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 = (PetscViewer)ctx; 1541 1542 PetscFunctionBegin; 1543 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 1544 its=tao->niter; 1545 fct=tao->fc; 1546 gnorm=tao->residual; 1547 ierr=PetscViewerASCIIPrintf(viewer,"iter = %3D,",its);CHKERRQ(ierr); 1548 ierr=PetscViewerASCIIPrintf(viewer," Function value: %g,",(double)fct);CHKERRQ(ierr); 1549 if (gnorm >= PETSC_INFINITY) { 1550 ierr=PetscViewerASCIIPrintf(viewer," Residual: Inf \n");CHKERRQ(ierr); 1551 } else { 1552 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g \n",(double)gnorm);CHKERRQ(ierr); 1553 } 1554 PetscFunctionReturn(0); 1555 } 1556 1557 #undef __FUNCT__ 1558 #define __FUNCT__ "TaoDefaultSMonitor" 1559 /*@ 1560 TaoDefaultSMonitor - Default routine for monitoring progress of the 1561 solver. Same as TaoDefaultMonitor() except 1562 it prints fewer digits of the residual as the residual gets smaller. 1563 This is because the later digits are meaningless and are often 1564 different on different machines; by using this routine different 1565 machines will usually generate the same output. It can be turned on 1566 by using the -tao_smonitor option 1567 1568 Collective on Tao 1569 1570 Input Parameters: 1571 + tao - the Tao context 1572 - ctx - PetscViewer context of type ASCII 1573 1574 Options Database Keys: 1575 . -tao_smonitor 1576 1577 Level: advanced 1578 1579 .seealso: TaoDefaultMonitor(), TaoSetMonitor() 1580 @*/ 1581 PetscErrorCode TaoDefaultSMonitor(Tao tao, void *ctx) 1582 { 1583 PetscErrorCode ierr; 1584 PetscInt its; 1585 PetscReal fct,gnorm; 1586 PetscViewer viewer = (PetscViewer)ctx; 1587 1588 PetscFunctionBegin; 1589 PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2); 1590 its=tao->niter; 1591 fct=tao->fc; 1592 gnorm=tao->residual; 1593 ierr=PetscViewerASCIIPrintf(viewer,"iter = %3D,",its);CHKERRQ(ierr); 1594 ierr=PetscViewerASCIIPrintf(viewer," Function value %g,",(double)fct);CHKERRQ(ierr); 1595 if (gnorm >= PETSC_INFINITY/2) { 1596 ierr=PetscViewerASCIIPrintf(viewer," Residual: Inf \n");CHKERRQ(ierr); 1597 } else if (gnorm > 1.e-6) { 1598 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g \n",(double)gnorm);CHKERRQ(ierr); 1599 } else if (gnorm > 1.e-11) { 1600 ierr=PetscViewerASCIIPrintf(viewer," Residual: < 1.0e-6 \n");CHKERRQ(ierr); 1601 } else { 1602 ierr=PetscViewerASCIIPrintf(viewer," Residual: < 1.0e-11 \n");CHKERRQ(ierr); 1603 } 1604 PetscFunctionReturn(0); 1605 } 1606 1607 #undef __FUNCT__ 1608 #define __FUNCT__ "TaoDefaultCMonitor" 1609 /*@ 1610 TaoDefaultCMonitor - same as TaoDefaultMonitor() except 1611 it prints the norm of the constraints function. It can be turned on 1612 from the command line using the -tao_cmonitor option 1613 1614 Collective on Tao 1615 1616 Input Parameters: 1617 + tao - the Tao context 1618 - ctx - PetscViewer context or NULL 1619 1620 Options Database Keys: 1621 . -tao_cmonitor 1622 1623 Level: advanced 1624 1625 .seealso: TaoDefaultMonitor(), TaoSetMonitor() 1626 @*/ 1627 PetscErrorCode TaoDefaultCMonitor(Tao tao, void *ctx) 1628 { 1629 PetscErrorCode ierr; 1630 PetscInt its; 1631 PetscReal fct,gnorm; 1632 PetscViewer viewer; 1633 1634 PetscFunctionBegin; 1635 if (ctx) { 1636 viewer = (PetscViewer)ctx; 1637 } else { 1638 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1639 } 1640 its=tao->niter; 1641 fct=tao->fc; 1642 gnorm=tao->residual; 1643 ierr=PetscViewerASCIIPrintf(viewer,"iter = %D,",its);CHKERRQ(ierr); 1644 ierr=PetscViewerASCIIPrintf(viewer," Function value: %g,",(double)fct);CHKERRQ(ierr); 1645 ierr=PetscViewerASCIIPrintf(viewer," Residual: %g ",(double)gnorm);CHKERRQ(ierr); 1646 ierr = PetscViewerASCIIPrintf(viewer," Constraint: %g \n",(double)tao->cnorm);CHKERRQ(ierr); 1647 PetscFunctionReturn(0); 1648 } 1649 1650 #undef __FUNCT__ 1651 #define __FUNCT__ "TaoSolutionMonitor" 1652 /*@C 1653 TaoSolutionMonitor - Views the solution at each iteration 1654 It can be turned on from the command line using the 1655 -tao_view_solution option 1656 1657 Collective on Tao 1658 1659 Input Parameters: 1660 + tao - the Tao context 1661 - ctx - PetscViewer context or NULL 1662 1663 Options Database Keys: 1664 . -tao_view_solution 1665 1666 Level: advanced 1667 1668 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1669 @*/ 1670 PetscErrorCode TaoSolutionMonitor(Tao tao, void *ctx) 1671 { 1672 PetscErrorCode ierr; 1673 PetscViewer viewer; 1674 1675 PetscFunctionBegin; 1676 if (ctx) { 1677 viewer = (PetscViewer)ctx; 1678 } else { 1679 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1680 } 1681 ierr = VecView(tao->solution, viewer);CHKERRQ(ierr); 1682 PetscFunctionReturn(0); 1683 } 1684 1685 #undef __FUNCT__ 1686 #define __FUNCT__ "TaoGradientMonitor" 1687 /*@C 1688 TaoGradientMonitor - Views the gradient at each iteration 1689 It can be turned on from the command line using the 1690 -tao_view_gradient option 1691 1692 Collective on Tao 1693 1694 Input Parameters: 1695 + tao - the Tao context 1696 - ctx - PetscViewer context or