1 #include <../src/tao/bound/impls/bqnk/bqnk.h> 2 3 static const char *BNK_AS[64] = {"none", "bertsekas"}; 4 5 static PetscErrorCode TaoBQNLSComputeHessian(Tao tao) 6 { 7 TAO_BNK *bnk = (TAO_BNK *)tao->data; 8 TAO_BQNK *bqnk = (TAO_BQNK *)bnk->ctx; 9 PetscReal gnorm2, delta; 10 11 PetscFunctionBegin; 12 /* Compute the initial scaling and update the approximation */ 13 gnorm2 = bnk->gnorm * bnk->gnorm; 14 if (gnorm2 == 0.0) gnorm2 = PETSC_MACHINE_EPSILON; 15 if (bnk->f == 0.0) delta = 2.0 / gnorm2; 16 else delta = 2.0 * PetscAbsScalar(bnk->f) / gnorm2; 17 PetscCall(MatLMVMSymBroydenSetDelta(bqnk->B, delta)); 18 PetscCall(MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient)); 19 PetscFunctionReturn(PETSC_SUCCESS); 20 } 21 22 static PetscErrorCode TaoBQNLSComputeStep(Tao tao, PetscBool shift, KSPConvergedReason *ksp_reason, PetscInt *step_type) 23 { 24 TAO_BNK *bnk = (TAO_BNK *)tao->data; 25 TAO_BQNK *bqnk = (TAO_BQNK *)bnk->ctx; 26 PetscInt nupdates; 27 28 PetscFunctionBegin; 29 PetscCall(MatSolve(bqnk->B, tao->gradient, tao->stepdirection)); 30 PetscCall(VecScale(tao->stepdirection, -1.0)); 31 PetscCall(TaoBNKBoundStep(tao, bnk->as_type, tao->stepdirection)); 32 *ksp_reason = KSP_CONVERGED_ATOL; 33 PetscCall(MatLMVMGetUpdateCount(bqnk->B, &nupdates)); 34 if (nupdates == 0) *step_type = BNK_SCALED_GRADIENT; 35 else *step_type = BNK_BFGS; 36 PetscFunctionReturn(PETSC_SUCCESS); 37 } 38 39 static PetscErrorCode TaoSetFromOptions_BQNLS(Tao tao, PetscOptionItems PetscOptionsObject) 40 { 41 TAO_BNK *bnk = (TAO_BNK *)tao->data; 42 TAO_BQNK *bqnk = (TAO_BQNK *)bnk->ctx; 43 PetscBool is_set, is_spd; 44 45 PetscFunctionBegin; 46 PetscOptionsHeadBegin(PetscOptionsObject, "Quasi-Newton-Krylov method for bound constrained optimization"); 47 PetscCall(PetscOptionsEList("-tao_bnk_as_type", "active set estimation method", "", BNK_AS, BNK_AS_TYPES, BNK_AS[bnk->as_type], &bnk->as_type, NULL)); 48 PetscCall(PetscOptionsReal("-tao_bnk_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon, NULL)); 49 PetscCall(PetscOptionsReal("-tao_bnk_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol, NULL)); 50 PetscCall(PetscOptionsReal("-tao_bnk_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step, NULL)); 51 PetscCall(PetscOptionsInt("-tao_bnk_max_cg_its", "number of BNCG iterations to take for each Newton step", "", bnk->max_cg_its, &bnk->max_cg_its, NULL)); 52 PetscOptionsHeadEnd(); 53 54 PetscCall(TaoSetOptionsPrefix(bnk->bncg, ((PetscObject)tao)->prefix)); 55 PetscCall(TaoAppendOptionsPrefix(bnk->bncg, "tao_bnk_")); 56 PetscCall(TaoSetFromOptions(bnk->bncg)); 57 58 PetscCall(MatSetOptionsPrefix(bqnk->B, ((PetscObject)tao)->prefix)); 59 PetscCall(MatAppendOptionsPrefix(bqnk->B, "tao_bqnls_")); 60 PetscCall(MatSetFromOptions(bqnk->B)); 61 PetscCall(MatIsSPDKnown(bqnk->B, &is_set, &is_spd)); 62 PetscCheck(is_set && is_spd, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite"); 63 PetscFunctionReturn(PETSC_SUCCESS); 64 } 65 66 /*MC 67 TAOBQNLS - Bounded Quasi-Newton Line Search method for nonlinear minimization with bound 68 constraints. This method approximates the action of the inverse-Hessian with a 69 limited memory quasi-Newton formula. The quasi-Newton matrix and its options are 70 accessible via the prefix `-tao_bqnls_` 71 72 Options Database Keys: 73 + -tao_bnk_max_cg_its - maximum number of bounded conjugate-gradient iterations taken in each Newton loop 74 . -tao_bnk_as_type - active-set estimation method ("none", "bertsekas") 75 . -tao_bnk_epsilon - (developer) tolerance for small pred/actual ratios that trigger automatic step acceptance 76 . -tao_bnk_as_tol - (developer) initial tolerance used in estimating bounded active variables (-as_type bertsekas) 77 - -tao_bnk_as_step - (developer) trial step length used in estimating bounded active variables (-as_type bertsekas) 78 79 Level: beginner 80 81 .seealso: `TAOBNK` 82 M*/ 83 PETSC_EXTERN PetscErrorCode TaoCreate_BQNLS(Tao tao) 84 { 85 TAO_BNK *bnk; 86 TAO_BQNK *bqnk; 87 88 PetscFunctionBegin; 89 PetscCall(TaoCreate_BQNK(tao)); 90 tao->ops->setfromoptions = TaoSetFromOptions_BQNLS; 91 92 bnk = (TAO_BNK *)tao->data; 93 bnk->update_type = BNK_UPDATE_STEP; 94 bnk->computehessian = TaoBQNLSComputeHessian; 95 bnk->computestep = TaoBQNLSComputeStep; 96 97 bqnk = (TAO_BQNK *)bnk->ctx; 98 bqnk->solve = TaoSolve_BNLS; 99 PetscCall(MatSetType(bqnk->B, MATLMVMBFGS)); 100 PetscFunctionReturn(PETSC_SUCCESS); 101 } 102