1 #include <petsctaolinesearch.h> 2 #include <../src/tao/matrix/lmvmmat.h> 3 #include <../src/tao/unconstrained/impls/lmvm/lmvm.h> 4 #include <../src/tao/bound/impls/blmvm/blmvm.h> 5 6 /*------------------------------------------------------------*/ 7 static PetscErrorCode TaoSolve_BLMVM(Tao tao) 8 { 9 PetscErrorCode ierr; 10 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 11 TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING; 12 PetscReal f, fold, gdx, gnorm; 13 PetscReal stepsize = 1.0,delta; 14 15 PetscFunctionBegin; 16 /* Project initial point onto bounds */ 17 ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr); 18 ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr); 19 ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr); 20 21 22 /* Check convergence criteria */ 23 ierr = TaoComputeObjectiveAndGradient(tao, tao->solution,&f,blmP->unprojected_gradient);CHKERRQ(ierr); 24 ierr = VecBoundGradientProjection(blmP->unprojected_gradient,tao->solution, tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr); 25 26 ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr); 27 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN"); 28 29 tao->reason = TAO_CONTINUE_ITERATING; 30 ierr = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 31 ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr); 32 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 33 if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0); 34 35 /* Set initial scaling for the function */ 36 if (f != 0.0) { 37 delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm); 38 } else { 39 delta = 2.0 / (gnorm*gnorm); 40 } 41 ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr); 42 ierr = MatLMVMReset(blmP->M);CHKERRQ(ierr); 43 44 /* Set counter for gradient/reset steps */ 45 blmP->grad = 0; 46 blmP->reset = 0; 47 48 /* Have not converged; continue with Newton method */ 49 while (tao->reason == TAO_CONTINUE_ITERATING) { 50 /* Compute direction */ 51 ierr = MatLMVMUpdate(blmP->M, tao->solution, tao->gradient);CHKERRQ(ierr); 52 ierr = MatLMVMSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr); 53 ierr = VecBoundGradientProjection(tao->stepdirection,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr); 54 55 /* Check for success (descent direction) */ 56 ierr = VecDot(blmP->unprojected_gradient, tao->gradient, &gdx);CHKERRQ(ierr); 57 if (gdx <= 0) { 58 /* Step is not descent or solve was not successful 59 Use steepest descent direction (scaled) */ 60 ++blmP->grad; 61 62 if (f != 0.0) { 63 delta = 2.0*PetscAbsScalar(f) / (gnorm*gnorm); 64 } else { 65 delta = 2.0 / (gnorm*gnorm); 66 } 67 ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr); 68 ierr = MatLMVMReset(blmP->M);CHKERRQ(ierr); 69 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr); 70 ierr = MatLMVMSolve(blmP->M,blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr); 71 } 72 ierr = VecScale(tao->stepdirection,-1.0);CHKERRQ(ierr); 73 74 /* Perform the linesearch */ 75 fold = f; 76 ierr = VecCopy(tao->solution, blmP->Xold);CHKERRQ(ierr); 77 ierr = VecCopy(blmP->unprojected_gradient, blmP->Gold);CHKERRQ(ierr); 78 ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr); 79 ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr); 80 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr); 81 82 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) { 83 /* Linesearch failed 84 Reset factors and use scaled (projected) gradient step */ 85 ++blmP->reset; 