NULL 1697 1698 Options Database Keys: 1699 . -tao_view_gradient 1700 1701 Level: advanced 1702 1703 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1704 @*/ 1705 PetscErrorCode TaoGradientMonitor(Tao tao, void *ctx) 1706 { 1707 PetscErrorCode ierr; 1708 PetscViewer viewer; 1709 1710 PetscFunctionBegin; 1711 if (ctx) { 1712 viewer = (PetscViewer)ctx; 1713 } else { 1714 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1715 } 1716 ierr = VecView(tao->gradient, viewer);CHKERRQ(ierr); 1717 PetscFunctionReturn(0); 1718 } 1719 1720 #undef __FUNCT__ 1721 #define __FUNCT__ "TaoStepDirectionMonitor" 1722 /*@C 1723 TaoStepDirectionMonitor - Views the gradient at each iteration 1724 It can be turned on from the command line using the 1725 -tao_view_gradient option 1726 1727 Collective on Tao 1728 1729 Input Parameters: 1730 + tao - the Tao context 1731 - ctx - PetscViewer context or NULL 1732 1733 Options Database Keys: 1734 . -tao_view_gradient 1735 1736 Level: advanced 1737 1738 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1739 @*/ 1740 PetscErrorCode TaoStepDirectionMonitor(Tao tao, void *ctx) 1741 { 1742 PetscErrorCode ierr; 1743 PetscViewer viewer; 1744 PetscFunctionBegin; 1745 if (ctx) { 1746 viewer = (PetscViewer)ctx; 1747 } else { 1748 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1749 } 1750 ierr = VecView(tao->stepdirection, viewer);CHKERRQ(ierr); 1751 PetscFunctionReturn(0); 1752 } 1753 1754 #undef __FUNCT__ 1755 #define __FUNCT__ "TaoDrawSolutionMonitor" 1756 /*@C 1757 TaoDrawSolutionMonitor - Plots the solution at each iteration 1758 It can be turned on from the command line using the 1759 -tao_draw_solution option 1760 1761 Collective on Tao 1762 1763 Input Parameters: 1764 + tao - the Tao context 1765 - ctx - PetscViewer context 1766 1767 Options Database Keys: 1768 . -tao_draw_solution 1769 1770 Level: advanced 1771 1772 .seealso: TaoSolutionMonitor(), TaoSetMonitor(), TaoDrawGradientMonitor 1773 @*/ 1774 PetscErrorCode TaoDrawSolutionMonitor(Tao tao, void *ctx) 1775 { 1776 PetscErrorCode ierr; 1777 PetscViewer viewer = (PetscViewer) ctx; 1778 1779 PetscFunctionBegin; 1780 ierr = VecView(tao->solution, viewer);CHKERRQ(ierr); 1781 PetscFunctionReturn(0); 1782 } 1783 1784 #undef __FUNCT__ 1785 #define __FUNCT__ "TaoDrawGradientMonitor" 1786 /*@C 1787 TaoDrawGradientMonitor - Plots the gradient at each iteration 1788 It can be turned on from the command line using the 1789 -tao_draw_gradient option 1790 1791 Collective on Tao 1792 1793 Input Parameters: 1794 + tao - the Tao context 1795 - ctx - PetscViewer context 1796 1797 Options Database Keys: 1798 . -tao_draw_gradient 1799 1800 Level: advanced 1801 1802 .seealso: TaoGradientMonitor(), TaoSetMonitor(), TaoDrawSolutionMonitor 1803 @*/ 1804 PetscErrorCode TaoDrawGradientMonitor(Tao tao, void *ctx) 1805 { 1806 PetscErrorCode ierr; 1807 PetscViewer viewer = (PetscViewer)ctx; 1808 1809 PetscFunctionBegin; 1810 ierr = VecView(tao->gradient, viewer);CHKERRQ(ierr); 1811 PetscFunctionReturn(0); 1812 } 1813 1814 #undef __FUNCT__ 1815 #define __FUNCT__ "TaoDrawStepMonitor" 1816 /*@C 1817 TaoDrawStepMonitor - Plots the step direction at each iteration 1818 It can be turned on from the command line using the 1819 -tao_draw_step option 1820 1821 Collective on Tao 1822 1823 Input Parameters: 1824 + tao - the Tao context 1825 - ctx - PetscViewer context 1826 1827 Options Database Keys: 1828 . -tao_draw_step 1829 1830 Level: advanced 1831 1832 .seealso: TaoSetMonitor(), TaoDrawSolutionMonitor 1833 @*/ 1834 PetscErrorCode TaoDrawStepMonitor(Tao tao, void *ctx) 1835 { 1836 PetscErrorCode ierr; 1837 PetscViewer viewer = (PetscViewer)(ctx); 1838 1839 PetscFunctionBegin; 1840 ierr = VecView(tao->stepdirection, viewer);CHKERRQ(ierr); 1841 PetscFunctionReturn(0); 1842 } 1843 1844 #undef __FUNCT__ 1845 #define __FUNCT__ "TaoSeparableObjectiveMonitor" 1846 /*@C 1847 TaoSeparableObjectiveMonitor - Views the separable objective function at each iteration 1848 It can be turned on from the command line using the 1849 -tao_view_separableobjective option 1850 1851 Collective on Tao 1852 1853 Input Parameters: 1854 + tao - the Tao context 1855 - ctx - PetscViewer context or NULL 1856 1857 Options Database Keys: 1858 . -tao_view_separableobjective 1859 1860 Level: advanced 1861 1862 .seealso: TaoDefaultSMonitor(), TaoSetMonitor() 1863 @*/ 1864 PetscErrorCode TaoSeparableObjectiveMonitor(Tao tao, void *ctx) 1865 { 1866 PetscErrorCode ierr; 1867 PetscViewer viewer; 1868 1869 PetscFunctionBegin; 1870 if (ctx) { 1871 viewer = (PetscViewer)ctx; 1872 } else { 1873 viewer = PETSC_VIEWER_STDOUT_(((PetscObject)tao)->comm); 1874 } 1875 ierr = VecView(tao->sep_objective,viewer);CHKERRQ(ierr); 1876 PetscFunctionReturn(0); 1877 } 1878 1879 #undef __FUNCT__ 1880 #define __FUNCT__ "TaoDefaultConvergenceTest" 1881 /*@ 1882 TaoDefaultConvergenceTest - Determines whether the solver should continue iterating 1883 or terminate. 