1 #include <petsctaolinesearch.h> 2 #include <../src/tao/unconstrained/impls/lmvm/lmvm.h> 3 #include <../src/tao/bound/impls/blmvm/blmvm.h> 4 5 #define BLMVM_STEP_BFGS 0 6 #define BLMVM_STEP_GRAD 1 7 8 #define BLMVM_AS_NONE 0 9 #define BLMVM_AS_BERTSEKAS 1 10 #define BLMVM_AS_SIZE 2 11 12 static const char *BLMVM_AS_TYPE[64] = {"none", "bertsekas"}; 13 14 PETSC_INTERN PetscErrorCode TaoBLMVMEstimateActiveSet(Tao tao, PetscInt asType) 15 { 16 PetscErrorCode ierr; 17 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 18 19 PetscFunctionBegin; 20 if (!tao->bounded) PetscFunctionReturn(0); 21 switch (asType) { 22 case BLMVM_AS_NONE: 23 ierr = ISDestroy(&blmP->inactive_idx);CHKERRQ(ierr); 24 ierr = VecWhichInactive(tao->XL, tao->solution, blmP->unprojected_gradient, tao->XU, PETSC_TRUE, &blmP->inactive_idx);CHKERRQ(ierr); 25 ierr = ISDestroy(&blmP->active_idx);CHKERRQ(ierr); 26 ierr = ISComplementVec(blmP->inactive_idx, tao->solution, &blmP->active_idx);CHKERRQ(ierr); 27 break; 28 29 case BLMVM_AS_BERTSEKAS: 30 /* Use gradient descent to estimate the active set */ 31 ierr = VecCopy(blmP->unprojected_gradient, blmP->W);CHKERRQ(ierr); 32 ierr = VecScale(blmP->W, -1.0);CHKERRQ(ierr); 33 ierr = TaoEstimateActiveBounds(tao->solution, tao->XL, tao->XU, blmP->unprojected_gradient, blmP->W, blmP->work, blmP->as_step, &blmP->as_tol, 34 &blmP->active_lower, &blmP->active_upper, &blmP->active_fixed, &blmP->active_idx, &blmP->inactive_idx);CHKERRQ(ierr); 35 break; 36 37 default: 38 break; 39 } 40 PetscFunctionReturn(0); 41 } 42 43 PETSC_INTERN PetscErrorCode TaoBLMVMBoundStep(Tao tao, PetscInt asType, Vec step) 44 { 45 PetscErrorCode ierr; 46 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 47 48 PetscFunctionBegin; 49 if (!tao->bounded) PetscFunctionReturn(0); 50 switch (asType) { 51 case BLMVM_AS_NONE: 52 ierr = VecISSet(step, blmP->active_idx, 0.0);CHKERRQ(ierr); 53 break; 54 55 case BLMVM_AS_BERTSEKAS: 56 ierr = TaoBoundStep(tao->solution, tao->XL, tao->XU, blmP->active_lower, blmP->active_upper, blmP->active_fixed, 1.0, step);CHKERRQ(ierr); 57 break; 58 59 default: 60 break; 61 } 62 PetscFunctionReturn(0); 63 } 64 65 /*------------------------------------------------------------*/ 66 PETSC_INTERN PetscErrorCode TaoSolve_BLMVM(Tao tao) 67 { 68 PetscErrorCode ierr; 69 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 70 TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING; 71 PetscReal f, fold, gdx, gnorm, resnorm; 72 PetscReal stepsize = 1.0; 73 PetscInt nDiff, nupdates, stepType = BLMVM_STEP_GRAD; 74 75 PetscFunctionBegin; 76 /* Project initial point onto bounds */ 77 ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr); 78 ierr = TaoBoundSolution(tao->solution, tao->XL,tao->XU, 0.0, &nDiff, tao->solution);CHKERRQ(ierr); 79 if (tao->bounded) { 80 ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr); 81 } 82 83 /* Check convergence criteria */ 84 ierr = TaoComputeObjectiveAndGradient(tao, tao->solution,&f,blmP->unprojected_gradient);CHKERRQ(ierr); 85 ierr = VecBoundGradientProjection(blmP->unprojected_gradient,tao->solution, tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr); 86 87 ierr = TaoGradientNorm(tao, blmP->unprojected_gradient,NORM_2,&gnorm);CHKERRQ(ierr); 88 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN"); 89 90 tao->reason = TAO_CONTINUE_ITERATING; 91 ierr = VecFischer(tao->solution, blmP->unprojected_gradient, tao->XL, tao->XU, blmP->W);CHKERRQ(ierr); 92 ierr = VecNorm(blmP->W, NORM_2, &resnorm);CHKERRQ(ierr); 93 ierr = TaoLogConvergenceHistory(tao,f,resnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 