1 #include <../src/tao/bound/impls/bnk/bnk.h> 2 #include <petscksp.h> 3 4 /* 5 Implements Newton's Method with a trust region approach for solving 6 bound constrained minimization problems. 7 8 The linear system solve has to be done with a conjugate gradient method. 9 */ 10 11 static PetscErrorCode TaoSolve_BNTR(Tao tao) 12 { 13 PetscErrorCode ierr; 14 TAO_BNK *bnk = (TAO_BNK *)tao->data; 15 KSPConvergedReason ksp_reason; 16 17 PetscReal oldTrust, prered, actred, stepNorm, steplen; 18 PetscBool stepAccepted = PETSC_TRUE, shift = PETSC_FALSE; 19 PetscInt stepType = BNK_NEWTON; 20 21 PetscFunctionBegin; 22 /* Initialize the preconditioner, KSP solver and trust radius/line search */ 23 tao->reason = TAO_CONTINUE_ITERATING; 24 ierr = TaoBNKInitialize(tao, bnk->init_type);CHKERRQ(ierr); 25 if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0); 26 27 /* Have not converged; continue with Newton method */ 28 while (tao->reason == TAO_CONTINUE_ITERATING) { 29 30 if (stepAccepted) { 31 tao->niter++; 32 tao->ksp_its=0; 33 /* Compute the hessian and update the BFGS preconditioner at the new iterate*/ 34 ierr = TaoBNKComputeHessian(tao);CHKERRQ(ierr); 35 } 36 37 /* Use the common BNK kernel to compute the Newton step (for inactive variables only) */ 38 ierr = TaoBNKComputeStep(tao, shift, &ksp_reason);CHKERRQ(ierr); 39 40 /* Store current solution before it changes */ 41 oldTrust = tao->trust; 42 bnk->fold = bnk->f; 43 ierr = VecCopy(tao->solution, bnk->Xold);CHKERRQ(ierr); 44 ierr = VecCopy(tao->gradient, bnk->Gold);CHKERRQ(ierr); 45 ierr = VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);CHKERRQ(ierr); 46 47 /* Temporarily accept the step and project it into the bounds */ 48 ierr = VecAXPY(tao->solution, 1.0, tao->stepdirection);CHKERRQ(ierr); 49 ierr = VecMedian(tao->XL, tao->solution, tao->XU, tao->solution);CHKERRQ(ierr); 50 51 /* Check if the projection changed the step direction */ 52 ierr = VecCopy(tao->solution, tao->stepdirection);CHKERRQ(ierr); 53 ierr = VecAXPY(tao->stepdirection, -1.0, bnk->Xold);CHKERRQ(ierr); 54 ierr = VecNorm(tao->stepdirection, NORM_2, &stepNorm);CHKERRQ(ierr); 55 if (stepNorm != bnk->dnorm) { 56 /* Projection changed the step, so we have to recompute predicted reduction. 57 However, we deliberately do not change the step norm and the trust radius 58 in order for the safeguard to more closely mimic a piece-wise linesearch 59 along the bounds. */ 60 ierr = MatMult(bnk->H_inactive, tao->stepdirection, bnk->Xwork);CHKERRQ(ierr); 61 ierr = VecAYPX(bnk->Xwork, -0.5, tao->gradient);CHKERRQ(ierr); 62 ierr = VecDot(bnk->Xwork, tao->stepdirection, &prered); 63 } else { 64 /* Step did not change, so we can just recover the pre-computed prediction */ 65 ierr = KSPCGGetObjFcn(tao->ksp, &prered);CHKERRQ(ierr); 66 } 67 prered = -prered; 68 69 /* Compute the actual reduction and update the trust radius */ 70 ierr = TaoComputeObjective(tao, tao->solution, &bnk->f);CHKERRQ(ierr); 71 actred = bnk->fold - bnk->f; 72 ierr = TaoBNKUpdateTrustRadius(tao, prered, actred, bnk->update_type, stepType, &stepAccepted);CHKERRQ(ierr); 73 74 if (stepAccepted) { 75 /* Step is good, evaluate the gradient and the hessian */ 76 steplen = 1.0; 77 ++bnk->newt; 78 ierr = TaoComputeGradient(tao, tao->solution, bnk->unprojected_gradient);CHKERRQ(ierr); 79 ierr = VecBoundGradientProjection(bnk->unprojected_gradient,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr); 80 } else { 81 /* Step is bad, revert old solution and re-solve with new radius*/ 82 steplen = 0.0; 83 bnk->f = bnk->fold; 84 ierr = VecCopy(bnk->Xold, tao->solution);CHKERRQ(ierr); 85 ierr = VecCopy(bnk->Gold, tao->gradient);CHKERRQ(ierr); 86 ierr = VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);CHKERRQ(ierr); 87 if (oldTrust == tao->trust) { 88 /* Can't change the radius anymore so just terminate */ 89 tao->reason = TAO_DIVERGED_TR_REDUCTION; 90 } 91 } 92 93 /* Check for termination */ 94 ierr = TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);CHKERRQ(ierr); 95 if (PetscIsInfOrNanReal(bnk->gnorm)) SETERRQ(PETSC_COMM_SELF,1,"User provided compute function generated Not-a-Number"); 96 ierr = TaoLogConvergenceHistory(tao,bnk->f,bnk->gnorm,0.0,tao->ksp_its);CHKERRQ(ierr); 97 ierr = TaoMonitor(tao,tao->niter,bnk->f,bnk->gnorm,0.0,steplen);CHKERRQ(ierr); 98 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr); 99 } 100 PetscFunctionReturn(0); 101 } 102 103 /*------------------------------------------------------------*/ 104 105 PETSC_EXTERN PetscErrorCode TaoCreate_BNTR(Tao tao) 106 { 107 TAO_BNK *bnk; 108 PetscErrorCode ierr; 109 110 PetscFunctionBegin; 111 ierr = TaoCreate_BNK(tao);CHKERRQ(ierr); 112 tao->ops->solve=TaoSolve_BNTR; 113 114 bnk = (TAO_BNK *)tao->data; 115 bnk->update_type = BNK_UPDATE_REDUCTION; /* trust region updates based on predicted/actual reduction */ 116 bnk->sval = 0.0; /* disable Hessian shifting */ 117 PetscFunctionReturn(0); 118 }