1884 1885 Collective on Tao 1886 1887 Input Parameters: 1888 + tao - the Tao context 1889 - dummy - unused dummy context 1890 1891 Output Parameter: 1892 . reason - for terminating 1893 1894 Notes: 1895 This routine checks the residual in the optimality conditions, the 1896 relative residual in the optimity conditions, the number of function 1897 evaluations, and the function value to test convergence. Some 1898 solvers may use different convergence routines. 1899 1900 Level: developer 1901 1902 .seealso: TaoSetTolerances(),TaoGetConvergedReason(),TaoSetConvergedReason() 1903 @*/ 1904 1905 PetscErrorCode TaoDefaultConvergenceTest(Tao tao,void *dummy) 1906 { 1907 PetscInt niter=tao->niter, nfuncs=PetscMax(tao->nfuncs,tao->nfuncgrads); 1908 PetscInt max_funcs=tao->max_funcs; 1909 PetscReal gnorm=tao->residual, gnorm0=tao->gnorm0; 1910 PetscReal f=tao->fc, steptol=tao->steptol,trradius=tao->step; 1911 PetscReal gatol=tao->gatol,grtol=tao->grtol,gttol=tao->gttol; 1912 PetscReal fatol=tao->fatol,frtol=tao->frtol,catol=tao->catol,crtol=tao->crtol; 1913 PetscReal fmin=tao->fmin, cnorm=tao->cnorm, cnorm0=tao->cnorm0; 1914 PetscReal gnorm2; 1915 TaoConvergedReason reason=tao->reason; 1916 PetscErrorCode ierr; 1917 1918 PetscFunctionBegin; 1919 PetscValidHeaderSpecific(tao, TAO_CLASSID,1); 1920 if (reason != TAO_CONTINUE_ITERATING) { 1921 PetscFunctionReturn(0); 1922 } 1923 gnorm2=gnorm*gnorm; 1924 1925 if (PetscIsInfOrNanReal(f)) { 1926 ierr = PetscInfo(tao,"Failed to converged, function value is Inf or NaN\n");CHKERRQ(ierr); 1927 reason = TAO_DIVERGED_NAN; 1928 } else if (f <= fmin && cnorm <=catol) { 1929 ierr = PetscInfo2(tao,"Converged due to function value %g < minimum function value %g\n", (double)f,(double)fmin);CHKERRQ(ierr); 1930 reason = TAO_CONVERGED_MINF; 1931 } else if (gnorm2 <= fatol && cnorm <=catol) { 1932 ierr = PetscInfo2(tao,"Converged due to estimated f(X) - f(X*) = %g < %g\n",(double)gnorm2,(double)fatol);CHKERRQ(ierr); 1933 reason = TAO_CONVERGED_FATOL; 1934 } else if (f != 0 && gnorm2 / PetscAbsReal(f)<= frtol && cnorm/PetscMax(cnorm0,1.0) <= crtol) { 1935 ierr = PetscInfo2(tao,"Converged due to estimated |f(X)-f(X*)|/f(X) = %g < %g\n",(double)(gnorm2/PetscAbsReal(f)),(double)frtol);CHKERRQ(ierr); 1936 reason = TAO_CONVERGED_FRTOL; 1937 } else if (gnorm<= gatol && cnorm <=catol) { 1938 ierr = PetscInfo2(tao,"Converged due to residual norm ||g(X)||=%g < %g\n",(double)gnorm,(double)gatol);CHKERRQ(ierr); 1939 reason = TAO_CONVERGED_GATOL; 1940 } else if ( f!=0 && PetscAbsReal(gnorm/f) <= grtol && cnorm <= crtol) { 1941 ierr = PetscInfo2(tao,"Converged due to residual ||g(X)||/|f(X)| =%g < %g\n",(double)(gnorm/f),(double)grtol);CHKERRQ(ierr); 1942 reason = TAO_CONVERGED_GRTOL; 1943 } else if (gnorm0 != 0 && gnorm/gnorm0 <= gttol && cnorm <= crtol) { 1944 ierr = PetscInfo2(tao,"Converged due to relative residual norm ||g(X)||/||g(X0)|| = %g < %g\n",(double)(gnorm/gnorm0),(double)gttol);CHKERRQ(ierr); 1945 reason = TAO_CONVERGED_GTTOL; 1946 } else if (nfuncs > max_funcs){ 1947 ierr = PetscInfo2(tao,"Exceeded maximum number of function evaluations: %D > %D\n", nfuncs,max_funcs);CHKERRQ(ierr); 1948 reason = TAO_DIVERGED_MAXFCN; 1949 } else if ( tao->lsflag != 0 ){ 1950 ierr = PetscInfo(tao,"Tao Line Search failure.\n");CHKERRQ(ierr); 1951 reason = TAO_DIVERGED_LS_FAILURE; 1952 } else if (trradius < steptol && niter > 0){ 1953 ierr = PetscInfo2(tao,"Trust region/step size too small: %g < %g\n", (double)trradius,(double)steptol);CHKERRQ(ierr); 1954 reason = TAO_CONVERGED_STEPTOL; 1955 } else if (niter > tao->max_it) { 1956 ierr = PetscInfo2(tao,"Exceeded maximum number of iterations: %D > %D\n",niter,tao->max_it);CHKERRQ(ierr); 1957 reason = TAO_DIVERGED_MAXITS; 1958 } else { 1959 reason = TAO_CONTINUE_ITERATING; 1960 } 1961 tao->reason = reason; 1962 PetscFunctionReturn(0); 1963 } 1964 1965 #undef __FUNCT__ 1966 #define __FUNCT__ "TaoSetOptionsPrefix" 1967 /*@C 1968 TaoSetOptionsPrefix - Sets the prefix used for searching for all 1969 TAO options in the database. 1970 1971 1972 Logically Collective on Tao 1973 1974 Input Parameters: 1975 + tao - the Tao context 1976 - prefix - the prefix string to prepend to all TAO option requests 1977 1978 Notes: 1979 A hyphen (-) must NOT be given at the beginning of the prefix name. 1980 The first character of all runtime options is AUTOMATICALLY the hyphen. 1981 1982 For example, to distinguish between the runtime options for two 1983 different TAO solvers, one could call 1984 .vb 1985 TaoSetOptionsPrefix(tao1,"sys1_") 1986 TaoSetOptionsPrefix(tao2,"sys2_") 1987 .ve 1988 1989 This would enable use of different options for each system, such as 1990 .vb 1991 -sys1_tao_method blmvm -sys1_tao_gtol 1.e-3 1992 -sys2_tao_method lmvm -sys2_tao_gtol 1.e-4 1993 .ve 1994 1995 1996 Level: advanced 1997 1998 .seealso: TaoAppendOptionsPrefix(), TaoGetOptionsPrefix() 1999 @*/ 2000 2001 PetscErrorCode TaoSetOptionsPrefix(Tao tao, const char p[]) 2002 { 2003 PetscErrorCode ierr; 2004 2005 PetscFunctionBegin; 2006 ierr = PetscObjectSetOptionsPrefix((PetscObject)tao,p);CHKERRQ(ierr); 2007 if (tao->linesearch) { 2008 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,p);CHKERRQ(ierr); 2009 } 2010 if (tao->ksp) { 2011 ierr = KSPSetOptionsPrefix(tao->ksp,p);CHKERRQ(ierr); 2012 } 2013 PetscFunctionReturn(0); 2014 } 2015 2016 #undef __FUNCT__ 2017 #define __FUNCT__ "TaoAppendOptionsPrefix" 2018 /*@C 2019 TaoAppendOptionsPrefix - Appends to the prefix used for searching for all 2020 TAO options in the database. 