1 #include <../src/tao/bound/impls/bqnk/bqnk.h> 2 3 static PetscErrorCode TaoBQNLSComputeHessian(Tao tao) 4 { 5 TAO_BNK *bnk = (TAO_BNK *)tao->data; 6 TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx; 7 PetscErrorCode ierr; 8 PetscReal gnorm2, delta; 9 10 PetscFunctionBegin; 11 gnorm2 = bnk->gnorm*bnk->gnorm; 12 delta = 2.0 * PetscMax(1.0, PetscAbsScalar(bnk->f)) / PetscMax(gnorm2, PetscPowReal(PETSC_MACHINE_EPSILON, 2.0/3.0)); 13 ierr = MatSymBrdnSetDelta(bqnk->B, delta);CHKERRQ(ierr); 14 ierr = MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr); 15 PetscFunctionReturn(0); 16 } 17 18 static PetscErrorCode TaoBQNLSComputeStep(Tao tao, PetscBool shift, KSPConvergedReason *ksp_reason, PetscInt *step_type) 19 { 20 TAO_BNK *bnk = (TAO_BNK *)tao->data; 21 TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx; 22 PetscErrorCode ierr; 23 PetscInt nupdates; 24 25 PetscFunctionBegin; 26 ierr = MatSolve(bqnk->B, tao->gradient, tao->stepdirection);CHKERRQ(ierr); 27 ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr); 28 ierr = TaoBNKBoundStep(tao, bnk->as_type, tao->stepdirection);CHKERRQ(ierr); 29 *ksp_reason = KSP_CONVERGED_ATOL; 30 ierr = MatLMVMGetUpdateCount(bqnk->B, &nupdates);CHKERRQ(ierr); 31 if (nupdates == 0) { 32 *step_type = BNK_SCALED_GRADIENT; 33 } else { 34 *step_type = BNK_BFGS; 35 } 36 PetscFunctionReturn(0); 37 } 38 39 static PetscErrorCode TaoSetFromOptions_BQNLS(PetscOptionItems *PetscOptionsObject,Tao tao) 40 { 41 TAO_BNK *bnk = (TAO_BNK *)tao->data; 42 TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx; 43 PetscErrorCode ierr; 44 KSPType ksp_type; 45 PetscBool is_spd; 46 47 PetscFunctionBegin; 48 ierr = PetscOptionsHead(PetscOptionsObject,"Quasi-Newton-Krylov method for bound constrained optimization");CHKERRQ(ierr); 49 ierr = PetscOptionsEList("-tao_bqnls_as_type", "active set estimation method", "", BNK_AS, BNK_AS_TYPES, BNK_AS[bnk->as_type], &bnk->as_type, 0);CHKERRQ(ierr); 50 ierr = PetscOptionsReal("-tao_bqnls_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon,NULL);CHKERRQ(ierr); 51 ierr = PetscOptionsReal("-tao_bqnls_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol,NULL);CHKERRQ(ierr); 52 ierr = PetscOptionsReal("-tao_bqnls_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step,NULL);CHKERRQ(ierr); 53 ierr = PetscOptionsInt("-tao_bqnls_max_cg_its", "number of BNCG iterations to take for each Newton step", "", bnk->max_cg_its, &bnk->max_cg_its,NULL);CHKERRQ(ierr); 54 ierr = PetscOptionsTail();CHKERRQ(ierr); 55 ierr = TaoSetFromOptions(bnk->bncg);CHKERRQ(ierr); 56 ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr); 57 ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr); 58 ierr = KSPGetType(tao->ksp,&ksp_type);CHKERRQ(ierr); 59 bnk->is_nash = bnk->is_gltr = bnk->is_stcg = PETSC_FALSE; 60 ierr = MatSetFromOptions(bqnk->B);CHKERRQ(ierr); 61 ierr = MatGetOption(bqnk->B, MAT_SPD, &is_spd);CHKERRQ(ierr); 62 if (!is_spd) SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite"); 63 PetscFunctionReturn(0); 64 } 65 66 PETSC_EXTERN PetscErrorCode TaoCreate_BQNLS(Tao tao) 67 { 68 TAO_BNK *bnk; 69 TAO_BQNK *bqnk; 70 PetscErrorCode ierr; 71 72 PetscFunctionBegin; 73 ierr = TaoCreate_BQNK(tao);CHKERRQ(ierr); 74 ierr = KSPSetOptionsPrefix(tao->ksp, "unused");CHKERRQ(ierr); 75 tao->ops->solve = TaoSolve_BNLS; 76 tao->ops->setfromoptions = TaoSetFromOptions_BQNLS; 77 78 bnk = (TAO_BNK*)tao->data; 79 bnk->update_type = BNK_UPDATE_STEP; 80 bnk->computehessian = TaoBQNLSComputeHessian; 81 bnk->computestep = TaoBQNLSComputeStep; 82 83 bqnk = (TAO_BQNK*)bnk->ctx; 84 ierr = MatSetOptionsPrefix(bqnk->B, "tao_bqnls_");CHKERRQ(ierr); 85 ierr = MatSetType(bqnk->B, MATLMVMBFGS);CHKERRQ(ierr); 86 PetscFunctionReturn(0); 87 }