xref: /petsc/src/tao/bound/impls/bncg/bncg.c (revision 7a73cf095dfdbbe020b40cffddd741186a1539ca) 
1 #include <petsctaolinesearch.h>
2 #include <../src/tao/bound/impls/bncg/bncg.h>
3 
4 #define CG_FletcherReeves       0
5 #define CG_PolakRibiere         1
6 #define CG_PolakRibierePlus     2
7 #define CG_HestenesStiefel      3
8 #define CG_DaiYuan              4
9 #define CG_Types                5
10 
11 static const char *CG_Table[64] = {"fr", "pr", "prp", "hs", "dy"};
12 
13 #define CG_AS_NONE       0
14 #define CG_AS_BERTSEKAS  1
15 #define CG_AS_SIZE       2
16 
17 static const char *CG_AS_TYPE[64] = {"none", "bertsekas"};
18 
19 PetscErrorCode TaoBNCGSetRecycleFlag(Tao tao, PetscBool recycle)
20 {
21   TAO_BNCG                     *cg = (TAO_BNCG*)tao->data;
22 
23   PetscFunctionBegin;
24   cg->recycle = recycle;
25   PetscFunctionReturn(0);
26 }
27 
28 PetscErrorCode TaoBNCGEstimateActiveSet(Tao tao, PetscInt asType)
29 {
30   PetscErrorCode               ierr;
31   TAO_BNCG                     *cg = (TAO_BNCG *)tao->data;
32 
33   PetscFunctionBegin;
34   ierr = ISDestroy(&cg->inactive_old);CHKERRQ(ierr);
35   if (cg->inactive_idx) {
36     ierr = ISDuplicate(cg->inactive_idx, &cg->inactive_old);CHKERRQ(ierr);
37     ierr = ISCopy(cg->inactive_idx, cg->inactive_old);CHKERRQ(ierr);
38   }
39   switch (asType) {
40   case CG_AS_NONE:
41     ierr = ISDestroy(&cg->inactive_idx);CHKERRQ(ierr);
42     ierr = VecWhichInactive(tao->XL, tao->solution, cg->unprojected_gradient, tao->XU, PETSC_TRUE, &cg->inactive_idx);CHKERRQ(ierr);
43     ierr = ISDestroy(&cg->active_idx);CHKERRQ(ierr);
44     ierr = ISComplementVec(cg->inactive_idx, tao->solution, &cg->active_idx);CHKERRQ(ierr);
45     break;
46 
47   case CG_AS_BERTSEKAS:
48     /* Use gradient descent to estimate the active set */
49     ierr = VecCopy(cg->unprojected_gradient, cg->W);CHKERRQ(ierr);
50     ierr = VecScale(cg->W, -1.0);CHKERRQ(ierr);
51     ierr = TaoEstimateActiveBounds(tao->solution, tao->XL, tao->XU, cg->unprojected_gradient, cg->W, cg->work, cg->as_step, &cg->as_tol,
52                                    &cg->active_lower, &cg->active_upper, &cg->active_fixed, &cg->active_idx, &cg->inactive_idx);CHKERRQ(ierr);
53     break;
54 
55   default:
56     break;
57   }
58   PetscFunctionReturn(0);
59 }
60 
61 PetscErrorCode TaoBNCGBoundStep(Tao tao, PetscInt asType, Vec step)
62 {
63   PetscErrorCode               ierr;
64   TAO_BNCG                     *cg = (TAO_BNCG *)tao->data;
65 
66   PetscFunctionBegin;
67   switch (asType) {
68   case CG_AS_NONE:
69     ierr = VecISSet(step, cg->active_idx, 0.0);CHKERRQ(ierr);
70     break;
71 
72   case CG_AS_BERTSEKAS:
73     ierr = TaoBoundStep(tao->solution, tao->XL, tao->XU, cg->active_lower, cg->active_upper, cg->active_fixed, 1.0, step);CHKERRQ(ierr);
74     break;
75 
76   default:
77     break;
78   }
79   PetscFunctionReturn(0);
80 }
81 
82 static PetscErrorCode TaoSolve_BNCG(Tao tao)
83 {
84   TAO_BNCG                     *cg = (TAO_BNCG*)tao->data;
85   PetscErrorCode               ierr;
86   TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
87   PetscReal                    step=1.0,gnorm,gnorm2,gd,ginner,beta,dnorm;
88   PetscReal                    gd_old,gnorm2_old,f_old,resnorm;
