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