91   TAO_BNK           *bnk = (TAO_BNK *)tao->data;  in TaoSolve_BNTR()  local
101   PetscCall(TaoBNKInitialize(tao, bnk->init_type, &needH));  in TaoSolve_BNTR()
109       PetscCall(TaoComputeObjective(tao, tao->solution, &bnk->f));  in TaoSolve_BNTR()
112     if (needH && bnk->inactive_idx) {  in TaoSolve_BNTR()
116         tao->reason = bnk->bncg->reason;  in TaoSolve_BNTR()
120       PetscCall((*bnk->computehessian)(tao));  in TaoSolve_BNTR()
125     bnk->fold = bnk->f;  in TaoSolve_BNTR()
126     PetscCall(VecCopy(tao->solution, bnk->Xold));  in TaoSolve_BNTR()
127     PetscCall(VecCopy(tao->gradient, bnk->Gold));  in TaoSolve_BNTR()
128     PetscCall(VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old));  in TaoSolve_BNTR()
136       PetscCall((*bnk->computestep)(tao, shift, &ksp_reason, &stepType));  in TaoSolve_BNTR()
147         PetscCall(VecAXPY(tao->stepdirection, -1.0, bnk->Xold));  in TaoSolve_BNTR()
156       PetscCall(TaoComputeObjective(tao, tao->solution, &bnk->f));  in TaoSolve_BNTR()
157 …PetscCheck(!PetscIsInfOrNanReal(bnk->f), PetscObjectComm((PetscObject)tao), PETSC_ERR_USER, "User …  in TaoSolve_BNTR()
158       actred   = bnk->fold - bnk->f;  in TaoSolve_BNTR()
160 …PetscCall(TaoBNKUpdateTrustRadius(tao, prered, actred, bnk->update_type, stepType, &stepAccepted));  in TaoSolve_BNTR()
166         ++bnk->newt;  in TaoSolve_BNTR()
167         PetscCall(TaoComputeGradient(tao, tao->solution, bnk->unprojected_gradient));  in TaoSolve_BNTR()
168         PetscCall(TaoBNKEstimateActiveSet(tao, bnk->as_type));  in TaoSolve_BNTR()
169         PetscCall(VecCopy(bnk->unprojected_gradient, tao->gradient));  in TaoSolve_BNTR()
170         if (bnk->active_idx) PetscCall(VecISSet(tao->gradient, bnk->active_idx, 0.0));  in TaoSolve_BNTR()
171         PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm));  in TaoSolve_BNTR()
176         bnk->f  = bnk->fold;  in TaoSolve_BNTR()
177         PetscCall(VecCopy(bnk->Xold, tao->solution));  in TaoSolve_BNTR()
178         PetscCall(VecCopy(bnk->Gold, tao->gradient));  in TaoSolve_BNTR()
179         PetscCall(VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient));  in TaoSolve_BNTR()
187     PetscCall(VecFischer(tao->solution, bnk->unprojected_gradient, tao->XL, tao->XU, bnk->W));  in TaoSolve_BNTR()
188     PetscCall(VecNorm(bnk->W, NORM_2, &resnorm));  in TaoSolve_BNTR()
191     PetscCall(TaoLogConvergenceHistory(tao, bnk->f, resnorm, 0.0, tao->ksp_its));  in TaoSolve_BNTR()
192     PetscCall(TaoMonitor(tao, tao->niter, bnk->f, resnorm, 0.0, steplen));  in TaoSolve_BNTR()
213   TAO_BNK *bnk = (TAO_BNK *)tao->data;  in TaoSetFromOptions_BNTR()  local
217   if (bnk->update_type == BNK_UPDATE_STEP) bnk->update_type = BNK_UPDATE_REDUCTION;  in TaoSetFromOptions_BNTR()
234   TAO_BNK *bnk;  in TaoCreate_BNTR()  local
242   bnk              = (TAO_BNK *)tao->data;  in TaoCreate_BNTR()
243 …bnk->update_type = BNK_UPDATE_REDUCTION; /* trust region updates based on predicted/actual reducti…  in TaoCreate_BNTR()