89   PetscBool                    cg_restart;
90   PetscInt                     nDiff;
91 
92   PetscFunctionBegin;
93   /*   Project the current point onto the feasible set */
94   ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr);
95   ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr);
96 
97   /* Project the initial point onto the feasible region */
98   ierr = TaoBoundSolution(tao->solution, tao->XL,tao->XU, 0.0, &nDiff, tao->solution);CHKERRQ(ierr);
99 
100   if (nDiff > 0 || !cg->recycle) {
101     /*  Solver is not being recycled so just compute the objective function and criteria */
102     ierr = TaoComputeObjectiveAndGradient(tao, tao->solution, &cg->f, cg->unprojected_gradient);CHKERRQ(ierr);
103   } else {
104     /* We are recycling, so we have to compute ||g_old||^2 for use in the CG step calculation */
105     ierr = VecDot(cg->G_old, cg->G_old, &gnorm2_old);CHKERRQ(ierr);
106   }
107   ierr = VecNorm(cg->unprojected_gradient,NORM_2,&gnorm);CHKERRQ(ierr);
108   if (PetscIsInfOrNanReal(cg->f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
109 
110   /* Estimate the active set and compute the projected gradient */
111   ierr = TaoBNCGEstimateActiveSet(tao, cg->as_type);CHKERRQ(ierr);
112 
113   /* Project the gradient and calculate the norm */
114   ierr = VecCopy(cg->unprojected_gradient, tao->gradient);CHKERRQ(ierr);
115   ierr = VecISSet(tao->gradient, cg->active_idx, 0.0);CHKERRQ(ierr);
116   ierr = VecNorm(tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
117   gnorm2 = gnorm*gnorm;
118 
119   /* Convergence check */
120   tao->niter = 0;
121   tao->reason = TAO_CONTINUE_ITERATING;
122   ierr = TaoLogConvergenceHistory(tao, cg->f, gnorm, 0.0, tao->ksp_its);CHKERRQ(ierr);
123   ierr = TaoMonitor(tao, tao->niter, cg->f, gnorm, 0.0, step);CHKERRQ(ierr);
124   ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
125   if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);
126 
127   /* Start optimization iterations */
128   cg->ls_fails = cg->broken_ortho = cg->descent_error = 0;
129   cg->resets = -1;
130   while (tao->reason == TAO_CONTINUE_ITERATING) {
131     ++tao->niter;
132 
133     /* Check restart conditions for using steepest descent */
134     cg_restart = PETSC_FALSE;
135     ierr = VecDot(tao->gradient, cg->G_old, &ginner);CHKERRQ(ierr);
136     ierr = VecNorm(tao->stepdirection, NORM_2, &dnorm);CHKERRQ(ierr);
137     if (tao->niter == 1 && !cg->recycle && dnorm != 0.0) {
138       /* 1) First iteration, with recycle disabled, and a non-zero previous step */
139       cg_restart = PETSC_TRUE;
140     } else if (PetscAbsScalar(ginner) >= cg->eta * gnorm2) {
141       /* 2) Gradients are far from orthogonal */
142       cg_restart = PETSC_TRUE;
143       ++cg->broken_ortho;
144     }
145 
146     /* Compute CG step */
147     if (cg_restart) {
148       beta = 0.0;
149       ++cg->resets;
150     } else {
151       switch (cg->cg_type) {
152       case CG_FletcherReeves:
153         beta = gnorm2 / gnorm2_old;
154         break;
155 
156       case CG_PolakRibiere:
157         beta = (gnorm2 - ginner) / gnorm2_old;
158         break;
159 
160       case CG_PolakRibierePlus:
