Date: 2025-03-29T05:58:48+00:00

Actual source code: nls.c

      1: #include <petsctaolinesearch.h>
      2: #include <../src/tao/unconstrained/impls/nls/nlsimpl.h>

      4: #include <petscksp.h>

      6: #define NLS_INIT_CONSTANT      0
      7: #define NLS_INIT_DIRECTION     1
      8: #define NLS_INIT_INTERPOLATION 2
      9: #define NLS_INIT_TYPES         3

     11: #define NLS_UPDATE_STEP          0
     12: #define NLS_UPDATE_REDUCTION     1
     13: #define NLS_UPDATE_INTERPOLATION 2
     14: #define NLS_UPDATE_TYPES         3

     16: static const char *NLS_INIT[64] = {"constant", "direction", "interpolation"};

     18: static const char *NLS_UPDATE[64] = {"step", "reduction", "interpolation"};

     20: /*
     21:  Implements Newton's Method with a line search approach for solving
     22:  unconstrained minimization problems.  A More'-Thuente line search
     23:  is used to guarantee that the bfgs preconditioner remains positive
     24:  definite.

     26:  The method can shift the Hessian matrix.  The shifting procedure is
     27:  adapted from the PATH algorithm for solving complementarity
     28:  problems.

     30:  The linear system solve should be done with a conjugate gradient
     31:  method, although any method can be used.
     32: */

     34: #define NLS_NEWTON   0
     35: #define NLS_BFGS     1
     36: #define NLS_GRADIENT 2

     38: static PetscErrorCode TaoSolve_NLS(Tao tao)
     39: {
     40:   TAO_NLS                     *nlsP = (TAO_NLS *)tao->data;
     41:   KSPType                      ksp_type;
     42:   PetscBool                    is_nash, is_stcg, is_gltr, is_bfgs, is_jacobi, is_symmetric, sym_set;
     43:   KSPConvergedReason           ksp_reason;
     44:   PC                           pc;
     45:   TaoLineSearchConvergedReason ls_reason;

     47:   PetscReal fmin, ftrial, f_full, prered, actred, kappa, sigma;
     48:   PetscReal tau, tau_1, tau_2, tau_max, tau_min, max_radius;
     49:   PetscReal f, fold, gdx, gnorm, pert;
     50:   PetscReal step   = 1.0;
     51:   PetscReal norm_d = 0.0, e_min;

     53:   PetscInt stepType;
     54:   PetscInt bfgsUpdates = 0;
     55:   PetscInt n, N, kspits;
     56:   PetscInt needH = 1;

     58:   PetscInt i_max = 5;
     59:   PetscInt j_max = 1;
     60:   PetscInt i, j;

     62:   PetscFunctionBegin;
     63:   if (tao->XL || tao->XU || tao->ops->computebounds) PetscCall(PetscInfo(tao, "WARNING: Variable bounds have been set but will be ignored by nls algorithm\n"));

     65:   /* Initialized variables */
     66:   pert = nlsP->sval;

     68:   /* Number of times ksp stopped because of these reasons */
     69:   nlsP->ksp_atol = 0;
     70:   nlsP->ksp_rtol = 0;
     71:   nlsP->ksp_dtol = 0;
     72:   nlsP->ksp_ctol = 0;
     73:   nlsP->ksp_negc = 0;
     74:   nlsP->ksp_iter = 0;
     75:   nlsP->ksp_othr = 0;

     77:   /* Initialize trust-region radius when using nash, stcg, or gltr
     78:      Command automatically ignored for other methods
     79:      Will be reset during the first iteration
     80:   */
     81:   PetscCall(KSPGetType(tao->ksp, &ksp_type));
     82:   PetscCall(PetscStrcmp(ksp_type, KSPNASH, &is_nash));
     83:   PetscCall(PetscStrcmp(ksp_type, KSPSTCG, &is_stcg));
     84:   PetscCall(PetscStrcmp(ksp_type, KSPGLTR, &is_gltr));

     86:   PetscCall(KSPCGSetRadius(tao->ksp, nlsP->max_radius));

     88:   if (is_nash || is_stcg || is_gltr) {
     89:     PetscCheck(tao->trust0 >= 0.0, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "Initial radius negative");
     90:     tao->trust = tao->trust0;
     91:     tao->trust = PetscMax(tao->trust, nlsP->min_radius);
     92:     tao->trust = PetscMin(tao->trust, nlsP->max_radius);
     93:   }

     95:   /* Check convergence criteria */
     96:   PetscCall(TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient));
     97:   PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm));
     98:   PetscCheck(!PetscIsInfOrNanReal(f) && !PetscIsInfOrNanReal(gnorm), PetscObjectComm((PetscObject)tao), PETSC_ERR_USER, "User provided compute function generated Inf or NaN");

    100:   tao->reason = TAO_CONTINUE_ITERATING;
    101:   PetscCall(TaoLogConvergenceHistory(tao, f, gnorm, 0.0, tao->ksp_its));
    102:   PetscCall(TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step));
    103:   PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
    104:   if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);

    106:   /* Allocate the vectors needed for the BFGS approximation */
    107:   PetscCall(KSPGetPC(tao->ksp, &pc));
    108:   PetscCall(PetscObjectTypeCompare((PetscObject)pc, PCLMVM, &is_bfgs));
    109:   PetscCall(PetscObjectTypeCompare((PetscObject)pc, PCJACOBI, &is_jacobi));
    110:   if (is_bfgs) {
    111:     nlsP->bfgs_pre = pc;
    112:     PetscCall(PCLMVMGetMatLMVM(nlsP->bfgs_pre, &nlsP->M));
    113:     PetscCall(VecGetLocalSize(tao->solution, &n));
    114:     PetscCall(VecGetSize(tao->solution, &N));
    115:     PetscCall(MatSetSizes(nlsP->M, n, n, N, N));
    116:     PetscCall(MatLMVMAllocate(nlsP->M, tao->solution, tao->gradient));
    117:     PetscCall(MatIsSymmetricKnown(nlsP->M, &sym_set, &is_symmetric));
    118:     PetscCheck(sym_set && is_symmetric, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix in the LMVM preconditioner must be symmetric.");
    119:   } else if (is_jacobi) PetscCall(PCJacobiSetUseAbs(pc, PETSC_TRUE));

    121:   /* Initialize trust-region radius.  The initialization is only performed
    122:      when we are using Nash, Steihaug-Toint or the Generalized Lanczos method. */
    123:   if (is_nash || is_stcg || is_gltr) {
    124:     switch (nlsP->init_type) {
    125:     case NLS_INIT_CONSTANT:
    126:       /* Use the initial radius specified */
    127:       break;

    129:     case NLS_INIT_INTERPOLATION:
    130:       /* Use the initial radius specified */
    131:       max_radius = 0.0;

    133:       for (j = 0; j < j_max; ++j) {
    134:         fmin  = f;
    135:         sigma = 0.0;

    137:         if (needH) {
    138:           PetscCall(TaoComputeHessian(tao, tao->solution, tao->hessian, tao->hessian_pre));
    139:           needH = 0;
    140:         }

