options is AUTOMATICALLY the hyphen. 2036 2037 2038 Level: advanced 2039 2040 .seealso: TaoSetOptionsPrefix(), TaoGetOptionsPrefix() 2041 @*/ 2042 PetscErrorCode TaoAppendOptionsPrefix(Tao tao, const char p[]) 2043 { 2044 PetscErrorCode ierr; 2045 2046 PetscFunctionBegin; 2047 ierr = PetscObjectAppendOptionsPrefix((PetscObject)tao,p);CHKERRQ(ierr); 2048 if (tao->linesearch) { 2049 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,p);CHKERRQ(ierr); 2050 } 2051 if (tao->ksp) { 2052 ierr = KSPSetOptionsPrefix(tao->ksp,p);CHKERRQ(ierr); 2053 } 2054 PetscFunctionReturn(0); 2055 } 2056 2057 #undef __FUNCT__ 2058 #define __FUNCT__ "TaoGetOptionsPrefix" 2059 /*@C 2060 TaoGetOptionsPrefix - Gets the prefix used for searching for all 2061 TAO options in the database 2062 2063 Not Collective 2064 2065 Input Parameters: 2066 . tao - the Tao context 2067 2068 Output Parameters: 2069 . prefix - pointer to the prefix string used is returned 2070 2071 Notes: On the fortran side, the user should pass in a string 'prefix' of 2072 sufficient length to hold the prefix. 2073 2074 Level: advanced 2075 2076 .seealso: TaoSetOptionsPrefix(), TaoAppendOptionsPrefix() 2077 @*/ 2078 PetscErrorCode TaoGetOptionsPrefix(Tao tao, const char *p[]) 2079 { 2080 return PetscObjectGetOptionsPrefix((PetscObject)tao,p); 2081 } 2082 2083 #undef __FUNCT__ 2084 #define __FUNCT__ "TaoSetType" 2085 /*@C 2086 TaoSetType - Sets the method for the unconstrained minimization solver. 2087 2088 Collective on Tao 2089 2090 Input Parameters: 2091 + solver - the Tao solver context 2092 - type - a known method 2093 2094 Options Database Key: 2095 . -tao_type <type> - Sets the method; use -help for a list 2096 of available methods (for instance, "-tao_type lmvm" or "-tao_type tron") 2097 2098 Available methods include: 2099 + nls - Newton's method with line search for unconstrained minimization 2100 . ntr - Newton's method with trust region for unconstrained minimization 2101 . ntl - Newton's method with trust region, line search for unconstrained minimization 2102 . lmvm - Limited memory variable metric method for unconstrained minimization 2103 . cg - Nonlinear conjugate gradient method for unconstrained minimization 2104 . nm - Nelder-Mead algorithm for derivate-free unconstrained minimization 2105 . tron - Newton Trust Region method for bound constrained minimization 2106 . gpcg - Newton Trust Region method for quadratic bound constrained minimization 2107 . blmvm - Limited memory variable metric method for bound constrained minimization 2108 - pounders - Model-based algorithm pounder extended for nonlinear least squares 2109 2110 Level: intermediate 2111 2112 .seealso: TaoCreate(), TaoGetType(), TaoType 2113 2114 @*/ 2115 PetscErrorCode TaoSetType(Tao tao, const TaoType type) 2116 { 2117 PetscErrorCode ierr; 2118 PetscErrorCode (*create_xxx)(Tao); 2119 PetscBool issame; 2120 2121 PetscFunctionBegin; 2122 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2123 2124 ierr = PetscObjectTypeCompare((PetscObject)tao,type,&issame);CHKERRQ(ierr); 2125 if (issame) PetscFunctionReturn(0); 2126 2127 ierr = PetscFunctionListFind(TaoList, type, (void(**)(void))&create_xxx);CHKERRQ(ierr); 2128 if (!create_xxx) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unable to find requested Tao type %s",type); 2129 2130 /* Destroy the existing solver information */ 2131 if (tao->ops->destroy) { 2132 ierr = (*tao->ops->destroy)(tao);CHKERRQ(ierr); 2133 } 2134 ierr = KSPDestroy(&tao->ksp);CHKERRQ(ierr); 2135 ierr = TaoLineSearchDestroy(&tao->linesearch);CHKERRQ(ierr); 2136 ierr = VecDestroy(&tao->gradient);CHKERRQ(ierr); 2137 ierr = VecDestroy(&tao->stepdirection);CHKERRQ(ierr); 2138 2139 tao->ops->setup = 0; 2140 tao->ops->solve = 0; 2141 tao->ops->view = 0; 2142 tao->ops->setfromoptions = 0; 2143 tao->ops->destroy = 0; 2144 2145 tao->setupcalled = PETSC_FALSE; 2146 2147 ierr = (*create_xxx)(tao);CHKERRQ(ierr); 2148 ierr = PetscObjectChangeTypeName((PetscObject)tao,type);CHKERRQ(ierr); 2149 PetscFunctionReturn(0); 2150 } 2151 2152 #undef __FUNCT__ 2153 #define __FUNCT__ "TaoRegister" 2154 /*MC 2155 TaoRegister - Adds a method to the TAO package for unconstrained minimization. 2156 2157 Synopsis: 2158 TaoRegister(char *name_solver,char *path,char *name_Create,int (*routine_Create)(Tao)) 2159 2160 Not collective 2161 2162 Input Parameters: 2163 + sname - name of a new user-defined solver 2164 - func - routine to Create method context 2165 2166 Notes: 2167 TaoRegister() may be called multiple times to add several user-defined solvers. 2168 2169 Sample usage: 2170 .vb 2171 TaoRegister("my_solver",MySolverCreate); 2172 .ve 2173 2174 Then, your solver can be chosen with the procedural interface via 2175 $ TaoSetType(tao,"my_solver") 2176 or at runtime via the option 2177 $ -tao_type my_solver 2178 2179 Level: advanced 2180 2181 .seealso: TaoRegisterAll(), TaoRegisterDestroy() 2182 M*/ 2183 PetscErrorCode TaoRegister(const char sname[], PetscErrorCode (*func)(Tao)) 2184 { 2185 PetscErrorCode ierr; 2186 2187 PetscFunctionBegin; 2188 ierr = PetscFunctionListAdd(&TaoList,sname, (void (*)(void))func);CHKERRQ(ierr); 2189 PetscFunctionReturn(0); 2190 } 2191 2192 #undef __FUNCT__ 2193 #define __FUNCT__ "TaoRegisterDestroy" 2194 /*@C 2195 TaoRegisterDestroy - Frees the list of minimization solvers that were 2196 registered by TaoRegisterDynamic(). 2197 2198 Not Collective 2199 2200 Level: advanced 2201 2202 .seealso: TaoRegisterAll(), TaoRegister() 2203 @*/ 2204 PetscErrorCode TaoRegisterDestroy(void) 2205 { 2206 PetscErrorCode ierr; 2207 PetscFunctionBegin; 2208 ierr = PetscFunctionListDestroy(&TaoList);CHKERRQ(ierr); 2209 TaoRegisterAllCalled = PETSC_FALSE; 2210 PetscFunctionReturn(0); 2211 } 2212 2213 #undef __FUNCT__ 2214 #define __FUNCT__ "TaoGetIterationNumber" 2215 /*@ 2216 TaoGetIterationNumber - Gets the number of Tao iterations completed 2217 at this time. 2218 2219 Not Collective 2220 2221 Input Parameter: 2222 . tao - Tao context 2223 2224 Output Parameter: 2225 . iter - iteration number 2226 2227 Notes: 2228 For example, during the computation of iteration 2 this would return 1. 2229 2230 2231 Level: intermediate 2232 2233 .keywords: Tao, nonlinear, get, iteration, number, 2234 2235 .seealso: TaoGetLinearSolveIterations() 2236 @*/ 2237 PetscErrorCode TaoGetIterationNumber(Tao tao,PetscInt *iter) 2238 { 2239 PetscFunctionBegin; 2240 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2241 PetscValidIntPointer(iter,2); 2242 *iter = tao->niter; 2243 PetscFunctionReturn(0); 2244 } 2245 2246 #undef __FUNCT__ 2247 #define __FUNCT__ "TaoSetIterationNumber" 2248 /*@ 2249 TaoSetIterationNumber - Sets the current iteration number. 