2021 2022 2023 Logically Collective on Tao 2024 2025 Input Parameters: 2026 + tao - the Tao solver context 2027 - prefix - the prefix string to prepend to all TAO option requests 2028 2029 Notes: 2030 A hyphen (-) must NOT be given at the beginning of the prefix name. 2031 The first character of all runtime options is AUTOMATICALLY the hyphen. 2032 2033 2034 Level: advanced 2035 2036 .seealso: TaoSetOptionsPrefix(), TaoGetOptionsPrefix() 2037 @*/ 2038 PetscErrorCode TaoAppendOptionsPrefix(Tao tao, const char p[]) 2039 { 2040 PetscErrorCode ierr; 2041 2042 PetscFunctionBegin; 2043 ierr = PetscObjectAppendOptionsPrefix((PetscObject)tao,p);CHKERRQ(ierr); 2044 if (tao->linesearch) { 2045 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,p);CHKERRQ(ierr); 2046 } 2047 if (tao->ksp) { 2048 ierr = KSPSetOptionsPrefix(tao->ksp,p);CHKERRQ(ierr); 2049 } 2050 PetscFunctionReturn(0); 2051 } 2052 2053 #undef __FUNCT__ 2054 #define __FUNCT__ "TaoGetOptionsPrefix" 2055 /*@C 2056 TaoGetOptionsPrefix - Gets the prefix used for searching for all 2057 TAO options in the database 2058 2059 Not Collective 2060 2061 Input Parameters: 2062 . tao - the Tao context 2063 2064 Output Parameters: 2065 . prefix - pointer to the prefix string used is returned 2066 2067 Notes: On the fortran side, the user should pass in a string 'prefix' of 2068 sufficient length to hold the prefix. 2069 2070 Level: advanced 2071 2072 .seealso: TaoSetOptionsPrefix(), TaoAppendOptionsPrefix() 2073 @*/ 2074 PetscErrorCode TaoGetOptionsPrefix(Tao tao, const char *p[]) 2075 { 2076 return PetscObjectGetOptionsPrefix((PetscObject)tao,p); 2077 } 2078 2079 #undef __FUNCT__ 2080 #define __FUNCT__ "TaoSetType" 2081 /*@C 2082 TaoSetType - Sets the method for the unconstrained minimization solver. 2083 2084 Collective on Tao 2085 2086 Input Parameters: 2087 + solver - the Tao solver context 2088 - type - a known method 2089 2090 Options Database Key: 2091 . -tao_type <type> - Sets the method; use -help for a list 2092 of available methods (for instance, "-tao_type lmvm" or "-tao_type tron") 2093 2094 Available methods include: 2095 + nls - Newton's method with line search for unconstrained minimization 2096 . ntr - Newton's method with trust region for unconstrained minimization 2097 . ntl - Newton's method with trust region, line search for unconstrained minimization 2098 . lmvm - Limited memory variable metric method for unconstrained minimization 2099 . cg - Nonlinear conjugate gradient method for unconstrained minimization 2100 . nm - Nelder-Mead algorithm for derivate-free unconstrained minimization 2101 . tron - Newton Trust Region method for bound constrained minimization 2102 . gpcg - Newton Trust Region method for quadratic bound constrained minimization 2103 . blmvm - Limited memory variable metric method for bound constrained minimization 2104 - pounders - Model-based algorithm pounder extended for nonlinear least squares 2105 2106 Level: intermediate 2107 2108 .seealso: TaoCreate(), TaoGetType(), TaoType 2109 2110 @*/ 2111 PetscErrorCode TaoSetType(Tao tao, const TaoType type) 2112 { 2113 PetscErrorCode ierr; 2114 PetscErrorCode (*create_xxx)(Tao); 2115 PetscBool issame; 2116 2117 PetscFunctionBegin; 2118 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2119 2120 ierr = PetscObjectTypeCompare((PetscObject)tao,type,&issame);CHKERRQ(ierr); 2121 if (issame) PetscFunctionReturn(0); 2122 2123 ierr = PetscFunctionListFind(TaoList, type, (void(**)(void))&create_xxx);CHKERRQ(ierr); 2124 if (!create_xxx) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unable to find requested Tao type %s",type); 2125 2126 /* Destroy the existing solver information */ 2127 if (tao->ops->destroy) { 2128 ierr = (*tao->ops->destroy)(tao);CHKERRQ(ierr); 2129 } 2130 ierr = KSPDestroy(&tao->ksp);CHKERRQ(ierr); 2131 ierr = TaoLineSearchDestroy(&tao->linesearch);CHKERRQ(ierr); 2132 ierr = VecDestroy(&tao->gradient);CHKERRQ(ierr); 2133 ierr = VecDestroy(&tao->stepdirection);CHKERRQ(ierr); 2134 2135 tao->ops->setup = 0; 2136 tao->ops->solve = 0; 2137 tao->ops->view = 0; 2138 tao->ops->setfromoptions = 0; 2139 tao->ops->destroy = 0; 2140 2141 tao->setupcalled = PETSC_FALSE; 2142 2143 ierr = (*create_xxx)(tao);CHKERRQ(ierr); 2144 ierr = PetscObjectChangeTypeName((PetscObject)tao,type);CHKERRQ(ierr); 2145 PetscFunctionReturn(0); 2146 } 2147 2148 #undef __FUNCT__ 2149 #define __FUNCT__ "TaoRegister" 2150 /*MC 2151 TaoRegister - Adds a method to the TAO package for unconstrained minimization. 