161         beta = PetscMax((gnorm2-ginner)/gnorm2_old, 0.0);
162         break;
163 
164       case CG_HestenesStiefel:
165         ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
166         ierr = VecDot(cg->G_old, tao->stepdirection, &gd_old);CHKERRQ(ierr);
167         beta = (gnorm2 - ginner) / (gd - gd_old);
168         break;
169 
170       case CG_DaiYuan:
171         ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
172         ierr = VecDot(cg->G_old, tao->stepdirection, &gd_old);CHKERRQ(ierr);
173         beta = gnorm2 / (gd - gd_old);
174         break;
175 
176       default:
177         beta = 0.0;
178         break;
179       }
180     }
181 
182     /*  Compute the direction d=-g + beta*d */
183     ierr = VecAXPBY(tao->stepdirection, -1.0, beta, tao->gradient);CHKERRQ(ierr);
184     ierr = TaoBNCGBoundStep(tao, cg->as_type, tao->stepdirection);CHKERRQ(ierr);
185 
186     /* Figure out which previously active variables became inactive this iteration */
187     ierr = ISDestroy(&cg->new_inactives);CHKERRQ(ierr);
188     if (cg->inactive_idx && cg->inactive_old) {
189       ierr = ISDifference(cg->inactive_idx, cg->inactive_old, &cg->new_inactives);CHKERRQ(ierr);
190     }
191 
192     /* Selectively reset the CG step those freshly inactive variables */
193     if (cg->new_inactives) {
194       ierr = VecGetSubVector(tao->stepdirection, cg->new_inactives, &cg->inactive_step);CHKERRQ(ierr);
195       ierr = VecGetSubVector(cg->unprojected_gradient, cg->new_inactives, &cg->inactive_grad);CHKERRQ(ierr);
196       ierr = VecCopy(cg->inactive_grad, cg->inactive_step);CHKERRQ(ierr);
197       ierr = VecScale(cg->inactive_step, -1.0);CHKERRQ(ierr);
198       ierr = VecRestoreSubVector(tao->stepdirection, cg->new_inactives, &cg->inactive_step);CHKERRQ(ierr);
199       ierr = VecRestoreSubVector(cg->unprojected_gradient, cg->new_inactives, &cg->inactive_grad);CHKERRQ(ierr);
200     }
201 
202     /* Verify that this is a descent direction */
203     ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
204     ierr = VecNorm(tao->stepdirection, NORM_2, &dnorm);CHKERRQ(ierr);
205     if (gd > -cg->rho*PetscPowReal(dnorm, cg->pow)) {
206       /* Not a descent direction, so we reset back to projected gradient descent */
207       ierr = VecAXPBY(tao->stepdirection, -1.0, 0.0, tao->gradient);CHKERRQ(ierr);
208       ++cg->resets;
209       ++cg->descent_error;
210     }
211 
212     /* Store solution and gradient info before it changes */
213     ierr = VecCopy(tao->solution, cg->X_old);CHKERRQ(ierr);
214     ierr = VecCopy(tao->gradient, cg->G_old);CHKERRQ(ierr);
215     ierr = VecCopy(cg->unprojected_gradient, cg->unprojected_gradient_old);CHKERRQ(ierr);
216     gnorm2_old = gnorm2;
217     f_old = cg->f;
218 
219     /* Perform bounded line search */
220     ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &cg->f, cg->unprojected_gradient, tao->stepdirection, &step, &ls_status);CHKERRQ(ierr);
221     ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
222 
223     /*  Check linesearch failure */
224     if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
225       ++cg->ls_fails;
226       /* Restore previous point */
227       gnorm2 = gnorm2_old;
228       cg->f = f_old;