2250 2251 Not Collective 2252 2253 Input Parameter: 2254 . tao - Tao context 2255 . iter - iteration number 2256 2257 Level: developer 2258 2259 .keywords: Tao, nonlinear, set, iteration, number, 2260 2261 .seealso: TaoGetLinearSolveIterations() 2262 @*/ 2263 PetscErrorCode TaoSetIterationNumber(Tao tao,PetscInt iter) 2264 { 2265 PetscErrorCode ierr; 2266 2267 PetscFunctionBegin; 2268 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2269 ierr = PetscObjectSAWsTakeAccess((PetscObject)tao);CHKERRQ(ierr); 2270 tao->niter = iter; 2271 ierr = PetscObjectSAWsGrantAccess((PetscObject)tao);CHKERRQ(ierr); 2272 PetscFunctionReturn(0); 2273 } 2274 2275 #undef __FUNCT__ 2276 #define __FUNCT__ "TaoGetTotalIterationNumber" 2277 /*@ 2278 TaoGetTotalIterationNumber - Gets the total number of Tao iterations 2279 completed. This number keeps accumulating if multiple solves 2280 are called with the Tao object. 2281 2282 Not Collective 2283 2284 Input Parameter: 2285 . tao - Tao context 2286 2287 Output Parameter: 2288 . iter - iteration number 2289 2290 Notes: 2291 The total iteration count is updated after each solve, if there is a current 2292 TaoSolve() in progress then those iterations are not yet counted. 2293 2294 Level: intermediate 2295 2296 .keywords: Tao, nonlinear, get, iteration, number, 2297 2298 .seealso: TaoGetLinearSolveIterations() 2299 @*/ 2300 PetscErrorCode TaoGetTotalIterationNumber(Tao tao,PetscInt *iter) 2301 { 2302 PetscFunctionBegin; 2303 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2304 PetscValidIntPointer(iter,2); 2305 *iter = tao->ntotalits; 2306 PetscFunctionReturn(0); 2307 } 2308 2309 #undef __FUNCT__ 2310 #define __FUNCT__ "TaoSetTotalIterationNumber" 2311 /*@ 2312 TaoSetTotalIterationNumber - Sets the current total iteration number. 2313 2314 Not Collective 2315 2316 Input Parameter: 2317 . tao - Tao context 2318 . iter - iteration number 2319 2320 Level: developer 2321 2322 .keywords: Tao, nonlinear, set, iteration, number, 2323 2324 .seealso: TaoGetLinearSolveIterations() 2325 @*/ 2326 PetscErrorCode TaoSetTotalIterationNumber(Tao tao,PetscInt iter) 2327 { 2328 PetscErrorCode ierr; 2329 2330 PetscFunctionBegin; 2331 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2332 ierr = PetscObjectSAWsTakeAccess((PetscObject)tao);CHKERRQ(ierr); 2333 tao->ntotalits = iter; 2334 ierr = PetscObjectSAWsGrantAccess((PetscObject)tao);CHKERRQ(ierr); 2335 PetscFunctionReturn(0); 2336 } 2337 2338 #undef __FUNCT__ 2339 #define __FUNCT__ "TaoSetConvergedReason" 2340 /*@ 2341 TaoSetConvergedReason - Sets the termination flag on a Tao object 2342 2343 Logically Collective on Tao 2344 2345 Input Parameters: 2346 + tao - the Tao context 2347 - reason - one of 2348 $ TAO_CONVERGED_ATOL (2), 2349 $ TAO_CONVERGED_RTOL (3), 2350 $ TAO_CONVERGED_STEPTOL (4), 2351 $ TAO_CONVERGED_MINF (5), 2352 $ TAO_CONVERGED_USER (6), 2353 $ TAO_DIVERGED_MAXITS (-2), 2354 $ TAO_DIVERGED_NAN (-4), 2355 $ TAO_DIVERGED_MAXFCN (-5), 2356 $ TAO_DIVERGED_LS_FAILURE (-6), 2357 $ TAO_DIVERGED_TR_REDUCTION (-7), 2358 $ TAO_DIVERGED_USER (-8), 2359 $ TAO_CONTINUE_ITERATING (0) 2360 2361 Level: intermediate 2362 2363 @*/ 2364 PetscErrorCode TaoSetConvergedReason(Tao tao, TaoConvergedReason reason) 2365 { 2366 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2367 PetscFunctionBegin; 2368 tao->reason = reason; 2369 PetscFunctionReturn(0); 2370 } 2371 2372 #undef __FUNCT__ 2373 #define __FUNCT__ "TaoGetConvergedReason" 2374 /*@ 2375 TaoGetConvergedReason - Gets the reason the Tao iteration was stopped. 