    142:         for (i = 0; i < i_max; ++i) {
    143:           PetscCall(VecCopy(tao->solution, nlsP->W));
    144:           PetscCall(VecAXPY(nlsP->W, -tao->trust / gnorm, tao->gradient));
    145:           PetscCall(TaoComputeObjective(tao, nlsP->W, &ftrial));
    146:           if (PetscIsInfOrNanReal(ftrial)) {
    147:             tau = nlsP->gamma1_i;
    148:           } else {
    149:             if (ftrial < fmin) {
    150:               fmin  = ftrial;
    151:               sigma = -tao->trust / gnorm;
    152:             }

    154:             PetscCall(MatMult(tao->hessian, tao->gradient, nlsP->D));
    155:             PetscCall(VecDot(tao->gradient, nlsP->D, &prered));

    157:             prered = tao->trust * (gnorm - 0.5 * tao->trust * prered / (gnorm * gnorm));
    158:             actred = f - ftrial;
    159:             if ((PetscAbsScalar(actred) <= nlsP->epsilon) && (PetscAbsScalar(prered) <= nlsP->epsilon)) {
    160:               kappa = 1.0;
    161:             } else {
    162:               kappa = actred / prered;
    163:             }

    165:             tau_1   = nlsP->theta_i * gnorm * tao->trust / (nlsP->theta_i * gnorm * tao->trust + (1.0 - nlsP->theta_i) * prered - actred);
    166:             tau_2   = nlsP->theta_i * gnorm * tao->trust / (nlsP->theta_i * gnorm * tao->trust - (1.0 + nlsP->theta_i) * prered + actred);
    167:             tau_min = PetscMin(tau_1, tau_2);
    168:             tau_max = PetscMax(tau_1, tau_2);

    170:             if (PetscAbsScalar(kappa - (PetscReal)1.0) <= nlsP->mu1_i) {
    171:               /* Great agreement */
    172:               max_radius = PetscMax(max_radius, tao->trust);

    174:               if (tau_max < 1.0) {
    175:                 tau = nlsP->gamma3_i;
    176:               } else if (tau_max > nlsP->gamma4_i) {
    177:                 tau = nlsP->gamma4_i;
    178:               } else if (tau_1 >= 1.0 && tau_1 <= nlsP->gamma4_i && tau_2 < 1.0) {
    179:                 tau = tau_1;
    180:               } else if (tau_2 >= 1.0 && tau_2 <= nlsP->gamma4_i && tau_1 < 1.0) {
    181:                 tau = tau_2;
    182:               } else {
    183:                 tau = tau_max;
    184:               }
    185:             } else if (PetscAbsScalar(kappa - (PetscReal)1.0) <= nlsP->mu2_i) {
    186:               /* Good agreement */
    187:               max_radius = PetscMax(max_radius, tao->trust);

    189:               if (tau_max < nlsP->gamma2_i) {
    190:                 tau = nlsP->gamma2_i;
    191:               } else if (tau_max > nlsP->gamma3_i) {
    192:                 tau = nlsP->gamma3_i;
    193:               } else {
    194:                 tau = tau_max;
    195:               }
    196:             } else {
    197:               /* Not good agreement */
    198:               if (tau_min > 1.0) {
    199:                 tau = nlsP->gamma2_i;
    200:               } else if (tau_max < nlsP->gamma1_i) {
    201:                 tau = nlsP->gamma1_i;
    202:               } else if ((tau_min < nlsP->gamma1_i) && (tau_max >= 1.0)) {
    203:                 tau = nlsP->gamma1_i;
    204:               } else if ((tau_1 >= nlsP->gamma1_i) && (tau_1 < 1.0) && ((tau_2 < nlsP->gamma1_i) || (tau_2 >= 1.0))) {
    205:                 tau = tau_1;
    206:               } else if ((tau_2 >= nlsP->gamma1_i) && (tau_2 < 1.0) && ((tau_1 < nlsP->gamma1_i) || (tau_2 >= 1.0))) {
    207:                 tau = tau_2;
    208:               } else {
    209:                 tau = tau_max;
    210:               }
    211:             }
    212:           }
    213:           tao->trust = tau * tao->trust;
    214:         }

    216:         if (fmin < f) {
    217:           f = fmin;
    218:           PetscCall(VecAXPY(tao->solution, sigma, tao->gradient));
    219:           PetscCall(TaoComputeGradient(tao, tao->solution, tao->gradient));

    221:           PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm));
    222:           PetscCheck(!PetscIsInfOrNanReal(gnorm), PetscObjectComm((PetscObject)tao), PETSC_ERR_USER, "User provided compute gradient generated Inf or NaN");
    223:           needH = 1;

    225:           PetscCall(TaoLogConvergenceHistory(tao, f, gnorm, 0.0, tao->ksp_its));
    226:           PetscCall(TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step));
    227:           PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
    228:           if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);
    229:         }
    230:       }
    231:       tao->trust = PetscMax(tao->trust, max_radius);

    233:       /* Modify the radius if it is too large or small */
    234:       tao->trust = PetscMax(tao->trust, nlsP->min_radius);
    235:       tao->trust = PetscMin(tao->trust, nlsP->max_radius);
    236:       break;

    238:     default:
    239:       /* Norm of the first direction will initialize radius */
    240:       tao->trust = 0.0;
    241:       break;
    242:     }
    243:   }

    245:   /* Set counter for gradient/reset steps*/
    246:   nlsP->newt = 0;
    247:   nlsP->bfgs = 0;
    248:   nlsP->grad = 0;

    250:   /* Have not converged; continue with Newton method */
    251:   while (tao->reason == TAO_CONTINUE_ITERATING) {
    252:     /* Call general purpose update function */
    253:     if (tao->ops->update) {
    254:       PetscUseTypeMethod(tao, update, tao->niter, tao->user_update);
    255:       PetscCall(TaoComputeObjective(tao, tao->solution, &f));
    256:     }
    257:     ++tao->niter;
    258:     tao->ksp_its = 0;

    260:     /* Compute the Hessian */
    261:     if (needH) PetscCall(TaoComputeHessian(tao, tao->solution, tao->hessian, tao->hessian_pre));

    263:     /* Shift the Hessian matrix */
    264:     if (pert > 0) {
    265:       PetscCall(MatShift(tao->hessian, pert));
    266:       if (tao->hessian != tao->hessian_pre) PetscCall(MatShift(tao->hessian_pre, pert));
    267:     }

    269:     if (nlsP->bfgs_pre) {
    270:       PetscCall(MatLMVMUpdate(nlsP->M, tao->solution, tao->gradient));
    271:       ++bfgsUpdates;
    272:     }