229       ierr = VecCopy(cg->X_old, tao->solution);CHKERRQ(ierr);
230       ierr = VecCopy(cg->G_old, tao->gradient);CHKERRQ(ierr);
231       ierr = VecCopy(cg->unprojected_gradient_old, cg->unprojected_gradient);CHKERRQ(ierr);
232 
233       /* Fall back on the gradient descent step */
234       ierr = VecCopy(tao->gradient, tao->stepdirection);CHKERRQ(ierr);
235       ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
236       ierr = TaoBNCGBoundStep(tao, cg->as_type, tao->stepdirection);CHKERRQ(ierr);
237       ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &cg->f, cg->unprojected_gradient, tao->stepdirection, &step, &ls_status);CHKERRQ(ierr);
238       ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
239 
240       if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER){
241         ++cg->ls_fails;
242         /* Restore previous point */
243         gnorm2 = gnorm2_old;
244         cg->f = f_old;
245         ierr = VecCopy(cg->X_old, tao->solution);CHKERRQ(ierr);
246         ierr = VecCopy(cg->G_old, tao->gradient);CHKERRQ(ierr);
247         ierr = VecCopy(cg->unprojected_gradient_old, cg->unprojected_gradient);CHKERRQ(ierr);
248 
249         /* Nothing left to do but fail out of the optimization */
250         step = 0.0;
251         tao->reason = TAO_DIVERGED_LS_FAILURE;
252       }
253     }
254 
255     if (tao->reason != TAO_DIVERGED_LS_FAILURE) {
256       /* Estimate the active set at the new solution */
257       ierr = TaoBNCGEstimateActiveSet(tao, cg->as_type);CHKERRQ(ierr);
258 
259       /* Compute the projected gradient and its norm */
260       ierr = VecCopy(cg->unprojected_gradient, tao->gradient);CHKERRQ(ierr);
261       ierr = VecISSet(tao->gradient, cg->active_idx, 0.0);CHKERRQ(ierr);
262       ierr = VecNorm(tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
263       gnorm2 = gnorm*gnorm;
264     }
265 
266     /* Convergence test */
267     ierr = VecFischer(tao->solution, cg->unprojected_gradient, tao->XL, tao->XU, cg->W);CHKERRQ(ierr);
268     ierr = VecNorm(cg->W, NORM_2, &resnorm);CHKERRQ(ierr);
269     if (PetscIsInfOrNanReal(resnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
270     ierr = TaoLogConvergenceHistory(tao, cg->f, resnorm, 0.0, tao->ksp_its);CHKERRQ(ierr);
271     ierr = TaoMonitor(tao, tao->niter, cg->f, resnorm, 0.0, step);CHKERRQ(ierr);
272     ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
273   }
274   PetscFunctionReturn(0);
275 }
276 
277 static PetscErrorCode TaoSetUp_BNCG(Tao tao)
278 {
279   TAO_BNCG         *cg = (TAO_BNCG*)tao->data;
280   PetscErrorCode ierr;
281 
282   PetscFunctionBegin;
283   if (!tao->gradient) {
284     ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr);
285   }
286   if (!tao->stepdirection) {
287     ierr = VecDuplicate(tao->solution,&tao->stepdirection);CHKERRQ(ierr);
288   }
289   if (!cg->W) {
290     ierr = VecDuplicate(tao->solution,&cg->W);CHKERRQ(ierr);
291   }
292   if (!cg->work) {
293     ierr = VecDuplicate(tao->solution,&cg->work);CHKERRQ(ierr);
294   }
295   if (!cg->X_old) {
296     ierr = VecDuplicate(tao->solution,&cg->X_old);CHKERRQ(ierr);
297   }
298   if (!cg->G_old) {
299     ierr = VecDuplicate(tao->gradient,&cg->G_old);CHKERRQ(ierr);