2376 2377 Not Collective 2378 2379 Input Parameter: 2380 . tao - the Tao solver context 2381 2382 Output Parameter: 2383 . reason - one of 2384 $ TAO_CONVERGED_FATOL (1) f(X)-f(X*) <= fatol 2385 $ TAO_CONVERGED_FRTOL (2) |f(X) - f(X*)|/|f(X)| < frtol 2386 $ TAO_CONVERGED_GATOL (3) ||g(X)|| < gatol 2387 $ TAO_CONVERGED_GRTOL (4) ||g(X)|| / f(X) < grtol 2388 $ TAO_CONVERGED_GTTOL (5) ||g(X)|| / ||g(X0)|| < gttol 2389 $ TAO_CONVERGED_STEPTOL (6) step size small 2390 $ TAO_CONVERGED_MINF (7) F < F_min 2391 $ TAO_CONVERGED_USER (8) User defined 2392 $ TAO_DIVERGED_MAXITS (-2) its > maxits 2393 $ TAO_DIVERGED_NAN (-4) Numerical problems 2394 $ TAO_DIVERGED_MAXFCN (-5) fevals > max_funcsals 2395 $ TAO_DIVERGED_LS_FAILURE (-6) line search failure 2396 $ TAO_DIVERGED_TR_REDUCTION (-7) trust region failure 2397 $ TAO_DIVERGED_USER(-8) (user defined) 2398 $ TAO_CONTINUE_ITERATING (0) 2399 2400 where 2401 + X - current solution 2402 . X0 - initial guess 2403 . f(X) - current function value 2404 . f(X*) - true solution (estimated) 2405 . g(X) - current gradient 2406 . its - current iterate number 2407 . maxits - maximum number of iterates 2408 . fevals - number of function evaluations 2409 - max_funcsals - maximum number of function evaluations 2410 2411 Level: intermediate 2412 2413 .seealso: TaoSetConvergenceTest(), TaoSetTolerances() 2414 2415 @*/ 2416 PetscErrorCode TaoGetConvergedReason(Tao tao, TaoConvergedReason *reason) 2417 { 2418 PetscFunctionBegin; 2419 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2420 PetscValidPointer(reason,2); 2421 *reason = tao->reason; 2422 PetscFunctionReturn(0); 2423 } 2424 2425 #undef __FUNCT__ 2426 #define __FUNCT__ "TaoGetSolutionStatus" 2427 /*@ 2428 TaoGetSolutionStatus - Get the current iterate, objective value, 2429 residual, infeasibility, and termination 2430 2431 Not Collective 2432 2433 Input Parameters: 2434 . tao - the Tao context 2435 2436 Output Parameters: 2437 + iterate - the current iterate number (>=0) 2438 . f - the current function value 2439 . gnorm - the square of the gradient norm, duality gap, or other measure indicating distance from optimality. 2440 . cnorm - the infeasibility of the current solution with regard to the constraints. 2441 . xdiff - the step length or trust region radius of the most recent iterate. 2442 - reason - The termination reason, which can equal TAO_CONTINUE_ITERATING 2443 2444 Level: intermediate 2445 2446 Note: 2447 TAO returns the values set by the solvers in the routine TaoMonitor(). 2448 2449 Note: 2450 If any of the output arguments are set to NULL, no corresponding value will be returned. 2451 2452 .seealso: TaoMonitor(), TaoGetConvergedReason() 2453 @*/ 2454 PetscErrorCode TaoGetSolutionStatus(Tao tao, PetscInt *its, PetscReal *f, PetscReal *gnorm, PetscReal *cnorm, PetscReal *xdiff, TaoConvergedReason *reason) 2455 { 2456 PetscFunctionBegin; 2457 if (its) *its=tao->niter; 2458 if (f) *f=tao->fc; 2459 if (gnorm) *gnorm=tao->residual; 2460 if (cnorm) *cnorm=tao->cnorm; 2461 if (reason) *reason=tao->reason; 2462 if (xdiff) *xdiff=tao->step; 2463 PetscFunctionReturn(0); 2464 } 2465 2466 #undef __FUNCT__ 2467 #define __FUNCT__ "TaoGetType" 2468 /*@C 2469 TaoGetType - Gets the current Tao algorithm. 2470 2471 Not Collective 2472 2473 Input Parameter: 2474 . tao - the Tao solver context 2475 2476 Output Parameter: 2477 . type - Tao method 2478 2479 Level: intermediate 2480 2481 @*/ 2482 PetscErrorCode TaoGetType(Tao tao, const TaoType *type) 2483 { 2484 PetscFunctionBegin; 2485 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2486 PetscValidPointer(type,2); 2487 *type=((PetscObject)tao)->type_name; 2488 PetscFunctionReturn(0); 2489 } 2490 2491 #undef __FUNCT__ 2492 #define __FUNCT__ "TaoMonitor" 2493 /*@C 2494 TaoMonitor - Monitor the solver and the current solution. This 2495 routine will record the iteration number and residual statistics, 2496 call any monitors specified by the user, and calls the convergence-check routine. 