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 /* Compute the initial scaling and update the approximation */ 12 gnorm2 = bnk->gnorm*bnk->gnorm; 13 if (gnorm2 == 0.0) gnorm2 = PETSC_MACHINE_EPSILON; 14 if (bnk->f == 0.0) { 15 delta = 2.0 / gnorm2; 16 } else { 17 delta = 2.0 * PetscAbsScalar(bnk->f) / gnorm2; 18 } 19 ierr = MatSymBrdnSetDelta(bqnk->B, delta);CHKERRQ(ierr); 20 ierr = MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr); 21 PetscFunctionReturn(0); 22 } 23 24 static PetscErrorCode TaoBQNLSComputeStep(Tao tao, PetscBool shift, KSPConvergedReason *ksp_reason, PetscInt *step_type) 25 { 26 TAO_BNK *bnk = (TAO_BNK *)tao->data; 27 TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx; 28 PetscErrorCode ierr; 29 PetscInt nupdates; 30 31 PetscFunctionBegin; 32 ierr = MatSolve(bqnk->B, tao->gradient, tao->stepdirection);CHKERRQ(ierr); 33 ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr); 34 ierr = TaoBNKBoundStep(tao, bnk->as_type, tao->stepdirection);CHKERRQ(ierr); 35 *ksp_reason = KSP_CONVERGED_ATOL; 36 ierr = MatLMVMGetUpdateCount(bqnk->B, &nupdates);CHKERRQ(ierr); 37 if (nupdates == 0) { 38 *step_type = BNK_SCALED_GRADIENT; 39 } else { 40 *step_type = BNK_BFGS; 41 } 42 PetscFunctionReturn(0); 43 } 44 45 static PetscErrorCode TaoSetFromOptions_BQNLS(PetscOptionItems *PetscOptionsObject,Tao tao) 46 { 47 TAO_BNK *bnk = (TAO_BNK *)tao->data; 48 TAO_BQNK *bqnk = (TAO_BQNK*)bnk->ctx; 49 PetscErrorCode ierr; 50 KSPType ksp_type; 51 PetscBool is_spd; 52 53 PetscFunctionBegin; 54 ierr = PetscOptionsHead(PetscOptionsObject,"Quasi-Newton-Krylov method for bound constrained optimization");CHKERRQ(ierr); 55 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); 56 ierr = PetscOptionsReal("-tao_bqnls_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon,NULL);CHKERRQ(ierr); 57 ierr = PetscOptionsReal("-tao_bqnls_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol,NULL);CHKERRQ(ierr); 58 ierr = PetscOptionsReal("-tao_bqnls_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step,NULL);CHKERRQ(ierr); 59 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); 60 ierr = PetscOptionsTail();CHKERRQ(ierr); 61 ierr = TaoSetFromOptions(bnk->bncg);CHKERRQ(ierr); 62 ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr); 63 ierr = KSPSetFromOptions(tao->ksp);CHKERRQ(ierr); 64 ierr = KSPGetType(tao->ksp,&ksp_type);CHKERRQ(ierr); 65 bnk->is_nash = bnk->is_gltr = bnk->is_stcg = PETSC_FALSE; 66 ierr = MatSetFromOptions(bqnk->B);CHKERRQ(ierr); 67 ierr = MatGetOption(bqnk->B, MAT_SPD, &is_spd);CHKERRQ(ierr); 68 if (!is_spd) SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite"); 69 PetscFunctionReturn(0); 70 } 71 72 PETSC_EXTERN PetscErrorCode TaoCreate_BQNLS(Tao tao) 73 { 74 TAO_BNK *bnk; 75 TAO_BQNK *bqnk; 76 PetscErrorCode ierr; 77 78 PetscFunctionBegin; 79 ierr = TaoCreate_BQNK(tao);CHKERRQ(ierr); 80 ierr = KSPSetOptionsPrefix(tao->ksp, "unused");CHKERRQ(ierr); 81 tao->ops->solve = TaoSolve_BNLS; 82 tao->ops->setfromoptions = TaoSetFromOptions_BQNLS; 83 84 bnk = (TAO_BNK*)tao->data; 85 bnk->update_type = BNK_UPDATE_STEP; 86 bnk->computehessian = TaoBQNLSComputeHessian; 87 bnk->computestep = TaoBQNLSComputeStep; 88 89 bqnk = (TAO_BQNK*)bnk->ctx; 90 ierr = MatSetOptionsPrefix(bqnk->B, "tao_bqnls_");CHKERRQ(ierr); 91 ierr = MatSetType(bqnk->B, MATLMVMBFGS);CHKERRQ(ierr); 92 PetscFunctionReturn(0); 93 }