    274:     /* Solve the Newton system of equations */
    275:     PetscCall(KSPSetOperators(tao->ksp, tao->hessian, tao->hessian_pre));
    276:     if (is_nash || is_stcg || is_gltr) {
    277:       PetscCall(KSPCGSetRadius(tao->ksp, nlsP->max_radius));
    278:       PetscCall(KSPSolve(tao->ksp, tao->gradient, nlsP->D));
    279:       PetscCall(KSPGetIterationNumber(tao->ksp, &kspits));
    280:       tao->ksp_its += kspits;
    281:       tao->ksp_tot_its += kspits;
    282:       PetscCall(KSPCGGetNormD(tao->ksp, &norm_d));

    284:       if (0.0 == tao->trust) {
    285:         /* Radius was uninitialized; use the norm of the direction */
    286:         if (norm_d > 0.0) {
    287:           tao->trust = norm_d;

    289:           /* Modify the radius if it is too large or small */
    290:           tao->trust = PetscMax(tao->trust, nlsP->min_radius);
    291:           tao->trust = PetscMin(tao->trust, nlsP->max_radius);
    292:         } else {
    293:           /* The direction was bad; set radius to default value and re-solve
    294:              the trust-region subproblem to get a direction */
    295:           tao->trust = tao->trust0;

    297:           /* Modify the radius if it is too large or small */
    298:           tao->trust = PetscMax(tao->trust, nlsP->min_radius);
    299:           tao->trust = PetscMin(tao->trust, nlsP->max_radius);

    301:           PetscCall(KSPCGSetRadius(tao->ksp, nlsP->max_radius));
    302:           PetscCall(KSPSolve(tao->ksp, tao->gradient, nlsP->D));
    303:           PetscCall(KSPGetIterationNumber(tao->ksp, &kspits));
    304:           tao->ksp_its += kspits;
    305:           tao->ksp_tot_its += kspits;
    306:           PetscCall(KSPCGGetNormD(tao->ksp, &norm_d));

    308:           PetscCheck(norm_d != 0.0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Initial direction zero");
    309:         }
    310:       }
    311:     } else {
    312:       PetscCall(KSPSolve(tao->ksp, tao->gradient, nlsP->D));
    313:       PetscCall(KSPGetIterationNumber(tao->ksp, &kspits));
    314:       tao->ksp_its += kspits;
    315:       tao->ksp_tot_its += kspits;
    316:     }
    317:     PetscCall(VecScale(nlsP->D, -1.0));
    318:     PetscCall(KSPGetConvergedReason(tao->ksp, &ksp_reason));
    319:     if ((KSP_DIVERGED_INDEFINITE_PC == ksp_reason) && (nlsP->bfgs_pre)) {
    320:       /* Preconditioner is numerically indefinite; reset the
    321:          approximate if using BFGS preconditioning. */
    322:       PetscCall(MatLMVMReset(nlsP->M, PETSC_FALSE));
    323:       PetscCall(MatLMVMUpdate(nlsP->M, tao->solution, tao->gradient));
    324:       bfgsUpdates = 1;
    325:     }

    327:     if (KSP_CONVERGED_ATOL == ksp_reason) {
    328:       ++nlsP->ksp_atol;
    329:     } else if (KSP_CONVERGED_RTOL == ksp_reason) {
    330:       ++nlsP->ksp_rtol;
    331:     } else if (KSP_CONVERGED_STEP_LENGTH == ksp_reason) {
    332:       ++nlsP->ksp_ctol;
    333:     } else if (KSP_CONVERGED_NEG_CURVE == ksp_reason) {
    334:       ++nlsP->ksp_negc;
    335:     } else if (KSP_DIVERGED_DTOL == ksp_reason) {
    336:       ++nlsP->ksp_dtol;
    337:     } else if (KSP_DIVERGED_ITS == ksp_reason) {
    338:       ++nlsP->ksp_iter;
    339:     } else {
    340:       ++nlsP->ksp_othr;
    341:     }

    343:     /* Check for success (descent direction) */
    344:     PetscCall(VecDot(nlsP->D, tao->gradient, &gdx));
    345:     if ((gdx >= 0.0) || PetscIsInfOrNanReal(gdx)) {
    346:       /* Newton step is not descent or direction produced Inf or NaN
    347:          Update the perturbation for next time */
    348:       if (pert <= 0.0) {
    349:         /* Initialize the perturbation */
    350:         pert = PetscMin(nlsP->imax, PetscMax(nlsP->imin, nlsP->imfac * gnorm));
    351:         if (is_gltr) {
    352:           PetscCall(KSPGLTRGetMinEig(tao->ksp, &e_min));
    353:           pert = PetscMax(pert, -e_min);
    354:         }
    355:       } else {
    356:         /* Increase the perturbation */
    357:         pert = PetscMin(nlsP->pmax, PetscMax(nlsP->pgfac * pert, nlsP->pmgfac * gnorm));
    358:       }

    360:       if (!nlsP->bfgs_pre) {
    361:         /* We don't have the bfgs matrix around and updated
    362:            Must use gradient direction in this case */
    363:         PetscCall(VecCopy(tao->gradient, nlsP->D));
    364:         PetscCall(VecScale(nlsP->D, -1.0));
    365:         ++nlsP->grad;
    366:         stepType = NLS_GRADIENT;
    367:       } else {
    368:         /* Attempt to use the BFGS direction */
    369:         PetscCall(MatSolve(nlsP->M, tao->gradient, nlsP->D));
    370:         PetscCall(VecScale(nlsP->D, -1.0));

    372:         /* Check for success (descent direction) */
    373:         PetscCall(VecDot(tao->gradient, nlsP->D, &gdx));
    374:         if ((gdx >= 0) || PetscIsInfOrNanReal(gdx)) {
    375:           /* BFGS direction is not descent or direction produced not a number
    376:              We can assert bfgsUpdates > 1 in this case because
    377:              the first solve produces the scaled gradient direction,
    378:              which is guaranteed to be descent */

    380:           /* Use steepest descent direction (scaled) */
    381:           PetscCall(MatLMVMReset(nlsP->M, PETSC_FALSE));
    382:           PetscCall(MatLMVMUpdate(nlsP->M, tao->solution, tao->gradient));
    383:           PetscCall(MatSolve(nlsP->M, tao->gradient, nlsP->D));
    384:           PetscCall(VecScale(nlsP->D, -1.0));

    386:           bfgsUpdates = 1;
    387:           ++nlsP->grad;
    388:           stepType = NLS_GRADIENT;
    389:         } else {
    390:           PetscCall(MatLMVMGetUpdateCount(nlsP->M, &bfgsUpdates));
    391:           if (1 == bfgsUpdates) {
    392:             /* The first BFGS direction is always the scaled gradient */
    393:             ++nlsP->grad;
    394:             stepType = NLS_GRADIENT;
    395:           } else {
    396:             ++nlsP->bfgs;
    397:             stepType = NLS_BFGS;
    398:           }
    399:         }
    400:       }
    401:     } else {
    402:       /* Computed Newton step is descent */
    403:       switch (ksp_reason) {
    404:       case KSP_DIVERGED_NANORINF:
    405:       case KSP_DIVERGED_BREAKDOWN:
    406:       case KSP_DIVERGED_INDEFINITE_MAT:
    407:       case KSP_DIVERGED_INDEFINITE_PC:
    408:       case KSP_CONVERGED_NEG_CURVE:
    409:         /* Matrix or preconditioner is indefinite; increase perturbation */
    410:         if (pert <= 0.0) {
    411:           /* Initialize the perturbation */
    412:           pert = PetscMin(nlsP->imax, PetscMax(nlsP->imin, nlsP->imfac * gnorm));
    413:           if (is_gltr) {
    414:             PetscCall(KSPGLTRGetMinEig(tao->ksp, &e_min));
    415:             pert = PetscMax(pert, -e_min);
    416:           }
    417:         } else {
    418:           /* Increase the perturbation */
    419:           pert = PetscMin(nlsP->pmax, PetscMax(nlsP->pgfac * pert, nlsP->pmgfac * gnorm));
    420:         }
    421:         break;