2152 2153 Synopsis: 2154 TaoRegister(char *name_solver,char *path,char *name_Create,int (*routine_Create)(Tao)) 2155 2156 Not collective 2157 2158 Input Parameters: 2159 + sname - name of a new user-defined solver 2160 - func - routine to Create method context 2161 2162 Notes: 2163 TaoRegister() may be called multiple times to add several user-defined solvers. 2164 2165 Sample usage: 2166 .vb 2167 TaoRegister("my_solver",MySolverCreate); 2168 .ve 2169 2170 Then, your solver can be chosen with the procedural interface via 2171 $ TaoSetType(tao,"my_solver") 2172 or at runtime via the option 2173 $ -tao_type my_solver 2174 2175 Level: advanced 2176 2177 .seealso: TaoRegisterAll(), TaoRegisterDestroy() 2178 M*/ 2179 PetscErrorCode TaoRegister(const char sname[], PetscErrorCode (*func)(Tao)) 2180 { 2181 PetscErrorCode ierr; 2182 2183 PetscFunctionBegin; 2184 ierr = PetscFunctionListAdd(&TaoList,sname, (void (*)(void))func);CHKERRQ(ierr); 2185 PetscFunctionReturn(0); 2186 } 2187 2188 #undef __FUNCT__ 2189 #define __FUNCT__ "TaoRegisterDestroy" 2190 /*@C 2191 TaoRegisterDestroy - Frees the list of minimization solvers that were 2192 registered by TaoRegisterDynamic(). 2193 2194 Not Collective 2195 2196 Level: advanced 2197 2198 .seealso: TaoRegisterAll(), TaoRegister() 2199 @*/ 2200 PetscErrorCode TaoRegisterDestroy(void) 2201 { 2202 PetscErrorCode ierr; 2203 PetscFunctionBegin; 2204 ierr = PetscFunctionListDestroy(&TaoList);CHKERRQ(ierr); 2205 TaoRegisterAllCalled = PETSC_FALSE; 2206 PetscFunctionReturn(0); 2207 } 2208 2209 #undef __FUNCT__ 2210 #define __FUNCT__ "TaoGetIterationNumber" 2211 /*@ 2212 TaoGetIterationNumber - Gets the number of Tao iterations completed 2213 at this time. 2214 2215 Not Collective 2216 2217 Input Parameter: 2218 . tao - Tao context 2219 2220 Output Parameter: 2221 . iter - iteration number 2222 2223 Notes: 2224 For example, during the computation of iteration 2 this would return 1. 2225 2226 2227 Level: intermediate 2228 2229 .keywords: Tao, nonlinear, get, iteration, number, 2230 2231 .seealso: TaoGetLinearSolveIterations() 2232 @*/ 2233 PetscErrorCode TaoGetIterationNumber(Tao tao,PetscInt *iter) 2234 { 2235 PetscFunctionBegin; 2236 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2237 PetscValidIntPointer(iter,2); 2238 *iter = tao->niter; 2239 PetscFunctionReturn(0); 2240 } 2241 2242 #undef __FUNCT__ 2243 #define __FUNCT__ "TaoSetIterationNumber" 2244 /*@ 2245 TaoSetIterationNumber - Sets the current iteration number. 2246 2247 Not Collective 2248 2249 Input Parameter: 2250 . tao - Tao context 2251 . iter - iteration number 2252 2253 Level: developer 2254 2255 .keywords: Tao, nonlinear, set, iteration, number, 2256 2257 .seealso: TaoGetLinearSolveIterations() 2258 @*/ 2259 PetscErrorCode TaoSetIterationNumber(Tao tao,PetscInt iter) 2260 { 2261 PetscErrorCode ierr; 2262 2263 PetscFunctionBegin; 2264 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2265 ierr = PetscObjectSAWsTakeAccess((PetscObject)tao);CHKERRQ(ierr); 2266 tao->niter = iter; 2267 ierr = PetscObjectSAWsGrantAccess((PetscObject)tao);CHKERRQ(ierr); 2268 PetscFunctionReturn(0); 2269 } 2270 2271 #undef __FUNCT__ 2272 #define __FUNCT__ "TaoGetTotalIterationNumber" 2273 /*@ 2274 TaoGetTotalIterationNumber - Gets the total number of Tao iterations 2275 completed. This number keeps accumulating if multiple solves 2276 are called with the Tao object. 2277 2278 Not Collective 2279 2280 Input Parameter: 2281 . tao - Tao context 2282 2283 Output Parameter: 2284 . iter - iteration number 2285 2286 Notes: 2287 The total iteration count is updated after each solve, if there is a current 2288 TaoSolve() in progress then those iterations are not yet counted. 2289 2290 Level: intermediate 2291 2292 .keywords: Tao, nonlinear, get, iteration, number, 2293 2294 .seealso: TaoGetLinearSolveIterations() 2295 @*/ 2296 PetscErrorCode TaoGetTotalIterationNumber(Tao tao,PetscInt *iter) 2297 { 2298 PetscFunctionBegin; 2299 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2300 PetscValidIntPointer(iter,2); 2301 *iter = tao->ntotalits; 2302 PetscFunctionReturn(0); 2303 } 2304 2305 #undef __FUNCT__ 2306 #define __FUNCT__ "TaoSetTotalIterationNumber" 2307 /*@ 2308 TaoSetTotalIterationNumber - Sets the current total iteration number. 2309 2310 Not Collective 2311 2312 Input Parameter: 2313 . tao - Tao context 2314 . iter - iteration number 2315 2316 Level: developer 2317 2318 .keywords: Tao, nonlinear, set, iteration, number, 2319 2320 .seealso: TaoGetLinearSolveIterations() 2321 @*/ 2322 PetscErrorCode TaoSetTotalIterationNumber(Tao tao,PetscInt iter) 2323 { 2324 PetscErrorCode ierr; 2325 2326 PetscFunctionBegin; 2327 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2328 ierr = PetscObjectSAWsTakeAccess((PetscObject)tao);CHKERRQ(ierr); 2329 tao->ntotalits = iter; 2330 ierr = PetscObjectSAWsGrantAccess((PetscObject)tao);CHKERRQ(ierr); 2331 PetscFunctionReturn(0); 2332 } 2333 2334 #undef __FUNCT__ 2335 #define __FUNCT__ "TaoSetConvergedReason" 2336 /*@ 2337 TaoSetConvergedReason - Sets the termination flag on a Tao object 2338 2339 Logically Collective on Tao 2340 2341 Input Parameters: 2342 + tao - the Tao context 2343 - reason - one of 2344 $ TAO_CONVERGED_ATOL (2), 2345 $ TAO_CONVERGED_RTOL (3), 2346 $ TAO_CONVERGED_STEPTOL (4), 2347 $ TAO_CONVERGED_MINF (5), 2348 $ TAO_CONVERGED_USER (6), 2349 $ TAO_DIVERGED_MAXITS (-2), 2350 $ TAO_DIVERGED_NAN (-4), 2351 $ TAO_DIVERGED_MAXFCN (-5), 2352 $ TAO_DIVERGED_LS_FAILURE (-6), 2353 $ TAO_DIVERGED_TR_REDUCTION (-7), 2354 $ TAO_DIVERGED_USER (-8), 2355 $ TAO_CONTINUE_ITERATING (0) 2356 2357 Level: intermediate 2358 2359 @*/ 2360 PetscErrorCode TaoSetConvergedReason(Tao tao, TaoConvergedReason reason) 2361 { 2362 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2363 PetscFunctionBegin; 2364 tao->reason = reason; 2365 PetscFunctionReturn(0); 2366 } 2367 2368 #undef __FUNCT__ 2369 #define __FUNCT__ "TaoGetConvergedReason" 2370 /*@ 2371 TaoGetConvergedReason - Gets the reason the Tao iteration was stopped. 2372 2373 Not Collective 2374 2375 Input Parameter: 2376 . tao - the Tao solver context 2377 2378 Output Parameter: 2379 . reason - one of 2380 $ TAO_CONVERGED_FATOL (1) f(X)-f(X*) <= fatol 2381 $ TAO_CONVERGED_FRTOL (2) |f(X) - f(X*)|/|f(X)| < frtol 2382 $ TAO_CONVERGED_GATOL (3) ||g(X)|| < gatol 2383 $ TAO_CONVERGED_GRTOL (4) ||g(X)|| / f(X) < grtol 2384 $ TAO_CONVERGED_GTTOL (5) ||g(X)|| / ||g(X0)|| < gttol 2385 $ TAO_CONVERGED_STEPTOL (6) step size small 2386 $ TAO_CONVERGED_MINF (7) F < F_min 2387 $ TAO_CONVERGED_USER (8) User defined 2388 $ TAO_DIVERGED_MAXITS (-2) its > maxits 2389 $ TAO_DIVERGED_NAN (-4) Numerical problems 2390 $ TAO_DIVERGED_MAXFCN (-5) fevals > max_funcsals 2391 $ TAO_DIVERGED_LS_FAILURE (-6) line search failure 2392 $ TAO_DIVERGED_TR_REDUCTION (-7) trust region failure 2393 $ TAO_DIVERGED_USER(-8) (user defined) 2394 $ TAO_CONTINUE_ITERATING (0) 2395 2396 where 2397 + X - current solution 2398 . X0 - initial guess 2399 . f(X) - current function value 2400 . f(X*) - true solution (estimated) 2401 . g(X) - current gradient 2402 . its - current iterate number 2403 . maxits - maximum number of iterates 2404 . fevals - number of function evaluations 2405 - max_funcsals - maximum number of function evaluations 2406 2407 Level: intermediate 2408 2409 .seealso: TaoSetConvergenceTest(), TaoSetTolerances() 2410 2411 @*/ 2412 PetscErrorCode TaoGetConvergedReason(Tao tao, TaoConvergedReason *reason) 2413 { 2414 PetscFunctionBegin; 2415 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2416 PetscValidPointer(reason,2); 2417 *reason = tao->reason; 2418 PetscFunctionReturn(0); 2419 } 2420 2421 #undef __FUNCT__ 2422 #define __FUNCT__ "TaoGetSolutionStatus" 2423 /*@ 2424 TaoGetSolutionStatus - Get the current iterate, objective value, 2425 residual, infeasibility, and termination 2426 2427 Not Collective 2428 2429 Input Parameters: 2430 . tao - the Tao context 2431 2432 Output Parameters: 2433 + iterate - the current iterate number (>=0) 2434 . f - the current function value 2435 . gnorm - the square of the gradient norm, duality gap, or other measure indicating distance from optimality. 2436 . cnorm - the infeasibility of the current solution with regard to the constraints. 2437 . xdiff - the step length or trust region radius of the most recent iterate. 2438 - reason - The termination reason, which can equal TAO_CONTINUE_ITERATING 2439 2440 Level: intermediate 2441 2442 Note: 2443 TAO returns the values set by the solvers in the routine TaoMonitor(). 2444 2445 Note: 2446 If any of the output arguments are set to NULL, no corresponding value will be returned. 2447 2448 .seealso: TaoMonitor(), TaoGetConvergedReason() 2449 @*/ 2450 PetscErrorCode TaoGetSolutionStatus(Tao tao, PetscInt *its, PetscReal *f, PetscReal *gnorm, PetscReal *cnorm, PetscReal *xdiff, TaoConvergedReason *reason) 2451 { 2452 PetscFunctionBegin; 2453 if (its) *its=tao->niter; 2454 if (f) *f=tao->fc; 2455 if (gnorm) *gnorm=tao->residual; 2456 if (cnorm) *cnorm=tao->cnorm; 2457 if (reason) *reason=tao->reason; 2458 if (xdiff) *xdiff=tao->step; 2459 PetscFunctionReturn(0); 2460 } 2461 2462 #undef __FUNCT__ 2463 #define __FUNCT__ "TaoGetType" 2464 /*@C 2465 TaoGetType - Gets the current Tao algorithm. 2466 2467 Not Collective 2468 2469 Input Parameter: 2470 . tao - the Tao solver context 2471 2472 Output Parameter: 2473 . type - Tao method 2474 2475 Level: intermediate 2476 2477 @*/ 2478 PetscErrorCode TaoGetType(Tao tao, const TaoType *type) 2479 { 2480 PetscFunctionBegin; 2481 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2482 PetscValidPointer(type,2); 2483 *type=((PetscObject)tao)->type_name; 2484 PetscFunctionReturn(0); 2485 } 2486 2487 #undef __FUNCT__ 2488 #define __FUNCT__ "TaoMonitor" 2489 /*@C 2490 TaoMonitor - Monitor the solver and the current solution. This 2491 routine will record the iteration number and residual statistics, 2492 call any monitors specified by the user, and calls the convergence-check routine. 