2497 2498 Input Parameters: 2499 + tao - the Tao context 2500 . its - the current iterate number (>=0) 2501 . f - the current objective function value 2502 . res - the gradient norm, square root of the duality gap, or other measure indicating distince from optimality. This measure will be recorded and 2503 used for some termination tests. 2504 . cnorm - the infeasibility of the current solution with regard to the constraints. 2505 - steplength - multiple of the step direction added to the previous iterate. 2506 2507 Output Parameters: 2508 . reason - The termination reason, which can equal TAO_CONTINUE_ITERATING 2509 2510 Options Database Key: 2511 . -tao_monitor - Use the default monitor, which prints statistics to standard output 2512 2513 .seealso TaoGetConvergedReason(), TaoDefaultMonitor(), TaoSetMonitor() 2514 2515 Level: developer 2516 2517 @*/ 2518 PetscErrorCode TaoMonitor(Tao tao, PetscInt its, PetscReal f, PetscReal res, PetscReal cnorm, PetscReal steplength, TaoConvergedReason *reason) 2519 { 2520 PetscErrorCode ierr; 2521 PetscInt i; 2522 2523 PetscFunctionBegin; 2524 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2525 tao->fc = f; 2526 tao->residual = res; 2527 tao->cnorm = cnorm; 2528 tao->step = steplength; 2529 if (its == 0) { 2530 tao->cnorm0 = cnorm; tao->gnorm0 = res; 2531 } 2532 TaoLogConvergenceHistory(tao,f,res,cnorm,tao->ksp_its); 2533 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(res)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN"); 2534 if (tao->ops->convergencetest) { 2535 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 2536 } 2537 for (i=0;i<tao->numbermonitors;i++) { 2538 ierr = (*tao->monitor[i])(tao,tao->monitorcontext[i]);CHKERRQ(ierr); 2539 } 2540 *reason = tao->reason; 2541 PetscFunctionReturn(0); 2542 } 2543 2544 #undef __FUNCT__ 2545 #define __FUNCT__ "TaoSetConvergenceHistory" 2546 /*@ 2547 TaoSetConvergenceHistory - Sets the array used to hold the convergence history. 2548 2549 Logically Collective on Tao 2550 2551 Input Parameters: 2552 + tao - the Tao solver context 2553 . obj - array to hold objective value history 2554 . resid - array to hold residual history 2555 . cnorm - array to hold constraint violation history 2556 . lits - integer array holds the number of linear iterations for each Tao iteration 2557 . na - size of obj, resid, and cnorm 2558 - reset - PetscTrue indicates each new minimization resets the history counter to zero, 2559 else it continues storing new values for new minimizations after the old ones 2560 2561 Notes: 2562 If set, TAO will fill the given arrays with the indicated 2563 information at each iteration. If 'obj','resid','cnorm','lits' are 2564 *all* NULL then space (using size na, or 1000 if na is PETSC_DECIDE or 2565 PETSC_DEFAULT) is allocated for the history. 2566 If not all are NULL, then only the non-NULL information categories 2567 will be stored, the others will be ignored. 2568 2569 Any convergence information after iteration number 'na' will not be stored. 2570 2571 This routine is useful, e.g., when running a code for purposes 2572 of accurate performance monitoring, when no I/O should be done 2573 during the section of code that is being timed. 