    423:       default:
    424:         /* Newton step computation is good; decrease perturbation */
    425:         pert = PetscMin(nlsP->psfac * pert, nlsP->pmsfac * gnorm);
    426:         if (pert < nlsP->pmin) pert = 0.0;
    427:         break;
    428:       }

    430:       ++nlsP->newt;
    431:       stepType = NLS_NEWTON;
    432:     }

    434:     /* Perform the linesearch */
    435:     fold = f;
    436:     PetscCall(VecCopy(tao->solution, nlsP->Xold));
    437:     PetscCall(VecCopy(tao->gradient, nlsP->Gold));

    439:     PetscCall(TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, nlsP->D, &step, &ls_reason));
    440:     PetscCall(TaoAddLineSearchCounts(tao));

    442:     while (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER && stepType != NLS_GRADIENT) { /* Linesearch failed */
    443:       f = fold;
    444:       PetscCall(VecCopy(nlsP->Xold, tao->solution));
    445:       PetscCall(VecCopy(nlsP->Gold, tao->gradient));

    447:       switch (stepType) {
    448:       case NLS_NEWTON:
    449:         /* Failed to obtain acceptable iterate with Newton 1step
    450:            Update the perturbation for next time */
    451:         if (pert <= 0.0) {
    452:           /* Initialize the perturbation */
    453:           pert = PetscMin(nlsP->imax, PetscMax(nlsP->imin, nlsP->imfac * gnorm));
    454:           if (is_gltr) {
    455:             PetscCall(KSPGLTRGetMinEig(tao->ksp, &e_min));
    456:             pert = PetscMax(pert, -e_min);
    457:           }
    458:         } else {
    459:           /* Increase the perturbation */
    460:           pert = PetscMin(nlsP->pmax, PetscMax(nlsP->pgfac * pert, nlsP->pmgfac * gnorm));
    461:         }

    463:         if (!nlsP->bfgs_pre) {
    464:           /* We don't have the bfgs matrix around and being updated
    465:              Must use gradient direction in this case */
    466:           PetscCall(VecCopy(tao->gradient, nlsP->D));
    467:           ++nlsP->grad;
    468:           stepType = NLS_GRADIENT;
    469:         } else {
    470:           /* Attempt to use the BFGS direction */
    471:           PetscCall(MatSolve(nlsP->M, tao->gradient, nlsP->D));
    472:           /* Check for success (descent direction) */
    473:           PetscCall(VecDot(tao->solution, nlsP->D, &gdx));
    474:           if ((gdx <= 0) || PetscIsInfOrNanReal(gdx)) {
    475:             /* BFGS direction is not descent or direction produced not a number
    476:                We can assert bfgsUpdates > 1 in this case
    477:                Use steepest descent direction (scaled) */
    478:             PetscCall(MatLMVMReset(nlsP->M, PETSC_FALSE));
    479:             PetscCall(MatLMVMUpdate(nlsP->M, tao->solution, tao->gradient));
    480:             PetscCall(MatSolve(nlsP->M, tao->gradient, nlsP->D));

    482:             bfgsUpdates = 1;
    483:             ++nlsP->grad;
    484:             stepType = NLS_GRADIENT;
    485:           } else {
    486:             if (1 == bfgsUpdates) {
    487:               /* The first BFGS direction is always the scaled gradient */
    488:               ++nlsP->grad;
    489:               stepType = NLS_GRADIENT;
    490:             } else {
    491:               ++nlsP->bfgs;
    492:               stepType = NLS_BFGS;
    493:             }
    494:           }
    495:         }
    496:         break;

    498:       case NLS_BFGS:
    499:         /* Can only enter if pc_type == NLS_PC_BFGS
    500:            Failed to obtain acceptable iterate with BFGS step
    501:            Attempt to use the scaled gradient direction */
    502:         PetscCall(MatLMVMReset(nlsP->M, PETSC_FALSE));
    503:         PetscCall(MatLMVMUpdate(nlsP->M, tao->solution, tao->gradient));
    504:         PetscCall(MatSolve(nlsP->M, tao->gradient, nlsP->D));

    506:         bfgsUpdates = 1;
    507:         ++nlsP->grad;
    508:         stepType = NLS_GRADIENT;
    509:         break;
    510:       }
    511:       PetscCall(VecScale(nlsP->D, -1.0));

    513:       PetscCall(TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, nlsP->D, &step, &ls_reason));
    514:       PetscCall(TaoLineSearchGetFullStepObjective(tao->linesearch, &f_full));
    515:       PetscCall(TaoAddLineSearchCounts(tao));
    516:     }

    518:     if (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER) {
    519:       /* Failed to find an improving point */
    520:       f = fold;
    521:       PetscCall(VecCopy(nlsP->Xold, tao->solution));
    522:       PetscCall(VecCopy(nlsP->Gold, tao->gradient));
    523:       step        = 0.0;
    524:       tao->reason = TAO_DIVERGED_LS_FAILURE;
    525:       break;
    526:     }

    528:     /* Update trust region radius */
    529:     if (is_nash || is_stcg || is_gltr) {
    530:       switch (nlsP->update_type) {
    531:       case NLS_UPDATE_STEP:
    532:         if (stepType == NLS_NEWTON) {
    533:           if (step < nlsP->nu1) {
    534:             /* Very bad step taken; reduce radius */
    535:             tao->trust = nlsP->omega1 * PetscMin(norm_d, tao->trust);
    536:           } else if (step < nlsP->nu2) {
    537:             /* Reasonably bad step taken; reduce radius */
    538:             tao->trust = nlsP->omega2 * PetscMin(norm_d, tao->trust);
    539:           } else if (step < nlsP->nu3) {
    540:             /*  Reasonable step was taken; leave radius alone */
    541:             if (nlsP->omega3 < 1.0) {
    542:               tao->trust = nlsP->omega3 * PetscMin(norm_d, tao->trust);
    543:             } else if (nlsP->omega3 > 1.0) {
    544:               tao->trust = PetscMax(nlsP->omega3 * norm_d, tao->trust);
    545:             }
    546:           } else if (step < nlsP->nu4) {
    547:             /*  Full step taken; increase the radius */
    548:             tao->trust = PetscMax(nlsP->omega4 * norm_d, tao->trust);
    549:           } else {
    550:             /*  More than full step taken; increase the radius */
    551:             tao->trust = PetscMax(nlsP->omega5 * norm_d, tao->trust);
    552:           }
    553:         } else {
    554:           /*  Newton step was not good; reduce the radius */
    555:           tao->trust = nlsP->omega1 * PetscMin(norm_d, tao->trust);
    556:         }
    557:         break;