300   }
301   if (!cg->unprojected_gradient) {
302     ierr = VecDuplicate(tao->gradient,&cg->unprojected_gradient);CHKERRQ(ierr);
303   }
304   if (!cg->unprojected_gradient_old) {
305     ierr = VecDuplicate(tao->gradient,&cg->unprojected_gradient_old);CHKERRQ(ierr);
306   }
307   PetscFunctionReturn(0);
308 }
309 
310 static PetscErrorCode TaoDestroy_BNCG(Tao tao)
311 {
312   TAO_BNCG       *cg = (TAO_BNCG*) tao->data;
313   PetscErrorCode ierr;
314 
315   PetscFunctionBegin;
316   if (tao->setupcalled) {
317     ierr = VecDestroy(&cg->W);CHKERRQ(ierr);
318     ierr = VecDestroy(&cg->work);CHKERRQ(ierr);
319     ierr = VecDestroy(&cg->X_old);CHKERRQ(ierr);
320     ierr = VecDestroy(&cg->G_old);CHKERRQ(ierr);
321     ierr = VecDestroy(&cg->unprojected_gradient);CHKERRQ(ierr);
322     ierr = VecDestroy(&cg->unprojected_gradient_old);CHKERRQ(ierr);
323   }
324   ierr = ISDestroy(&cg->active_lower);CHKERRQ(ierr);
325   ierr = ISDestroy(&cg->active_upper);CHKERRQ(ierr);
326   ierr = ISDestroy(&cg->active_fixed);CHKERRQ(ierr);
327   ierr = ISDestroy(&cg->active_idx);CHKERRQ(ierr);
328   ierr = ISDestroy(&cg->inactive_idx);CHKERRQ(ierr);
329   ierr = ISDestroy(&cg->inactive_old);CHKERRQ(ierr);
330   ierr = ISDestroy(&cg->new_inactives);CHKERRQ(ierr);
331   ierr = PetscFree(tao->data);CHKERRQ(ierr);
332   PetscFunctionReturn(0);
333 }
334 
335 static PetscErrorCode TaoSetFromOptions_BNCG(PetscOptionItems *PetscOptionsObject,Tao tao)
336  {
337     TAO_BNCG       *cg = (TAO_BNCG*)tao->data;
338     PetscErrorCode ierr;
339 
340     PetscFunctionBegin;
341     ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
342     ierr = PetscOptionsHead(PetscOptionsObject,"Nonlinear Conjugate Gradient method for unconstrained optimization");CHKERRQ(ierr);
343     ierr = PetscOptionsReal("-tao_bncg_eta","restart tolerance", "", cg->eta,&cg->eta,NULL);CHKERRQ(ierr);
344     ierr = PetscOptionsReal("-tao_bncg_rho","descent direction tolerance", "", cg->rho,&cg->rho,NULL);CHKERRQ(ierr);
345     ierr = PetscOptionsReal("-tao_bncg_pow","descent direction exponent", "", cg->pow,&cg->pow,NULL);CHKERRQ(ierr);
346     ierr = PetscOptionsEList("-tao_bncg_type","cg formula", "", CG_Table, CG_Types, CG_Table[cg->cg_type], &cg->cg_type,NULL);CHKERRQ(ierr);
347     ierr = PetscOptionsEList("-tao_bncg_as_type","active set estimation method", "", CG_AS_TYPE, CG_AS_SIZE, CG_AS_TYPE[cg->cg_type], &cg->cg_type,NULL);CHKERRQ(ierr);
348     ierr = PetscOptionsBool("-tao_bncg_recycle","enable recycling the existing solution and gradient at the start of a new solve","",cg->recycle,&cg->recycle,NULL);CHKERRQ(ierr);
349     ierr = PetscOptionsReal("-tao_bncg_as_tol", "initial tolerance used when estimating actively bounded variables","",cg->as_tol,&cg->as_tol,NULL);CHKERRQ(ierr);
350     ierr = PetscOptionsReal("-tao_bncg_as_step", "step length used when estimating actively bounded variables","",cg->as_step,&cg->as_step,NULL);CHKERRQ(ierr);
351    ierr = PetscOptionsTail();CHKERRQ(ierr);
352    PetscFunctionReturn(0);
353 }
354 
355 static PetscErrorCode TaoView_BNCG(Tao tao, PetscViewer viewer)
356 {
357   PetscBool      isascii;
358   TAO_BNCG       *cg = (TAO_BNCG*)tao->data;
359   PetscErrorCode ierr;
360 