2493 2494 Input Parameters: 2495 + tao - the Tao context 2496 . its - the current iterate number (>=0) 2497 . f - the current objective function value 2498 . res - the gradient norm, square root of the duality gap, or other measure indicating distince from optimality. This measure will be recorded and 2499 used for some termination tests. 2500 . cnorm - the infeasibility of the current solution with regard to the constraints. 2501 - steplength - multiple of the step direction added to the previous iterate. 2502 2503 Output Parameters: 2504 . reason - The termination reason, which can equal TAO_CONTINUE_ITERATING 2505 2506 Options Database Key: 2507 . -tao_monitor - Use the default monitor, which prints statistics to standard output 2508 2509 .seealso TaoGetConvergedReason(), TaoDefaultMonitor(), TaoSetMonitor() 2510 2511 Level: developer 2512 2513 @*/ 2514 PetscErrorCode TaoMonitor(Tao tao, PetscInt its, PetscReal f, PetscReal res, PetscReal cnorm, PetscReal steplength, TaoConvergedReason *reason) 2515 { 2516 PetscErrorCode ierr; 2517 PetscInt i; 2518 2519 PetscFunctionBegin; 2520 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2521 tao->fc = f; 2522 tao->residual = res; 2523 tao->cnorm = cnorm; 2524 tao->step = steplength; 2525 if (its == 0) { 2526 tao->cnorm0 = cnorm; tao->gnorm0 = res; 2527 } 2528 TaoLogConvergenceHistory(tao,f,res,cnorm,tao->ksp_its); 2529 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(res)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN"); 2530 if (tao->ops->convergencetest) { 2531 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 2532 } 2533 for (i=0;i<tao->numbermonitors;i++) { 2534 ierr = (*tao->monitor[i])(tao,tao->monitorcontext[i]);CHKERRQ(ierr); 2535 } 2536 *reason = tao->reason; 2537 PetscFunctionReturn(0); 2538 } 2539 2540 #undef __FUNCT__ 2541 #define __FUNCT__ "TaoSetConvergenceHistory" 2542 /*@ 2543 TaoSetConvergenceHistory - Sets the array used to hold the convergence history. 2544 2545 Logically Collective on Tao 2546 2547 Input Parameters: 2548 + tao - the Tao solver context 2549 . obj - array to hold objective value history 2550 . resid - array to hold residual history 2551 . cnorm - array to hold constraint violation history 2552 . lits - integer array holds the number of linear iterations for each Tao iteration 2553 . na - size of obj, resid, and cnorm 2554 - reset - PetscTrue indicates each new minimization resets the history counter to zero, 2555 else it continues storing new values for new minimizations after the old ones 2556 2557 Notes: 2558 If set, TAO will fill the given arrays with the indicated 2559 information at each iteration. If 'obj','resid','cnorm','lits' are 2560 *all* NULL then space (using size na, or 1000 if na is PETSC_DECIDE or 2561 PETSC_DEFAULT) is allocated for the history. 2562 If not all are NULL, then only the non-NULL information categories 2563 will be stored, the others will be ignored. 2564 2565 Any convergence information after iteration number 'na' will not be stored. 2566 2567 This routine is useful, e.g., when running a code for purposes 2568 of accurate performance monitoring, when no I/O should be done 2569 during the section of code that is being timed. 2570 2571 Level: intermediate 2572 2573 .seealso: TaoGetConvergenceHistory() 2574 2575 @*/ 2576 PetscErrorCode TaoSetConvergenceHistory(Tao tao, PetscReal *obj, PetscReal *resid, PetscReal *cnorm, PetscInt *lits, PetscInt na,PetscBool reset) 2577 { 2578 PetscErrorCode ierr; 2579 2580 PetscFunctionBegin; 2581 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2582 if (obj) PetscValidScalarPointer(obj,2); 2583 if (resid) PetscValidScalarPointer(resid,3); 2584 if (cnorm) PetscValidScalarPointer(cnorm,4); 2585 if (lits) PetscValidIntPointer(lits,5); 2586 2587 if (na == PETSC_DECIDE || na == PETSC_DEFAULT) na = 1000; 2588 if (!obj && !resid && !cnorm && !lits) { 2589 ierr = PetscCalloc1(na,&obj);CHKERRQ(ierr); 2590 ierr = PetscCalloc1(na,&resid);CHKERRQ(ierr); 2591 ierr = PetscCalloc1(na,&cnorm);CHKERRQ(ierr); 2592 ierr = PetscCalloc1(na,&lits);CHKERRQ(ierr); 2593 tao->hist_malloc=PETSC_TRUE; 2594 } 2595 2596 tao->hist_obj = obj; 2597 tao->hist_resid = resid; 2598 tao->hist_cnorm = cnorm; 2599 tao->hist_lits = lits; 2600 tao->hist_max = na; 2601 tao->hist_reset = reset; 2602 tao->hist_len = 0; 2603 PetscFunctionReturn(0); 2604 } 2605 2606 #undef __FUNCT__ 2607 #define __FUNCT__ "TaoGetConvergenceHistory" 2608 /*@C 2609 TaoGetConvergenceHistory - Gets the arrays used to hold the convergence history. 2610 2611 Collective on Tao 2612 2613 Input Parameter: 2614 . tao - the Tao context 2615 2616 Output Parameters: 2617 + obj - array used to hold objective value history 2618 . resid - array used to hold residual history 2619 . cnorm - array used to hold constraint violation history 2620 . lits - integer array used to hold linear solver iteration count 2621 - nhist - size of obj, resid, cnorm, and lits (will be less than or equal to na given in TaoSetHistory) 2622 2623 Notes: 2624 This routine must be preceded by calls to TaoSetConvergenceHistory() 2625 and TaoSolve(), otherwise it returns useless information. 