2574 2575 Level: intermediate 2576 2577 .seealso: TaoGetConvergenceHistory() 2578 2579 @*/ 2580 PetscErrorCode TaoSetConvergenceHistory(Tao tao, PetscReal *obj, PetscReal *resid, PetscReal *cnorm, PetscInt *lits, PetscInt na,PetscBool reset) 2581 { 2582 PetscErrorCode ierr; 2583 2584 PetscFunctionBegin; 2585 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2586 if (obj) PetscValidScalarPointer(obj,2); 2587 if (resid) PetscValidScalarPointer(resid,3); 2588 if (cnorm) PetscValidScalarPointer(cnorm,4); 2589 if (lits) PetscValidIntPointer(lits,5); 2590 2591 if (na == PETSC_DECIDE || na == PETSC_DEFAULT) na = 1000; 2592 if (!obj && !resid && !cnorm && !lits) { 2593 ierr = PetscCalloc1(na,&obj);CHKERRQ(ierr); 2594 ierr = PetscCalloc1(na,&resid);CHKERRQ(ierr); 2595 ierr = PetscCalloc1(na,&cnorm);CHKERRQ(ierr); 2596 ierr = PetscCalloc1(na,&lits);CHKERRQ(ierr); 2597 tao->hist_malloc=PETSC_TRUE; 2598 } 2599 2600 tao->hist_obj = obj; 2601 tao->hist_resid = resid; 2602 tao->hist_cnorm = cnorm; 2603 tao->hist_lits = lits; 2604 tao->hist_max = na; 2605 tao->hist_reset = reset; 2606 tao->hist_len = 0; 2607 PetscFunctionReturn(0); 2608 } 2609 2610 #undef __FUNCT__ 2611 #define __FUNCT__ "TaoGetConvergenceHistory" 2612 /*@C 2613 TaoGetConvergenceHistory - Gets the arrays used to hold the convergence history. 2614 2615 Collective on Tao 2616 2617 Input Parameter: 2618 . tao - the Tao context 2619 2620 Output Parameters: 2621 + obj - array used to hold objective value history 2622 . resid - array used to hold residual history 2623 . cnorm - array used to hold constraint violation history 2624 . lits - integer array used to hold linear solver iteration count 2625 - nhist - size of obj, resid, cnorm, and lits (will be less than or equal to na given in TaoSetHistory) 2626 2627 Notes: 2628 This routine must be preceded by calls to TaoSetConvergenceHistory() 2629 and TaoSolve(), otherwise it returns useless information. 2630 2631 The calling sequence for this routine in Fortran is 2632 $ call TaoGetConvergenceHistory(Tao tao, PetscInt nhist, PetscErrorCode ierr) 2633 2634 This routine is useful, e.g., when running a code for purposes 2635 of accurate performance monitoring, when no I/O should be done 2636 during the section of code that is being timed. 2637 2638 Level: advanced 2639 2640 .seealso: TaoSetConvergenceHistory() 2641 2642 @*/ 2643 PetscErrorCode TaoGetConvergenceHistory(Tao tao, PetscReal **obj, PetscReal **resid, PetscReal **cnorm, PetscInt **lits, PetscInt *nhist) 2644 { 2645 PetscFunctionBegin; 2646 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2647 if (obj) *obj = tao->hist_obj; 2648 if (cnorm) *cnorm = tao->hist_cnorm; 2649 if (resid) *resid = tao->hist_resid; 2650 if (nhist) *nhist = tao->hist_len; 2651 PetscFunctionReturn(0); 2652 } 2653 2654 #undef __FUNCT__ 2655 #define __FUNCT__ "TaoSetApplicationContext" 2656 /*@ 2657 TaoSetApplicationContext - Sets the optional user-defined context for 2658 a solver. 2659 2660 Logically Collective on Tao 2661 2662 Input Parameters: 2663 + tao - the Tao context 2664 - usrP - optional user context 2665 2666 Level: intermediate 2667 2668 .seealso: TaoGetApplicationContext(), TaoSetApplicationContext() 2669 @*/ 2670 PetscErrorCode TaoSetApplicationContext(Tao tao,void *usrP) 2671 { 2672 PetscFunctionBegin; 2673 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2674 tao->user = usrP; 2675 PetscFunctionReturn(0); 2676 } 2677 2678 #undef __FUNCT__ 2679 #define __FUNCT__ "TaoGetApplicationContext" 2680 /*@ 2681 TaoGetApplicationContext - Gets the user-defined context for a 2682 TAO solvers. 