94 ierr = TaoMonitor(tao,tao->niter,f,resnorm,0.0,stepsize);CHKERRQ(ierr); 95 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 96 if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0); 97 98 /* Set counter for gradient/reset steps */ 99 if (!blmP->recycle) { 100 blmP->bfgs = 0; 101 blmP->grad = 0; 102 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr); 103 } 104 105 /* Have not converged; continue with Newton method */ 106 while (tao->reason == TAO_CONTINUE_ITERATING) { 107 /* Estimate active set at the current iterate */ 108 ierr = TaoBLMVMEstimateActiveSet(tao, blmP->as_type);CHKERRQ(ierr); 109 110 /* Compute direction */ 111 if (blmP->H0) { 112 ierr = MatLMVMSetJ0(blmP->M, blmP->H0);CHKERRQ(ierr); 113 stepType = BLMVM_STEP_BFGS; 114 } 115 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr); 116 ierr = VecCopy(blmP->unprojected_gradient, blmP->red_grad);CHKERRQ(ierr); 117 ierr = VecISSet(blmP->red_grad, blmP->active_idx, 0.0);CHKERRQ(ierr); 118 ierr = MatSolve(blmP->M, blmP->red_grad, tao->stepdirection);CHKERRQ(ierr); 119 ierr = VecScale(tao->stepdirection,-1.0);CHKERRQ(ierr); 120 ierr = TaoBLMVMBoundStep(tao, blmP->as_type, tao->stepdirection);CHKERRQ(ierr); 121 ierr = MatLMVMGetUpdateCount(blmP->M, &nupdates);CHKERRQ(ierr); 122 if (nupdates > 0) stepType = BLMVM_STEP_BFGS; 123 124 /* Check for success (descent direction) */ 125 ierr = VecDot(tao->stepdirection, blmP->red_grad, &gdx);CHKERRQ(ierr); 126 if (gdx >= 0 || PetscIsInfOrNanReal(gdx)) { 127 /* Step is not descent or solve was not successful 128 Use steepest descent direction (scaled) */ 129 ierr = MatLMVMResetJ0(blmP->M);CHKERRQ(ierr); 130 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr); 131 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr); 132 ierr = MatSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr); 133 ierr = VecScale(tao->stepdirection,-1.0);CHKERRQ(ierr); 134 ierr = TaoBLMVMBoundStep(tao, blmP->as_type, tao->stepdirection);CHKERRQ(ierr); 135 stepType = BLMVM_STEP_GRAD; 136 } 137 138 /* Perform the linesearch */ 139 fold = f; 140 ierr = VecCopy(tao->solution, blmP->Xold);CHKERRQ(ierr); 141 ierr = VecCopy(blmP->unprojected_gradient, blmP->Gold);CHKERRQ(ierr); 142 ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr); 143 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr); 144 145 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER && stepType != BLMVM_STEP_GRAD) { 146 /* Linesearch failed 147 Reset factors and use scaled (projected) gradient step */ 148 f = fold; 149 ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr); 150 ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr); 151 152 ierr = MatLMVMResetJ0(blmP->M);CHKERRQ(ierr); 153 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr); 154 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr); 155 ierr = MatSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr); 156 ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr); 157 ierr = TaoBLMVMBoundStep(tao, blmP->as_type, tao->stepdirection);CHKERRQ(ierr); 158 stepType = BLMVM_STEP_GRAD; 159 160 ierr = TaoLineSearchApply(tao->linesearch,tao->solution,&f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr); 161 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr); 162 } 163 164 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) { 165 /* Line search failed on a gradient step, so just mark reason for divergence */ 166 f = fold; 167 ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr); 168 ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr); 169 tao->reason = TAO_DIVERGED_LS_FAILURE; 