    559:       case NLS_UPDATE_REDUCTION:
    560:         if (stepType == NLS_NEWTON) {
    561:           /*  Get predicted reduction */

    563:           PetscCall(KSPCGGetObjFcn(tao->ksp, &prered));
    564:           if (prered >= 0.0) {
    565:             /*  The predicted reduction has the wrong sign.  This cannot */
    566:             /*  happen in infinite precision arithmetic.  Step should */
    567:             /*  be rejected! */
    568:             tao->trust = nlsP->alpha1 * PetscMin(tao->trust, norm_d);
    569:           } else {
    570:             if (PetscIsInfOrNanReal(f_full)) {
    571:               tao->trust = nlsP->alpha1 * PetscMin(tao->trust, norm_d);
    572:             } else {
    573:               /*  Compute and actual reduction */
    574:               actred = fold - f_full;
    575:               prered = -prered;
    576:               if ((PetscAbsScalar(actred) <= nlsP->epsilon) && (PetscAbsScalar(prered) <= nlsP->epsilon)) {
    577:                 kappa = 1.0;
    578:               } else {
    579:                 kappa = actred / prered;
    580:               }

    582:               /*  Accept of reject the step and update radius */
    583:               if (kappa < nlsP->eta1) {
    584:                 /*  Very bad step */
    585:                 tao->trust = nlsP->alpha1 * PetscMin(tao->trust, norm_d);
    586:               } else if (kappa < nlsP->eta2) {
    587:                 /*  Marginal bad step */
    588:                 tao->trust = nlsP->alpha2 * PetscMin(tao->trust, norm_d);
    589:               } else if (kappa < nlsP->eta3) {
    590:                 /*  Reasonable step */
    591:                 if (nlsP->alpha3 < 1.0) {
    592:                   tao->trust = nlsP->alpha3 * PetscMin(norm_d, tao->trust);
    593:                 } else if (nlsP->alpha3 > 1.0) {
    594:                   tao->trust = PetscMax(nlsP->alpha3 * norm_d, tao->trust);
    595:                 }
    596:               } else if (kappa < nlsP->eta4) {
    597:                 /*  Good step */
    598:                 tao->trust = PetscMax(nlsP->alpha4 * norm_d, tao->trust);
    599:               } else {
    600:                 /*  Very good step */
    601:                 tao->trust = PetscMax(nlsP->alpha5 * norm_d, tao->trust);
    602:               }
    603:             }
    604:           }
    605:         } else {
    606:           /*  Newton step was not good; reduce the radius */
    607:           tao->trust = nlsP->alpha1 * PetscMin(norm_d, tao->trust);
    608:         }
    609:         break;

    611:       default:
    612:         if (stepType == NLS_NEWTON) {
    613:           PetscCall(KSPCGGetObjFcn(tao->ksp, &prered));
    614:           if (prered >= 0.0) {
    615:             /*  The predicted reduction has the wrong sign.  This cannot */
    616:             /*  happen in infinite precision arithmetic.  Step should */
    617:             /*  be rejected! */
    618:             tao->trust = nlsP->gamma1 * PetscMin(tao->trust, norm_d);
    619:           } else {
    620:             if (PetscIsInfOrNanReal(f_full)) {
    621:               tao->trust = nlsP->gamma1 * PetscMin(tao->trust, norm_d);
    622:             } else {
    623:               actred = fold - f_full;
    624:               prered = -prered;
    625:               if ((PetscAbsScalar(actred) <= nlsP->epsilon) && (PetscAbsScalar(prered) <= nlsP->epsilon)) {
    626:                 kappa = 1.0;
    627:               } else {
    628:                 kappa = actred / prered;
    629:               }

    631:               tau_1   = nlsP->theta * gdx / (nlsP->theta * gdx - (1.0 - nlsP->theta) * prered + actred);
    632:               tau_2   = nlsP->theta * gdx / (nlsP->theta * gdx + (1.0 + nlsP->theta) * prered - actred);
    633:               tau_min = PetscMin(tau_1, tau_2);
    634:               tau_max = PetscMax(tau_1, tau_2);

    636:               if (kappa >= 1.0 - nlsP->mu1) {
    637:                 /*  Great agreement */
    638:                 if (tau_max < 1.0) {
    639:                   tao->trust = PetscMax(tao->trust, nlsP->gamma3 * norm_d);
    640:                 } else if (tau_max > nlsP->gamma4) {
    641:                   tao->trust = PetscMax(tao->trust, nlsP->gamma4 * norm_d);
    642:                 } else {
    643:                   tao->trust = PetscMax(tao->trust, tau_max * norm_d);
    644:                 }
    645:               } else if (kappa >= 1.0 - nlsP->mu2) {
    646:                 /*  Good agreement */

    648:                 if (tau_max < nlsP->gamma2) {
    649:                   tao->trust = nlsP->gamma2 * PetscMin(tao->trust, norm_d);
    650:                 } else if (tau_max > nlsP->gamma3) {
    651:                   tao->trust = PetscMax(tao->trust, nlsP->gamma3 * norm_d);
    652:                 } else if (tau_max < 1.0) {
    653:                   tao->trust = tau_max * PetscMin(tao->trust, norm_d);
    654:                 } else {
    655:                   tao->trust = PetscMax(tao->trust, tau_max * norm_d);
    656:                 }
    657:               } else {
    658:                 /*  Not good agreement */
    659:                 if (tau_min > 1.0) {
    660:                   tao->trust = nlsP->gamma2 * PetscMin(tao->trust, norm_d);
    661:                 } else if (tau_max < nlsP->gamma1) {
    662:                   tao->trust = nlsP->gamma1 * PetscMin(tao->trust, norm_d);
    663:                 } else if ((tau_min < nlsP->gamma1) && (tau_max >= 1.0)) {
    664:                   tao->trust = nlsP->gamma1 * PetscMin(tao->trust, norm_d);
    665:                 } else if ((tau_1 >= nlsP->gamma1) && (tau_1 < 1.0) && ((tau_2 < nlsP->gamma1) || (tau_2 >= 1.0))) {
    666:                   tao->trust = tau_1 * PetscMin(tao->trust, norm_d);
    667:                 } else if ((tau_2 >= nlsP->gamma1) && (tau_2 < 1.0) && ((tau_1 < nlsP->gamma1) || (tau_2 >= 1.0))) {
    668:                   tao->trust = tau_2 * PetscMin(tao->trust, norm_d);
    669:                 } else {
    670:                   tao->trust = tau_max * PetscMin(tao->trust, norm_d);
    671:                 }
    672:               }
    673:             }
    674:           }
    675:         } else {
    676:           /*  Newton step was not good; reduce the radius */
    677:           tao->trust = nlsP->gamma1 * PetscMin(norm_d, tao->trust);
    678:         }
    679:         break;
    680:       }

