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: }