86 87 f = fold; 88 ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr); 89 ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr); 90 91 if (f != 0.0) { 92 delta = 2.0* PetscAbsScalar(f) / (gnorm*gnorm); 93 } else { 94 delta = 2.0/ (gnorm*gnorm); 95 } 96 ierr = MatLMVMSetDelta(blmP->M,delta);CHKERRQ(ierr); 97 ierr = MatLMVMReset(blmP->M);CHKERRQ(ierr); 98 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr); 99 ierr = MatLMVMSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr); 100 ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr); 101 102 /* This may be incorrect; linesearch has values for stepmax and stepmin 103 that should be reset. */ 104 ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr); 105 ierr = TaoLineSearchApply(tao->linesearch,tao->solution,&f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr); 106 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr); 107 108 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) { 109 tao->reason = TAO_DIVERGED_LS_FAILURE; 110 break; 111 } 112 } 113 114 /* Check for converged */ 115 ierr = VecBoundGradientProjection(blmP->unprojected_gradient, tao->solution, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr); 116 ierr = TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm);CHKERRQ(ierr); 117 118 119 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Not-a-Number"); 120 tao->niter++; 121 ierr = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 122 ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr); 123 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 124 } 125 PetscFunctionReturn(0); 126 } 127 128 static PetscErrorCode TaoSetup_BLMVM(Tao tao) 129 { 130 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 131 PetscInt n,N; 132 PetscErrorCode ierr; 133 KSP H0ksp; 134 135 PetscFunctionBegin; 136 /* Existence of tao->solution checked in TaoSetup() */ 137 ierr = VecDuplicate(tao->solution,&blmP->Xold);CHKERRQ(ierr); 138 ierr = VecDuplicate(tao->solution,&blmP->Gold);CHKERRQ(ierr); 139 ierr = VecDuplicate(tao->solution, &blmP->unprojected_gradient);CHKERRQ(ierr); 140 141 if (!tao->stepdirection) { 142 ierr = VecDuplicate(tao->solution, &tao->stepdirection);CHKERRQ(ierr); 143 } 144 if (!tao->gradient) { 145 ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr); 146 } 147 if (!tao->XL) { 148 ierr = VecDuplicate(tao->solution,&tao->XL);CHKERRQ(ierr); 149 ierr = VecSet(tao->XL,PETSC_NINFINITY);CHKERRQ(ierr); 150 } 151 if (!tao->XU) { 152 ierr = VecDuplicate(tao->solution,&tao->XU);CHKERRQ(ierr); 153 ierr = VecSet(tao->XU,PETSC_INFINITY);CHKERRQ(ierr); 154 } 155 /* Create matrix for the limited memory approximation */ 156 ierr = VecGetLocalSize(tao->solution,&n);CHKERRQ(ierr); 157 ierr = VecGetSize(tao->solution,&N);CHKERRQ(ierr); 158 ierr = MatCreateLMVM(((PetscObject)tao)->comm,n,N,&blmP->M);CHKERRQ(ierr); 159 ierr = MatLMVMAllocateVectors(blmP->M,tao->solution);CHKERRQ(ierr); 160 161 /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */ 162 if (blmP->H0) { 163 const char *prefix; 164 ierr = MatLMVMSetH0(blmP->M, blmP->H0);CHKERRQ(ierr); 165 ierr = MatLMVMGetH0KSP(blmP->M, &H0ksp);CHKERRQ(ierr); 166 167 ierr = TaoGetOptionsPrefix(tao, &prefix);CHKERRQ(ierr); 168 ierr = KSPSetOptionsPrefix(H0ksp, prefix);CHKERRQ(ierr); 169 ierr = KSPAppendOptionsPrefix(H0ksp, "tao_h0_");CHKERRQ(ierr); 170 ierr = KSPSetFromOptions(H0ksp);CHKERRQ(ierr); 171 ierr = KSPSetUp(H0ksp);CHKERRQ(ierr); 172 } 173 PetscFunctionReturn(0); 