2626 2627 The calling sequence for this routine in Fortran is 2628 $ call TaoGetConvergenceHistory(Tao tao, PetscInt nhist, PetscErrorCode ierr) 2629 2630 This routine is useful, e.g., when running a code for purposes 2631 of accurate performance monitoring, when no I/O should be done 2632 during the section of code that is being timed. 2633 2634 Level: advanced 2635 2636 .seealso: TaoSetConvergenceHistory() 2637 2638 @*/ 2639 PetscErrorCode TaoGetConvergenceHistory(Tao tao, PetscReal **obj, PetscReal **resid, PetscReal **cnorm, PetscInt **lits, PetscInt *nhist) 2640 { 2641 PetscFunctionBegin; 2642 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2643 if (obj) *obj = tao->hist_obj; 2644 if (cnorm) *cnorm = tao->hist_cnorm; 2645 if (resid) *resid = tao->hist_resid; 2646 if (nhist) *nhist = tao->hist_len; 2647 PetscFunctionReturn(0); 2648 } 2649 2650 #undef __FUNCT__ 2651 #define __FUNCT__ "TaoSetApplicationContext" 2652 /*@ 2653 TaoSetApplicationContext - Sets the optional user-defined context for 2654 a solver. 2655 2656 Logically Collective on Tao 2657 2658 Input Parameters: 2659 + tao - the Tao context 2660 - usrP - optional user context 2661 2662 Level: intermediate 2663 2664 .seealso: TaoGetApplicationContext(), TaoSetApplicationContext() 2665 @*/ 2666 PetscErrorCode TaoSetApplicationContext(Tao tao,void *usrP) 2667 { 2668 PetscFunctionBegin; 2669 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2670 tao->user = usrP; 2671 PetscFunctionReturn(0); 2672 } 2673 2674 #undef __FUNCT__ 2675 #define __FUNCT__ "TaoGetApplicationContext" 2676 /*@ 2677 TaoGetApplicationContext - Gets the user-defined context for a 2678 TAO solvers. 2679 2680 Not Collective 2681 2682 Input Parameter: 2683 . tao - Tao context 2684 2685 Output Parameter: 2686 . usrP - user context 2687 2688 Level: intermediate 2689 2690 .seealso: TaoSetApplicationContext() 2691 @*/ 2692 PetscErrorCode TaoGetApplicationContext(Tao tao,void *usrP) 2693 { 2694 PetscFunctionBegin; 2695 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2696 *(void**)usrP = tao->user; 2697 PetscFunctionReturn(0); 2698 } 2699 2700 #undef __FUNCT__ 2701 #define __FUNCT__ "TaoSetGradientNorm" 2702 /*@ 2703 TaoSetGradientNorm - Sets the matrix used to define the inner product that measures the size of the gradient. 2704 2705 Collective on tao 2706 2707 Input Parameters: 2708 + tao - the Tao context 2709 - M - gradient norm 2710 2711 Level: beginner 2712 2713 .seealso: TaoGetGradientNorm(), TaoGradientNorm() 2714 @*/ 2715 PetscErrorCode TaoSetGradientNorm(Tao tao, Mat M) 2716 { 2717 PetscErrorCode ierr; 2718 2719 PetscFunctionBegin; 2720 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2721 2722 if (tao->gradient_norm) { 2723 ierr = PetscObjectDereference((PetscObject)tao->gradient_norm);CHKERRQ(ierr); 2724 ierr = VecDestroy(&tao->gradient_norm_tmp);CHKERRQ(ierr); 2725 } 2726 2727 ierr = PetscObjectReference((PetscObject)M);CHKERRQ(ierr); 2728 tao->gradient_norm = M; 2729 ierr = MatCreateVecs(M, NULL, &tao->gradient_norm_tmp);CHKERRQ(ierr); 2730 PetscFunctionReturn(0); 2731 } 2732 2733 #undef __FUNCT__ 2734 #define __FUNCT__ "TaoGetGradientNorm" 2735 /*@ 2736 TaoGetGradientNorm - Returns the matrix used to define the inner product for measuring the size of the gradient. 2737 2738 Not Collective 2739 2740 Input Parameter: 2741 . tao - Tao context 2742 2743 Output Parameter: 2744 . M - gradient norm 2745 2746 Level: beginner 2747 2748 .seealso: TaoSetGradientNorm(), TaoGradientNorm() 2749 @*/ 2750 PetscErrorCode TaoGetGradientNorm(Tao tao, Mat *M) 2751 { 2752 PetscFunctionBegin; 2753 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 2754 *M = tao->gradient_norm; 2755 PetscFunctionReturn(0); 2756 } 2757 2758 #undef __FUNCT__ 2759 #define __FUNCT__ "TaoGradientNorm" 2760 /*c 2761 TaoGradientNorm - Compute the norm with respect to the inner product the user has set. 2762 2763 Collective on tao 2764 2765 Input Parameter: 2766 . tao - the Tao context 2767 . gradient - the gradient to be computed 2768 . norm - the norm type 2769 2770 Output Parameter: 2771 . gnorm - the gradient norm 2772 2773 Level: developer 2774 2775 .seealso: TaoSetGradientNorm(), TaoGetGradientNorm() 2776 @*/ 2777 PetscErrorCode TaoGradientNorm(Tao tao, Vec gradient, NormType type, PetscReal *gnorm) 2778 { 2779 PetscErrorCode ierr; 2780 2781 PetscFunctionBegin; 2782 PetscValidHeaderSpecific(gradient,VEC_CLASSID,1); 2783 2784 if (tao->gradient_norm) { 2785 PetscScalar gnorms; 2786 2787 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."); 2788 ierr = MatMult(tao->gradient_norm, gradient, tao->gradient_norm_tmp);CHKERRQ(ierr); 2789 ierr = VecDot(gradient, tao->gradient_norm_tmp, &gnorms);CHKERRQ(ierr); 2790 *gnorm = PetscRealPart(PetscSqrtScalar(gnorms)); 2791 } else { 2792 ierr = VecNorm(gradient, type, gnorm);CHKERRQ(ierr); 2793 } 2794 PetscFunctionReturn(0); 2795 } 2796 2797 2798