    682:       /*  The radius may have been increased; modify if it is too large */
    683:       tao->trust = PetscMin(tao->trust, nlsP->max_radius);
    684:     }

    686:     /*  Check for termination */
    687:     PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm));
    688:     PetscCheck(!PetscIsInfOrNanReal(f) && !PetscIsInfOrNanReal(gnorm), PetscObjectComm((PetscObject)tao), PETSC_ERR_USER, "User provided compute function generated Not-a-Number");
    689:     needH = 1;
    690:     PetscCall(TaoLogConvergenceHistory(tao, f, gnorm, 0.0, tao->ksp_its));
    691:     PetscCall(TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step));
    692:     PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
    693:   }
    694:   PetscFunctionReturn(PETSC_SUCCESS);
    695: }

    697: /* ---------------------------------------------------------- */
    698: static PetscErrorCode TaoSetUp_NLS(Tao tao)
    699: {
    700:   TAO_NLS *nlsP = (TAO_NLS *)tao->data;

    702:   PetscFunctionBegin;
    703:   if (!tao->gradient) PetscCall(VecDuplicate(tao->solution, &tao->gradient));
    704:   if (!tao->stepdirection) PetscCall(VecDuplicate(tao->solution, &tao->stepdirection));
    705:   if (!nlsP->W) PetscCall(VecDuplicate(tao->solution, &nlsP->W));
    706:   if (!nlsP->D) PetscCall(VecDuplicate(tao->solution, &nlsP->D));
    707:   if (!nlsP->Xold) PetscCall(VecDuplicate(tao->solution, &nlsP->Xold));
    708:   if (!nlsP->Gold) PetscCall(VecDuplicate(tao->solution, &nlsP->Gold));
    709:   nlsP->M        = NULL;
    710:   nlsP->bfgs_pre = NULL;
    711:   PetscFunctionReturn(PETSC_SUCCESS);
    712: }

    714: /*------------------------------------------------------------*/
    715: static PetscErrorCode TaoDestroy_NLS(Tao tao)
    716: {
    717:   TAO_NLS *nlsP = (TAO_NLS *)tao->data;

    719:   PetscFunctionBegin;
    720:   if (tao->setupcalled) {
    721:     PetscCall(VecDestroy(&nlsP->D));
    722:     PetscCall(VecDestroy(&nlsP->W));
    723:     PetscCall(VecDestroy(&nlsP->Xold));
    724:     PetscCall(VecDestroy(&nlsP->Gold));
    725:   }
    726:   PetscCall(KSPDestroy(&tao->ksp));
    727:   PetscCall(PetscFree(tao->data));
    728:   PetscFunctionReturn(PETSC_SUCCESS);
    729: }

    731: /*------------------------------------------------------------*/
    732: static PetscErrorCode TaoSetFromOptions_NLS(Tao tao, PetscOptionItems PetscOptionsObject)
    733: {
    734:   TAO_NLS *nlsP = (TAO_NLS *)tao->data;