170 } else { 171 /* LS found valid step, so tally the step type and compute projected gradient */ 172 switch (stepType) { 173 case BLMVM_STEP_BFGS: 174 ++blmP->bfgs; 175 break; 176 case BLMVM_STEP_GRAD: 177 ++blmP->grad; 178 break; 179 default: 180 break; 181 } 182 ierr = VecBoundGradientProjection(blmP->unprojected_gradient, tao->solution, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr); 183 ierr = TaoGradientNorm(tao, blmP->unprojected_gradient,NORM_2,&gnorm);CHKERRQ(ierr); 184 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Not-a-Number"); 185 } 186 187 /* Check for converged */ 188 tao->niter++; 189 ierr = VecFischer(tao->solution, blmP->unprojected_gradient, tao->XL, tao->XU, blmP->W);CHKERRQ(ierr); 190 ierr = VecNorm(blmP->W, NORM_2, &resnorm);CHKERRQ(ierr); 191 ierr = TaoLogConvergenceHistory(tao,f,resnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 192 ierr = TaoMonitor(tao,tao->niter,f,resnorm,0.0,stepsize);CHKERRQ(ierr); 193 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 194 } 195 PetscFunctionReturn(0); 196 } 197 198 PETSC_INTERN PetscErrorCode TaoSetup_BLMVM(Tao tao) 199 { 200 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 201 PetscInt n,N; 202 PetscErrorCode ierr; 203 204 PetscFunctionBegin; 205 /* Existence of tao->solution checked in TaoSetup() */ 206 ierr = VecDuplicate(tao->solution,&blmP->Xold);CHKERRQ(ierr); 207 ierr = VecDuplicate(tao->solution,&blmP->Gold);CHKERRQ(ierr); 208 ierr = VecDuplicate(tao->solution, &blmP->unprojected_gradient);CHKERRQ(ierr); 209 ierr = VecDuplicate(tao->solution, &blmP->W);CHKERRQ(ierr); 210 ierr = VecDuplicate(tao->solution, &blmP->work);CHKERRQ(ierr); 211 ierr = VecDuplicate(tao->solution, &blmP->red_grad);CHKERRQ(ierr); 212 213 if (!tao->stepdirection) { 214 ierr = VecDuplicate(tao->solution, &tao->stepdirection);CHKERRQ(ierr); 215 } 216 if (!tao->gradient) { 217 ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr); 218 } 219 /* Create matrix for the limited memory approximation */ 220 ierr = VecGetLocalSize(tao->solution,&n);CHKERRQ(ierr); 221 ierr = VecGetSize(tao->solution,&N);CHKERRQ(ierr); 222 ierr = MatSetSizes(blmP->M, n, n, N, N);CHKERRQ(ierr); 223 ierr = MatLMVMAllocate(blmP->M,tao->solution,blmP->unprojected_gradient);CHKERRQ(ierr); 224 225 /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */ 226 if (blmP->H0) { 227 ierr = MatLMVMSetJ0(blmP->M, blmP->H0);CHKERRQ(ierr); 228 } 229 PetscFunctionReturn(0); 230 } 231 232 /* ---------------------------------------------------------- */ 233 PETSC_INTERN PetscErrorCode TaoDestroy_BLMVM(Tao tao) 234 { 235 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 236 PetscErrorCode ierr; 237 238 PetscFunctionBegin; 239 if (tao->setupcalled) { 240 ierr = VecDestroy(&blmP->unprojected_gradient);CHKERRQ(ierr); 241 ierr = VecDestroy(&blmP->Xold);CHKERRQ(ierr); 242 ierr = VecDestroy(&blmP->Gold);CHKERRQ(ierr); 243 ierr = VecDestroy(&blmP->W);CHKERRQ(ierr); 244 ierr = VecDestroy(&blmP->work);CHKERRQ(ierr); 245 ierr = VecDestroy(&blmP->red_grad);CHKERRQ(ierr); 246 } 247 ierr = ISDestroy(&blmP->active_lower);CHKERRQ(ierr); 248 ierr = ISDestroy(&blmP->active_upper);CHKERRQ(ierr); 249 ierr = ISDestroy(&blmP->active_fixed);CHKERRQ(ierr); 250 ierr = ISDestroy(&blmP->active_idx);CHKERRQ(ierr); 251 ierr = ISDestroy(&blmP->inactive_idx);CHKERRQ(ierr); 252 ierr = MatDestroy(&blmP->M);CHKERRQ(ierr); 253 if (blmP->H0) { 254 PetscObjectDereference((PetscObject)blmP->H0); 255 } 256 257 ierr = PetscFree(tao->data);CHKERRQ(ierr); 258 PetscFunctionReturn(0); 259 } 260 261 /*------------------------------------------------------------*/ 262 PETSC_INTERN PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptionItems* PetscOptionsObject,Tao tao) 263 { 264 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data; 265 PetscErrorCode ierr; 266 267 PetscFunctionBegin; 268 ierr = PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for bound constrained optimization");CHKERRQ(ierr); 269 ierr = PetscOptionsBool("-tao_blmvm_recycle","enable recycling of the BFGS matrix between subsequent TaoSolve() calls","",blmP->recycle,&blmP->recycle,NULL);CHKERRQ(ierr); 270 ierr = PetscOptionsEList("-tao_blmvm_as_type","active set estimation method", "", BLMVM_AS_TYPE, BLMVM_AS_SIZE, BLMVM_AS_TYPE[blmP->as_type], &blmP->as_type,NULL);CHKERRQ(ierr); 271 ierr = PetscOptionsReal("-tao_blmvm_as_tol", "initial tolerance used when estimating actively bounded variables","",blmP->as_tol,&blmP->as_tol,NULL);CHKERRQ(ierr); 272 ierr = PetscOptionsReal("-tao_blmvm_as_step", "step length used when estimating actively bounded variables","",blmP->as_step,&blmP->as_step,NULL);CHKERRQ(ierr); 273 ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr); 274 ierr = MatSetFromOptions(blmP->M);CHKERRQ(ierr); 275 ierr = PetscOptionsTail();CHKERRQ(ierr); 276 PetscFunctionReturn(0); 277 } 278 279 280 /*------------------------------------------------------------*/ 281 PETSC_INTERN PetscErrorCode TaoView_BLMVM(Tao tao, PetscViewer viewer) 282 { 283 TAO_BLMVM *lmP = (TAO_BLMVM *)tao->data; 284 PetscBool isascii; 285 PetscErrorCode ierr; 286 PetscInt recycled_its; 287 288 PetscFunctionBegin; 289 ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr); 290 if (isascii) { 291 ierr = PetscViewerASCIIPrintf(viewer, " Gradient steps: %D\n", lmP->grad);CHKERRQ(ierr); 292 if (lmP->recycle) { 293 ierr = PetscViewerASCIIPrintf(viewer, " Recycle: on\n");CHKERRQ(ierr); 294 recycled_its = lmP->bfgs + lmP->grad; 295 ierr = PetscViewerASCIIPrintf(viewer, " Total recycled iterations: %D\n", recycled_its);CHKERRQ(ierr); 296 } 297 ierr = PetscViewerPushFormat(viewer, PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr); 298 ierr = MatView(lmP->M, viewer);CHKERRQ(ierr); 299 ierr = PetscViewerPopFormat(viewer);CHKERRQ(ierr); 300 } 301 PetscFunctionReturn(0); 302 } 303 304 PETSC_INTERN PetscErrorCode TaoComputeDual_BLMVM(Tao tao, Vec DXL, Vec DXU) 305 { 306 TAO_BLMVM *blm = (TAO_BLMVM *) tao->data; 307 PetscErrorCode ierr; 308 309 PetscFunctionBegin; 310 PetscValidHeaderSpecific(tao,TAO_CLASSID,1); 311 PetscValidHeaderSpecific(DXL,VEC_CLASSID,2); 312 PetscValidHeaderSpecific(DXU,VEC_CLASSID,3); 313 if (!tao->gradient || !blm->unprojected_gradient) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Dual variables don't exist yet or no longer exist.\n"); 314 315 ierr = VecCopy(tao->gradient,DXL);CHKERRQ(ierr); 316 ierr = VecAXPY(DXL,-1.0,blm->unprojected_gradient);CHKERRQ(ierr); 317 ierr = VecSet(DXU,0.0);CHKERRQ(ierr); 318 ierr = VecPointwiseMax(DXL,DXL,DXU);CHKERRQ(ierr); 319 320 ierr = VecCopy(blm->unprojected_gradient,DXU);CHKERRQ(ierr); 321 ierr = VecAXPY(DXU,-1.0,tao->gradient);CHKERRQ(ierr); 322 ierr = VecAXPY(DXU,1.0,DXL);CHKERRQ(ierr); 323 PetscFunctionReturn(0); 324 } 325 326 /* ---------------------------------------------------------- */ 327 /*MC 328 TAOBLMVM - Bounded limited memory variable metric is a quasi-Newton method 329 for nonlinear minimization with bound constraints. It is an extension 330 of TAOLMVM 331 332 Level: beginner 333 M*/ 334 PETSC_EXTERN PetscErrorCode TaoCreate_BLMVM(Tao tao) 335 { 336 TAO_BLMVM *blmP; 337 const char *morethuente_type = TAOLINESEARCHMT; 338 