361   PetscFunctionBegin;
362   ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr);
363   if (isascii) {
364     ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr);
365     ierr = PetscViewerASCIIPrintf(viewer, "CG Type: %s\n", CG_Table[cg->cg_type]);CHKERRQ(ierr);
366     ierr = PetscViewerASCIIPrintf(viewer, "Resets: %i\n", cg->resets);CHKERRQ(ierr);
367     ierr = PetscViewerASCIIPrintf(viewer, "  Broken ortho: %i\n", cg->broken_ortho);CHKERRQ(ierr);
368     ierr = PetscViewerASCIIPrintf(viewer, "  Not a descent dir.: %i\n", cg->descent_error);CHKERRQ(ierr);
369     ierr = PetscViewerASCIIPrintf(viewer, "Line search fails: %i\n", cg->ls_fails);CHKERRQ(ierr);
370     ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr);
371   }
372   PetscFunctionReturn(0);
373 }
374 
375 /*MC
376      TAOBNCG -   Bound-constrained Nonlinear Conjugate Gradient method.
377 
378    Options Database Keys:
379 +      -tao_bncg_recycle - enable recycling the latest calculated gradient vector in subsequent TaoSolve() calls
380 .      -tao_bncg_eta <r> - restart tolerance
381 .      -tao_bncg_type <taocg_type> - cg formula
382 .      -tao_bncg_as_type <none,bertsekas> - active set estimation method
383 .      -tao_bncg_as_tol <r> - tolerance used in Bertsekas active-set estimation
384 .      -tao_bncg_as_step <r> - trial step length used in Bertsekas active-set estimation
385 
386   Notes:
387      CG formulas are:
388          "fr" - Fletcher-Reeves
389          "pr" - Polak-Ribiere
390          "prp" - Polak-Ribiere-Plus
391          "hs" - Hestenes-Steifel
392          "dy" - Dai-Yuan
393   Level: beginner
394 M*/
395 
396 
397 PETSC_EXTERN PetscErrorCode TaoCreate_BNCG(Tao tao)
398 {
399   TAO_BNCG       *cg;
400   const char     *morethuente_type = TAOLINESEARCHMT;
401   PetscErrorCode ierr;
402 
403   PetscFunctionBegin;
404   tao->ops->setup = TaoSetUp_BNCG;
405   tao->ops->solve = TaoSolve_BNCG;
406   tao->ops->view = TaoView_BNCG;
407   tao->ops->setfromoptions = TaoSetFromOptions_BNCG;
408   tao->ops->destroy = TaoDestroy_BNCG;
409 
410   /* Override default settings (unless already changed) */
411   if (!tao->max_it_changed) tao->max_it = 2000;
412   if (!tao->max_funcs_changed) tao->max_funcs = 4000;
413 
414   /*  Note: nondefault values should be used for nonlinear conjugate gradient  */
415   /*  method.  In particular, gtol should be less that 0.5; the value used in  */
416   /*  Nocedal and Wright is 0.10.  We use the default values for the  */
417   /*  linesearch because it seems to work better. */
418   ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
419   ierr = PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);CHKERRQ(ierr);
420   ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr);
421   ierr = TaoLineSearchUseTaoRoutines(tao->linesearch, tao);CHKERRQ(ierr);
422   ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr);
423 
424   ierr = PetscNewLog(tao,&cg);CHKERRQ(ierr);
425   tao->data = (void*)cg;
426   cg->rho = 1e-4;
427   cg->pow = 2.1;
428   cg->eta = 0.5;
429   cg->as_step = 0.001;
430   cg->as_tol = 0.001;
431   cg->as_type = CG_AS_BERTSEKAS;
432   cg->cg_type = CG_DaiYuan;
433   cg->recycle = PETSC_FALSE;
434   PetscFunctionReturn(0);
435 }
436