    736:   PetscFunctionBegin;
    737:   PetscOptionsHeadBegin(PetscOptionsObject, "Newton line search method for unconstrained optimization");
    738:   PetscCall(PetscOptionsEList("-tao_nls_init_type", "radius initialization type", "", NLS_INIT, NLS_INIT_TYPES, NLS_INIT[nlsP->init_type], &nlsP->init_type, NULL));
    739:   PetscCall(PetscOptionsEList("-tao_nls_update_type", "radius update type", "", NLS_UPDATE, NLS_UPDATE_TYPES, NLS_UPDATE[nlsP->update_type], &nlsP->update_type, NULL));
    740:   PetscCall(PetscOptionsReal("-tao_nls_sval", "perturbation starting value", "", nlsP->sval, &nlsP->sval, NULL));
    741:   PetscCall(PetscOptionsReal("-tao_nls_imin", "minimum initial perturbation", "", nlsP->imin, &nlsP->imin, NULL));
    742:   PetscCall(PetscOptionsReal("-tao_nls_imax", "maximum initial perturbation", "", nlsP->imax, &nlsP->imax, NULL));
    743:   PetscCall(PetscOptionsReal("-tao_nls_imfac", "initial merit factor", "", nlsP->imfac, &nlsP->imfac, NULL));
    744:   PetscCall(PetscOptionsReal("-tao_nls_pmin", "minimum perturbation", "", nlsP->pmin, &nlsP->pmin, NULL));
    745:   PetscCall(PetscOptionsReal("-tao_nls_pmax", "maximum perturbation", "", nlsP->pmax, &nlsP->pmax, NULL));
    746:   PetscCall(PetscOptionsReal("-tao_nls_pgfac", "growth factor", "", nlsP->pgfac, &nlsP->pgfac, NULL));
    747:   PetscCall(PetscOptionsReal("-tao_nls_psfac", "shrink factor", "", nlsP->psfac, &nlsP->psfac, NULL));
    748:   PetscCall(PetscOptionsReal("-tao_nls_pmgfac", "merit growth factor", "", nlsP->pmgfac, &nlsP->pmgfac, NULL));
    749:   PetscCall(PetscOptionsReal("-tao_nls_pmsfac", "merit shrink factor", "", nlsP->pmsfac, &nlsP->pmsfac, NULL));
    750:   PetscCall(PetscOptionsReal("-tao_nls_eta1", "poor steplength; reduce radius", "", nlsP->eta1, &nlsP->eta1, NULL));
    751:   PetscCall(PetscOptionsReal("-tao_nls_eta2", "reasonable steplength; leave radius alone", "", nlsP->eta2, &nlsP->eta2, NULL));
    752:   PetscCall(PetscOptionsReal("-tao_nls_eta3", "good steplength; increase radius", "", nlsP->eta3, &nlsP->eta3, NULL));
    753:   PetscCall(PetscOptionsReal("-tao_nls_eta4", "excellent steplength; greatly increase radius", "", nlsP->eta4, &nlsP->eta4, NULL));
    754:   PetscCall(PetscOptionsReal("-tao_nls_alpha1", "", "", nlsP->alpha1, &nlsP->alpha1, NULL));
    755:   PetscCall(PetscOptionsReal("-tao_nls_alpha2", "", "", nlsP->alpha2, &nlsP->alpha2, NULL));
    756:   PetscCall(PetscOptionsReal("-tao_nls_alpha3", "", "", nlsP->alpha3, &nlsP->alpha3, NULL));
    757:   PetscCall(PetscOptionsReal("-tao_nls_alpha4", "", "", nlsP->alpha4, &nlsP->alpha4, NULL));
    758:   PetscCall(PetscOptionsReal("-tao_nls_alpha5", "", "", nlsP->alpha5, &nlsP->alpha5, NULL));
    759:   PetscCall(PetscOptionsReal("-tao_nls_nu1", "poor steplength; reduce radius", "", nlsP->nu1, &nlsP->nu1, NULL));
    760:   PetscCall(PetscOptionsReal("-tao_nls_nu2", "reasonable steplength; leave radius alone", "", nlsP->nu2, &nlsP->nu2, NULL));
    761:   PetscCall(PetscOptionsReal("-tao_nls_nu3", "good steplength; increase radius", "", nlsP->nu3, &nlsP->nu3, NULL));
    762:   PetscCall(PetscOptionsReal("-tao_nls_nu4", "excellent steplength; greatly increase radius", "", nlsP->nu4, &nlsP->nu4, NULL));
    763:   PetscCall(PetscOptionsReal("-tao_nls_omega1", "", "", nlsP->omega1, &nlsP->omega1, NULL));
    764:   PetscCall(PetscOptionsReal("-tao_nls_omega2", "", "", nlsP->omega2, &nlsP->omega2, NULL));
    765:   PetscCall(PetscOptionsReal("-tao_nls_omega3", "", "", nlsP->omega3, &nlsP->omega3, NULL));
    766:   PetscCall(PetscOptionsReal("-tao_nls_omega4", "", "", nlsP->omega4, &nlsP->omega4, NULL));
    767:   PetscCall(PetscOptionsReal("-tao_nls_omega5", "", "", nlsP->omega5, &nlsP->omega5, NULL));
    768:   PetscCall(PetscOptionsReal("-tao_nls_mu1_i", "", "", nlsP->mu1_i, &nlsP->mu1_i, NULL));
    769:   PetscCall(PetscOptionsReal("-tao_nls_mu2_i", "", "", nlsP->mu2_i, &nlsP->mu2_i, NULL));
    770:   PetscCall(PetscOptionsReal("-tao_nls_gamma1_i", "", "", nlsP->gamma1_i, &nlsP->gamma1_i, NULL));
    771:   PetscCall(PetscOptionsReal("-tao_nls_gamma2_i", "", "", nlsP->gamma2_i, &nlsP->gamma2_i, NULL));
    772:   PetscCall(PetscOptionsReal("-tao_nls_gamma3_i", "", "", nlsP->gamma3_i, &nlsP->gamma3_i, NULL));
    773:   PetscCall(PetscOptionsReal("-tao_nls_gamma4_i", "", "", nlsP->gamma4_i, &nlsP->gamma4_i, NULL));
    774:   PetscCall(PetscOptionsReal("-tao_nls_theta_i", "", "", nlsP->theta_i, &nlsP->theta_i, NULL));
    775:   PetscCall(PetscOptionsReal("-tao_nls_mu1", "", "", nlsP->mu1, &nlsP->mu1, NULL));
    776:   PetscCall(PetscOptionsReal("-tao_nls_mu2", "", "", nlsP->mu2, &nlsP->mu2, NULL));
    777:   PetscCall(PetscOptionsReal("-tao_nls_gamma1", "", "", nlsP->gamma1, &nlsP->gamma1, NULL));
    778:   PetscCall(PetscOptionsReal("-tao_nls_gamma2", "", "", nlsP->gamma2, &nlsP->gamma2, NULL));
    779:   PetscCall(PetscOptionsReal("-tao_nls_gamma3", "", "", nlsP->gamma3, &nlsP->gamma3, NULL));
    780:   PetscCall(PetscOptionsReal("-tao_nls_gamma4", "", "", nlsP->gamma4, &nlsP->gamma4, NULL));
    781:   PetscCall(PetscOptionsReal("-tao_nls_theta", "", "", nlsP->theta, &nlsP->theta, NULL));
    782:   PetscCall(PetscOptionsReal("-tao_nls_min_radius", "lower bound on initial radius", "", nlsP->min_radius, &nlsP->min_radius, NULL));
    783:   PetscCall(PetscOptionsReal("-tao_nls_max_radius", "upper bound on radius", "", nlsP->max_radius, &nlsP->max_radius, NULL));
    784:   PetscCall(PetscOptionsReal("-tao_nls_epsilon", "tolerance used when computing actual and predicted reduction", "", nlsP->epsilon, &nlsP->epsilon, NULL));
    785:   PetscOptionsHeadEnd();
    786:   PetscCall(TaoLineSearchSetFromOptions(tao->linesearch));
    787:   PetscCall(KSPSetFromOptions(tao->ksp));
    788:   PetscFunctionReturn(PETSC_SUCCESS);
    789: }

    791: /*------------------------------------------------------------*/
    792: static PetscErrorCode TaoView_NLS(Tao tao, PetscViewer viewer)
    793: {
    794:   TAO_NLS  *nlsP = (TAO_NLS *)tao->data;
    795:   PetscBool isascii;

    797:   PetscFunctionBegin;
    798:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
    799:   if (isascii) {
    800:     PetscCall(PetscViewerASCIIPushTab(viewer));
    801:     PetscCall(PetscViewerASCIIPrintf(viewer, "Newton steps: %" PetscInt_FMT "\n", nlsP->newt));
    802:     PetscCall(PetscViewerASCIIPrintf(viewer, "BFGS steps: %" PetscInt_FMT "\n", nlsP->bfgs));
    803:     PetscCall(PetscViewerASCIIPrintf(viewer, "Gradient steps: %" PetscInt_FMT "\n", nlsP->grad));

    805:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp atol: %" PetscInt_FMT "\n", nlsP->ksp_atol));
    806:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp rtol: %" PetscInt_FMT "\n", nlsP->ksp_rtol));
    807:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp ctol: %" PetscInt_FMT "\n", nlsP->ksp_ctol));
    808:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp negc: %" PetscInt_FMT "\n", nlsP->ksp_negc));
    809:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp dtol: %" PetscInt_FMT "\n", nlsP->ksp_dtol));
    810:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp iter: %" PetscInt_FMT "\n", nlsP->ksp_iter));
    811:     PetscCall(PetscViewerASCIIPrintf(viewer, "nls ksp othr: %" PetscInt_FMT "\n", nlsP->ksp_othr));
    812:     PetscCall(PetscViewerASCIIPopTab(viewer));
    813:   }
    814:   PetscFunctionReturn(PETSC_SUCCESS);
    815: }

    817: /* ---------------------------------------------------------- */
    818: /*MC
    819:   TAONLS - Newton's method with linesearch for unconstrained minimization.
    820:   At each iteration, the Newton line search method solves the symmetric
    821:   system of equations to obtain the step direction dk:
    822:               Hk dk = -gk
    823:   a More-Thuente line search is applied on the direction dk to approximately
    824:   solve
    825:               min_t f(xk + t d_k)

