1 #include <../src/tao/bound/impls/bnk/bnk.h> 2 #include <petscksp.h> 3 4 /* 5 Implements Newton's Method with a line search approach for solving 6 bound constrained minimization problems. A projected More'-Thuente line 7 search is used to guarantee that the bfgs preconditioner remains positive 8 definite. 9 10 The method can shift the Hessian matrix. The shifting procedure is 11 adapted from the PATH algorithm for solving complementarity 12 problems. 13 14 The linear system solve should be done with a conjugate gradient 15 method, although any method can be used. 16 */ 17 18 static PetscErrorCode TaoSolve_BNLS(Tao tao) 19 { 20 PetscErrorCode ierr; 21 TAO_BNK *bnk = (TAO_BNK *)tao->data; 22 TaoLineSearchConvergedReason ls_reason; 23 24 PetscReal f_full, prered, actred; 25 PetscReal steplen = 1.0; 26 27 PetscBool trustAccept; 28 PetscInt stepType; 29 PetscInt bfgsUpdates = 0; 30 31 PetscFunctionBegin; 32 /* Project the current point onto the feasible set */ 33 ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr); 34 ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr); 35 36 /* Project the initial point onto the feasible region */ 37 ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr); 38 39 /* Check convergence criteria */ 40 ierr = TaoComputeObjectiveAndGradient(tao, tao->solution, &bnk->f, bnk->unprojected_gradient);CHKERRQ(ierr); 41 ierr = VecBoundGradientProjection(bnk->unprojected_gradient,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr); 42 ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&bnk->gnorm);CHKERRQ(ierr); 43 if (PetscIsInfOrNanReal(bnk->f) || PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN"); 44 45 tao->reason = TAO_CONTINUE_ITERATING; 46 ierr = TaoLogConvergenceHistory(tao,bnk->f,bnk->gnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 47 ierr = TaoMonitor(tao,tao->niter,bnk->f,bnk->gnorm,0.0,steplen);CHKERRQ(ierr); 48 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 49 if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0); 50 51 /* Initialize the preconditioner and trust radius */ 52 ierr = TaoBNKInitialize(tao);CHKERRQ(ierr); 53 54 /* Have not converged; continue with Newton method */ 55 while (tao->reason == TAO_CONTINUE_ITERATING) { 56 ++tao->niter; 57 tao->ksp_its=0; 58 59 /* Compute the Hessian */ 60 ierr = TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);CHKERRQ(ierr); 61 62 /* Update the BFGS preconditioner */ 63 if (BNK_PC_BFGS == bnk->pc_type) { 64 if (BFGS_SCALE_PHESS == bnk->bfgs_scale_type) { 65 /* Obtain diagonal for the bfgs preconditioner */ 66 ierr = MatGetDiagonal(tao->hessian, bnk->Diag);CHKERRQ(ierr); 67 ierr = VecAbs(bnk->Diag);CHKERRQ(ierr); 68 ierr = VecReciprocal(bnk->Diag);CHKERRQ(ierr); 69 ierr = MatLMVMSetScale(bnk->M,bnk->Diag);CHKERRQ(ierr); 70 } 71 /* Update the limited memory preconditioner and get existing # of updates */ 72 ierr = MatLMVMUpdate(bnk->M, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr); 73 ierr = MatLMVMGetUpdates(bnk->M, &bfgsUpdates);CHKERRQ(ierr); 74 } 75 76 /* Use the common BNK kernel to compute the safeguarded Newton step */ 77 ierr = TaoBNKComputeStep(tao, PETSC_TRUE, &stepType);CHKERRQ(ierr); 78 79 /* Store current solution before it changes */ 80 bnk->fold = bnk->f; 81 ierr = VecCopy(tao->solution, bnk->Xold);CHKERRQ(ierr); 82 ierr = VecCopy(tao->gradient, bnk->Gold);CHKERRQ(ierr); 83 ierr = VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);CHKERRQ(ierr); 84 85 /* Trigger the line search */ 86 ierr = TaoBNKPerformLineSearch(tao, stepType, &steplen, &ls_reason);CHKERRQ(ierr); 87 88 if (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER) { 89 /* Failed to find an improving point */ 90 bnk->f = bnk->fold; 91 ierr = VecCopy(bnk->Xold, tao->solution);CHKERRQ(ierr); 92 ierr = VecCopy(bnk->Gold, tao->gradient);CHKERRQ(ierr); 93 ierr = VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);CHKERRQ(ierr); 94 steplen = 0.0; 95 tao->reason = TAO_DIVERGED_LS_FAILURE; 96 break; 97 } 98 99 /* update trust radius */ 100 ierr = TaoLineSearchGetFullStepObjective(tao->linesearch, &f_full);CHKERRQ(ierr); 101 ierr = KSPCGGetObjFcn(tao->ksp, &prered);CHKERRQ(ierr); 102 prered = -prered; 103 actred = bnk->fold - f_full; 104 ierr = TaoBNKUpdateTrustRadius(tao, prered, actred, stepType, &trustAccept);CHKERRQ(ierr); 105 106 /* Check for termination */ 107 ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&bnk->gnorm);CHKERRQ(ierr); 108 if (PetscIsInfOrNanReal(bnk->f) || PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF,1,"User provided compute function generated Not-a-Number"); 109 ierr = TaoLogConvergenceHistory(tao,bnk->f,bnk->gnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 110 ierr = TaoMonitor(tao,tao->niter,bnk->f,bnk->gnorm,0.0,steplen);CHKERRQ(ierr); 111 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 112 } 113 PetscFunctionReturn(0); 114 } 115 116 PETSC_EXTERN PetscErrorCode TaoCreate_BNLS(Tao tao) 117 { 118 TAO_BNK *bnk; 119 PetscErrorCode ierr; 120 121 PetscFunctionBegin; 122 ierr = TaoCreate_BNK(tao);CHKERRQ(ierr); 123 tao->ops->solve = TaoSolve_BNLS; 124 125 bnk = (TAO_BNK *)tao->data; 126 bnk->update_type = BNK_UPDATE_STEP; /* trust region updates based on line search step length */ 127 PetscFunctionReturn(0); 128 }