174 } 175 176 /* ---------------------------------------------------------- */ 177 static PetscErrorCode TaoDestroy_BLMVM(Tao tao) 178 { 179 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 180 PetscErrorCode ierr; 181 182 PetscFunctionBegin; 183 if (tao->setupcalled) { 184 ierr = MatDestroy(&blmP->M);CHKERRQ(ierr); 185 ierr = VecDestroy(&blmP->unprojected_gradient);CHKERRQ(ierr); 186 ierr = VecDestroy(&blmP->Xold);CHKERRQ(ierr); 187 ierr = VecDestroy(&blmP->Gold);CHKERRQ(ierr); 188 } 189 190 if (blmP->H0) { 191 PetscObjectDereference((PetscObject)blmP->H0); 192 } 193 194 ierr = PetscFree(tao->data);CHKERRQ(ierr); 195 PetscFunctionReturn(0); 196 } 197 198 /*------------------------------------------------------------*/ 199 static PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptionItems* PetscOptionsObject,Tao tao) 200 { 201 PetscErrorCode ierr; 202 203 PetscFunctionBegin; 204 ierr = PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for bound constrained optimization");CHKERRQ(ierr); 205 ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr); 206 ierr = PetscOptionsTail();CHKERRQ(ierr); 207 PetscFunctionReturn(0); 208 } 209 210 211 /*------------------------------------------------------------*/ 212 static int TaoView_BLMVM(Tao tao, PetscViewer viewer) 213 { 214 TAO_BLMVM *lmP = (TAO_BLMVM *)tao->data; 215 PetscBool isascii; 216 PetscErrorCode ierr; 217 218 PetscFunctionBegin; 219 ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr); 220 if (isascii) { 221 ierr = PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lmP->grad);CHKERRQ(ierr); 222 } 223 PetscFunctionReturn(0); 224 } 225 226 static PetscErrorCode TaoComputeDual_BLMVM(Tao tao, Vec DXL, Vec DXU) 227 { 228 TAO_BLMVM *blm = (TAO_BLMVM *) tao->data; 229 PetscErrorCode ierr; 230 231 PetscFunctionBegin; 232 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 233 PetscValidHeaderSpecific(DXL,VEC_CLASSID,2); 234 PetscValidHeaderSpecific(DXU,VEC_CLASSID,3); 235 if (!tao->gradient || !blm->unprojected_gradient) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Dual variables don't exist yet or no longer exist.\n"); 236 237 ierr = VecCopy(tao->gradient,DXL);CHKERRQ(ierr); 238 ierr = VecAXPY(DXL,-1.0,blm->unprojected_gradient);CHKERRQ(ierr); 239 ierr = VecSet(DXU,0.0);CHKERRQ(ierr); 240 ierr = VecPointwiseMax(DXL,DXL,DXU);CHKERRQ(ierr); 241 242 ierr = VecCopy(blm->unprojected_gradient,DXU);CHKERRQ(ierr); 243 ierr = VecAXPY(DXU,-1.0,tao->gradient);CHKERRQ(ierr); 244 ierr = VecAXPY(DXU,1.0,DXL);CHKERRQ(ierr); 245 PetscFunctionReturn(0); 246 } 247 248 /* ---------------------------------------------------------- */ 249 /*MC 250 TAOBLMVM - Bounded limited memory variable metric is a quasi-Newton method 251 for nonlinear minimization with bound constraints. It is an extension 252 of TAOLMVM 253 254 Options Database Keys: 255 + -tao_lmm_vectors - number of vectors to use for approximation 256 . -tao_lmm_scale_type - "none","scalar","broyden" 257 . -tao_lmm_limit_type - "none","average","relative","absolute" 258 . -tao_lmm_rescale_type - "none","scalar","gl" 259 . -tao_lmm_limit_mu - mu limiting factor 260 . -tao_lmm_limit_nu - nu limiting factor 261 . -tao_lmm_delta_min - minimum delta value 262 . -tao_lmm_delta_max - maximum delta value 263 . -tao_lmm_broyden_phi - phi factor for Broyden scaling 264 . -tao_lmm_scalar_alpha - alpha factor for scalar scaling 265 . -tao_lmm_rescale_alpha - alpha factor for rescaling diagonal 266 . -tao_lmm_rescale_beta - beta factor for rescaling diagonal 267 . -tao_lmm_scalar_history - amount of history for scalar scaling 268 . -tao_lmm_rescale_history - amount of history for rescaling diagonal 269 - -tao_lmm_eps - rejection tolerance 270 271 Level: beginner 272 M*/ 273 PETSC_EXTERN PetscErrorCode TaoCreate_BLMVM(Tao tao) 274 { 275 TAO_BLMVM *blmP; 276 const char *morethuente_type = TAOLINESEARCHMT; 277 PetscErrorCode ierr; 278 279 PetscFunctionBegin; 280 tao->ops->setup = TaoSetup_BLMVM; 281 tao->ops->solve = TaoSolve_BLMVM; 282 tao->ops->view = TaoView_BLMVM; 283 tao->ops->setfromoptions = TaoSetFromOptions_BLMVM; 284 tao->ops->destroy = TaoDestroy_BLMVM; 285 tao->ops->computedual = TaoComputeDual_BLMVM; 286 287 ierr = PetscNewLog(tao,&blmP);CHKERRQ(ierr); 288 blmP->H0 = NULL; 289 tao->data = (void*)blmP; 290 291 /* Override default settings (unless already changed) */ 292 if (!tao->max_it_changed) tao->max_it = 2000; 293 if (!tao->max_funcs_changed) tao->max_funcs = 4000; 294 295 ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr); 296 ierr = PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);CHKERRQ(ierr); 297 ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr); 298 ierr = TaoLineSearchUseTaoRoutines(tao->linesearch,tao);CHKERRQ(ierr); 299 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr); 300 PetscFunctionReturn(0); 301 } 302 303 PETSC_EXTERN PetscErrorCode TaoLMVMSetH0(Tao tao, Mat H0) 304 { 305 TAO_LMVM *lmP; 306 TAO_BLMVM *blmP; 307 const TaoType type; 308 PetscBool is_lmvm, is_blmvm; 309 PetscErrorCode ierr; 310 311 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 312 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 313 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 314 315 if (is_lmvm) { 316 lmP = (TAO_LMVM *)tao->data; 317 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr); 318 lmP->H0 = H0; 319 } else if (is_blmvm) { 320 blmP = (TAO_BLMVM *)tao->data; 321 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr); 322 blmP->H0 = H0; 323 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 324 PetscFunctionReturn(0); 325 } 326 327 PETSC_EXTERN PetscErrorCode TaoLMVMGetH0(Tao tao, Mat *H0) 328 { 329 TAO_LMVM *lmP; 330 TAO_BLMVM *blmP; 331 const TaoType type; 332 PetscBool is_lmvm, is_blmvm; 333 Mat M; 334 335 PetscErrorCode ierr; 336 337 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 338 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 339 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 340 341 if (is_lmvm) { 342 lmP = (TAO_LMVM *)tao->data; 343 M = lmP->M; 344 } else if (is_blmvm) { 345 blmP = (TAO_BLMVM *)tao->data; 346 M = blmP->M; 347 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 348 ierr = MatLMVMGetH0(M, H0);CHKERRQ(ierr); 349 PetscFunctionReturn(0); 350 } 351 352 PETSC_EXTERN PetscErrorCode TaoLMVMGetH0KSP(Tao tao, KSP *ksp) 353 { 354 TAO_LMVM *lmP; 355 TAO_BLMVM *blmP; 356 const TaoType type; 357 PetscBool is_lmvm, is_blmvm; 358 Mat M; 359 PetscErrorCode ierr; 360 361 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 362 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 363 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 364 365 if (is_lmvm) { 366 lmP = (TAO_LMVM *)tao->data; 367 M = lmP->M; 368 } else if (is_blmvm) { 369 blmP = (TAO_BLMVM *)tao->data; 370 M = blmP->M; 371 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 372 ierr = MatLMVMGetH0KSP(M, ksp);CHKERRQ(ierr); 373 PetscFunctionReturn(0); 374 } 375