    827:     Options Database Keys:
    828: + -tao_nls_init_type - "constant","direction","interpolation"
    829: . -tao_nls_update_type - "step","direction","interpolation"
    830: . -tao_nls_sval - perturbation starting value
    831: . -tao_nls_imin - minimum initial perturbation
    832: . -tao_nls_imax - maximum initial perturbation
    833: . -tao_nls_pmin - minimum perturbation
    834: . -tao_nls_pmax - maximum perturbation
    835: . -tao_nls_pgfac - growth factor
    836: . -tao_nls_psfac - shrink factor
    837: . -tao_nls_imfac - initial merit factor
    838: . -tao_nls_pmgfac - merit growth factor
    839: . -tao_nls_pmsfac - merit shrink factor
    840: . -tao_nls_eta1 - poor steplength; reduce radius
    841: . -tao_nls_eta2 - reasonable steplength; leave radius
    842: . -tao_nls_eta3 - good steplength; increase radius
    843: . -tao_nls_eta4 - excellent steplength; greatly increase radius
    844: . -tao_nls_alpha1 - alpha1 reduction
    845: . -tao_nls_alpha2 - alpha2 reduction
    846: . -tao_nls_alpha3 - alpha3 reduction
    847: . -tao_nls_alpha4 - alpha4 reduction
    848: . -tao_nls_alpha - alpha5 reduction
    849: . -tao_nls_mu1 - mu1 interpolation update
    850: . -tao_nls_mu2 - mu2 interpolation update
    851: . -tao_nls_gamma1 - gamma1 interpolation update
    852: . -tao_nls_gamma2 - gamma2 interpolation update
    853: . -tao_nls_gamma3 - gamma3 interpolation update
    854: . -tao_nls_gamma4 - gamma4 interpolation update
    855: . -tao_nls_theta - theta interpolation update
    856: . -tao_nls_omega1 - omega1 step update
    857: . -tao_nls_omega2 - omega2 step update
    858: . -tao_nls_omega3 - omega3 step update
    859: . -tao_nls_omega4 - omega4 step update
    860: . -tao_nls_omega5 - omega5 step update
    861: . -tao_nls_mu1_i -  mu1 interpolation init factor
    862: . -tao_nls_mu2_i -  mu2 interpolation init factor
    863: . -tao_nls_gamma1_i -  gamma1 interpolation init factor
    864: . -tao_nls_gamma2_i -  gamma2 interpolation init factor
    865: . -tao_nls_gamma3_i -  gamma3 interpolation init factor
    866: . -tao_nls_gamma4_i -  gamma4 interpolation init factor
    867: - -tao_nls_theta_i -  theta interpolation init factor

    869:   Level: beginner
    870: M*/

    872: PETSC_EXTERN PetscErrorCode TaoCreate_NLS(Tao tao)
    873: {
    874:   TAO_NLS    *nlsP;
    875:   const char *morethuente_type = TAOLINESEARCHMT;

    877:   PetscFunctionBegin;
    878:   PetscCall(PetscNew(&nlsP));

    880:   tao->ops->setup          = TaoSetUp_NLS;
    881:   tao->ops->solve          = TaoSolve_NLS;
    882:   tao->ops->view           = TaoView_NLS;
    883:   tao->ops->setfromoptions = TaoSetFromOptions_NLS;
    884:   tao->ops->destroy        = TaoDestroy_NLS;

    886:   /* Override default settings (unless already changed) */
    887:   PetscCall(TaoParametersInitialize(tao));
    888:   PetscObjectParameterSetDefault(tao, max_it, 50);
    889:   PetscObjectParameterSetDefault(tao, trust0, 100.0);

    891:   tao->data = (void *)nlsP;

    893:   nlsP->sval  = 0.0;
    894:   nlsP->imin  = 1.0e-4;
    895:   nlsP->imax  = 1.0e+2;
    896:   nlsP->imfac = 1.0e-1;

    898:   nlsP->pmin   = 1.0e-12;
    899:   nlsP->pmax   = 1.0e+2;
    900:   nlsP->pgfac  = 1.0e+1;
    901:   nlsP->psfac  = 4.0e-1;
    902:   nlsP->pmgfac = 1.0e-1;
    903:   nlsP->pmsfac = 1.0e-1;

    905:   /*  Default values for trust-region radius update based on steplength */
    906:   nlsP->nu1 = 0.25;
    907:   nlsP->nu2 = 0.50;
    908:   nlsP->nu3 = 1.00;
    909:   nlsP->nu4 = 1.25;

    911:   nlsP->omega1 = 0.25;
    912:   nlsP->omega2 = 0.50;
    913:   nlsP->omega3 = 1.00;
    914:   nlsP->omega4 = 2.00;
    915:   nlsP->omega5 = 4.00;

    917:   /*  Default values for trust-region radius update based on reduction */
    918:   nlsP->eta1 = 1.0e-4;
    919:   nlsP->eta2 = 0.25;
    920:   nlsP->eta3 = 0.50;
    921:   nlsP->eta4 = 0.90;

    923:   nlsP->alpha1 = 0.25;
    924:   nlsP->alpha2 = 0.50;
    925:   nlsP->alpha3 = 1.00;
    926:   nlsP->alpha4 = 2.00;
    927:   nlsP->alpha5 = 4.00;

    929:   /*  Default values for trust-region radius update based on interpolation */
    930:   nlsP->mu1 = 0.10;
    931:   nlsP->mu2 = 0.50;

    933:   nlsP->gamma1 = 0.25;
    934:   nlsP->gamma2 = 0.50;
    935:   nlsP->gamma3 = 2.00;
    936:   nlsP->gamma4 = 4.00;

    938:   nlsP->theta = 0.05;

    940:   /*  Default values for trust region initialization based on interpolation */
    941:   nlsP->mu1_i = 0.35;
    942:   nlsP->mu2_i = 0.50;

    944:   nlsP->gamma1_i = 0.0625;
    945:   nlsP->gamma2_i = 0.5;
    946:   nlsP->gamma3_i = 2.0;
    947:   nlsP->gamma4_i = 5.0;

    949:   nlsP->theta_i = 0.25;

    951:   /*  Remaining parameters */
    952:   nlsP->min_radius = 1.0e-10;
    953:   nlsP->max_radius = 1.0e10;
    954:   nlsP->epsilon    = 1.0e-6;

    956:   nlsP->init_type   = NLS_INIT_INTERPOLATION;
    957:   nlsP->update_type = NLS_UPDATE_STEP;

    959:   PetscCall(TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch));
    960:   PetscCall(PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1));
    961:   PetscCall(TaoLineSearchSetType(tao->linesearch, morethuente_type));
    962:   PetscCall(TaoLineSearchUseTaoRoutines(tao->linesearch, tao));
    963:   PetscCall(TaoLineSearchSetOptionsPrefix(tao->linesearch, tao->hdr.prefix));

    965:   /*  Set linear solver to default for symmetric matrices */
    966:   PetscCall(KSPCreate(((PetscObject)tao)->comm, &tao->ksp));
    967:   PetscCall(PetscObjectIncrementTabLevel((PetscObject)tao->ksp, (PetscObject)tao, 1));
    968:   PetscCall(KSPSetOptionsPrefix(tao->ksp, tao->hdr.prefix));
    969:   PetscCall(KSPAppendOptionsPrefix(tao->ksp, "tao_nls_"));
    970:   PetscCall(KSPSetType(tao->ksp, KSPSTCG));
    971:   PetscFunctionReturn(PETSC_SUCCESS);
    972: }