PetscErrorCode ierr; 339 340 PetscFunctionBegin; 341 tao->ops->setup = TaoSetup_BLMVM; 342 tao->ops->solve = TaoSolve_BLMVM; 343 tao->ops->view = TaoView_BLMVM; 344 tao->ops->setfromoptions = TaoSetFromOptions_BLMVM; 345 tao->ops->destroy = TaoDestroy_BLMVM; 346 tao->ops->computedual = TaoComputeDual_BLMVM; 347 348 ierr = PetscNewLog(tao,&blmP);CHKERRQ(ierr); 349 blmP->H0 = NULL; 350 blmP->as_step = 0.001; 351 blmP->as_tol = 0.001; 352 blmP->as_type = BLMVM_AS_BERTSEKAS; 353 blmP->recycle = PETSC_FALSE; 354 355 tao->data = (void*)blmP; 356 357 /* Override default settings (unless already changed) */ 358 if (!tao->max_it_changed) tao->max_it = 2000; 359 if (!tao->max_funcs_changed) tao->max_funcs = 4000; 360 361 ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr); 362 ierr = PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);CHKERRQ(ierr); 363 ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr); 364 ierr = TaoLineSearchUseTaoRoutines(tao->linesearch,tao);CHKERRQ(ierr); 365 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr); 366 367 ierr = KSPInitializePackage();CHKERRQ(ierr); 368 ierr = MatCreate(((PetscObject)tao)->comm, &blmP->M);CHKERRQ(ierr); 369 ierr = PetscObjectIncrementTabLevel((PetscObject)blmP->M, (PetscObject)tao, 1);CHKERRQ(ierr); 370 ierr = MatSetType(blmP->M, MATLMVMBFGS);CHKERRQ(ierr); 371 ierr = MatSetOptionsPrefix(blmP->M, "tao_blmvm_");CHKERRQ(ierr); 372 PetscFunctionReturn(0); 373 } 374 375 PETSC_EXTERN PetscErrorCode TaoBLMVMSetH0(Tao tao, Mat H0) 376 { 377 TAO_LMVM *lmP; 378 TAO_BLMVM *blmP; 379 TaoType type; 380 PetscBool is_lmvm, is_blmvm; 381 PetscErrorCode ierr; 382 383 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 384 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 385 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 386 387 if (is_lmvm) { 388 lmP = (TAO_LMVM *)tao->data; 389 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr); 390 lmP->H0 = H0; 391 } else if (is_blmvm) { 392 blmP = (TAO_BLMVM *)tao->data; 393 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr); 394 blmP->H0 = H0; 395 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 396 PetscFunctionReturn(0); 397 } 398 399 PETSC_EXTERN PetscErrorCode TaoBLMVMGetH0(Tao tao, Mat *H0) 400 { 401 TAO_LMVM *lmP; 402 TAO_BLMVM *blmP; 403 TaoType type; 404 PetscBool is_lmvm, is_blmvm; 405 Mat M; 406 407 PetscErrorCode ierr; 408 409 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 410 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 411 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 412 413 if (is_lmvm) { 414 lmP = (TAO_LMVM *)tao->data; 415 M = lmP->M; 416 } else if (is_blmvm) { 417 blmP = (TAO_BLMVM *)tao->data; 418 M = blmP->M; 419 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 420 ierr = MatLMVMGetJ0(M, H0);CHKERRQ(ierr); 421 PetscFunctionReturn(0); 422 } 423 424 PETSC_EXTERN PetscErrorCode TaoBLMVMGetH0KSP(Tao tao, KSP *ksp) 425 { 426 TAO_LMVM *lmP; 427 TAO_BLMVM *blmP; 428 TaoType type; 429 PetscBool is_lmvm, is_blmvm; 430 Mat M; 431 PetscErrorCode ierr; 432 433 ierr = TaoGetType(tao, &type);CHKERRQ(ierr); 434 ierr = PetscStrcmp(type, TAOLMVM, &is_lmvm);CHKERRQ(ierr); 435 ierr = PetscStrcmp(type, TAOBLMVM, &is_blmvm);CHKERRQ(ierr); 436 437 if (is_lmvm) { 438 lmP = (TAO_LMVM *)tao->data; 439 M = lmP->M; 440 } else if (is_blmvm) { 441 blmP = (TAO_BLMVM *)tao->data; 442 M = blmP->M; 443 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONGSTATE, "This routine applies to TAO_LMVM and TAO_BLMVM."); 444 ierr = MatLMVMGetJ0KSP(M, ksp);CHKERRQ(ierr); 445 PetscFunctionReturn(0); 446 } 447