2683 2684 Not Collective 2685 2686 Input Parameter: 2687 . tao - Tao context 2688 2689 Output Parameter: 2690 . usrP - user context 2691 2692 Level: intermediate 2693 2694 .seealso: TaoSetApplicationContext() 2695 @*/ 2696 PetscErrorCode TaoGetApplicationContext(Tao tao,void *usrP) 2697 { 2698 PetscFunctionBegin; 2699 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2700 *(void**)usrP = tao->user; 2701 PetscFunctionReturn(0); 2702 } 2703 2704 #undef __FUNCT__ 2705 #define __FUNCT__ "TaoSetGradientNorm" 2706 /*@ 2707 TaoSetGradientNorm - Sets the matrix used to define the inner product that measures the size of the gradient. 2708 2709 Collective on tao 2710 2711 Input Parameters: 2712 + tao - the Tao context 2713 - M - gradient norm 2714 2715 Level: beginner 2716 2717 .seealso: TaoGetGradientNorm(), TaoGradientNorm() 2718 @*/ 2719 PetscErrorCode TaoSetGradientNorm(Tao tao, Mat M) 2720 { 2721 PetscErrorCode ierr; 2722 2723 PetscFunctionBegin; 2724 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2725 2726 if (tao->gradient_norm) { 2727 ierr = PetscObjectDereference((PetscObject)tao->gradient_norm);CHKERRQ(ierr); 2728 ierr = VecDestroy(&tao->gradient_norm_tmp);CHKERRQ(ierr); 2729 } 2730 2731 ierr = PetscObjectReference((PetscObject)M);CHKERRQ(ierr); 2732 tao->gradient_norm = M; 2733 ierr = MatCreateVecs(M, NULL, &tao->gradient_norm_tmp);CHKERRQ(ierr); 2734 PetscFunctionReturn(0); 2735 } 2736 2737 #undef __FUNCT__ 2738 #define __FUNCT__ "TaoGetGradientNorm" 2739 /*@ 2740 TaoGetGradientNorm - Returns the matrix used to define the inner product for measuring the size of the gradient. 2741 2742 Not Collective 2743 2744 Input Parameter: 2745 . tao - Tao context 2746 2747 Output Parameter: 2748 . M - gradient norm 2749 2750 Level: beginner 2751 2752 .seealso: TaoSetGradientNorm(), TaoGradientNorm() 2753 @*/ 2754 PetscErrorCode TaoGetGradientNorm(Tao tao, Mat *M) 2755 { 2756 PetscFunctionBegin; 2757 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2758 *M = tao->gradient_norm; 2759 PetscFunctionReturn(0); 2760 } 2761 2762 #undef __FUNCT__ 2763 #define __FUNCT__ "TaoGradientNorm" 2764 /*c 2765 TaoGradientNorm - Compute the norm with respect to the inner product the user has set. 2766 2767 Collective on tao 2768 2769 Input Parameter: 2770 . tao - the Tao context 2771 . gradient - the gradient to be computed 2772 . norm - the norm type 2773 2774 Output Parameter: 2775 . gnorm - the gradient norm 2776 2777 Level: developer 2778 2779 .seealso: TaoSetGradientNorm(), TaoGetGradientNorm() 2780 @*/ 2781 PetscErrorCode TaoGradientNorm(Tao tao, Vec gradient, NormType type, PetscReal *gnorm) 2782 { 2783 PetscErrorCode ierr; 2784 2785 PetscFunctionBegin; 2786 PetscValidHeaderSpecific(gradient,VEC_CLASSID,1); 2787 2788 if (tao->gradient_norm) { 2789 PetscScalar gnorms; 2790 2791 if (type != NORM_2) SETERRQ(PetscObjectComm((PetscObject)gradient), PETSC_ERR_ARG_WRONGSTATE, "Norm type must be NORM_2 if an inner product for the gradient norm is set."); 2792 ierr = MatMult(tao->gradient_norm, gradient, tao->gradient_norm_tmp);CHKERRQ(ierr); 2793 ierr = VecDot(gradient, tao->gradient_norm_tmp, &gnorms);CHKERRQ(ierr); 2794 *gnorm = PetscRealPart(PetscSqrtScalar(gnorms)); 2795 } else { 2796 ierr = VecNorm(gradient, type, gnorm);CHKERRQ(ierr); 2797 } 2798 PetscFunctionReturn(0); 2799 } 2800 2801 2802