Actual source code: armijo.c
petsc-3.6.4 2016-04-12
1: #include <petsc/private/taolinesearchimpl.h>
2: #include <../src/tao/linesearch/impls/armijo/armijo.h>
4: #define REPLACE_FIFO 1
5: #define REPLACE_MRU 2
7: #define REFERENCE_MAX 1
8: #define REFERENCE_AVE 2
9: #define REFERENCE_MEAN 3
13: static PetscErrorCode TaoLineSearchDestroy_Armijo(TaoLineSearch ls)
14: {
15: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
16: PetscErrorCode ierr;
19: PetscFree(armP->memory);
20: VecDestroy(&armP->x);
21: VecDestroy(&armP->work);
22: PetscFree(ls->data);
23: return(0);
24: }
28: static PetscErrorCode TaoLineSearchReset_Armijo(TaoLineSearch ls)
29: {
30: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
31: PetscErrorCode ierr;
34: if (armP->memory != NULL) {
35: PetscFree(armP->memory);
36: }
37: armP->memorySetup = PETSC_FALSE;
38: return(0);
39: }
43: static PetscErrorCode TaoLineSearchSetFromOptions_Armijo(PetscOptions *PetscOptionsObject,TaoLineSearch ls)
44: {
45: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
46: PetscErrorCode ierr;
49: PetscOptionsHead(PetscOptionsObject,"Armijo linesearch options");
50: PetscOptionsReal("-tao_ls_armijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha,NULL);
51: PetscOptionsReal("-tao_ls_armijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf,NULL);
52: PetscOptionsReal("-tao_ls_armijo_beta", "decrease constant", "", armP->beta, &armP->beta,NULL);
53: PetscOptionsReal("-tao_ls_armijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma,NULL);
54: PetscOptionsInt("-tao_ls_armijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize,NULL);
55: PetscOptionsInt("-tao_ls_armijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy,NULL);
56: PetscOptionsInt("-tao_ls_armijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy,NULL);
57: PetscOptionsBool("-tao_ls_armijo_nondescending","Use nondescending armijo algorithm","",armP->nondescending,&armP->nondescending,NULL);
58: PetscOptionsTail();
59: return(0);
60: }
64: static PetscErrorCode TaoLineSearchView_Armijo(TaoLineSearch ls, PetscViewer pv)
65: {
66: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
67: PetscBool isascii;
68: PetscErrorCode ierr;
71: PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
72: if (isascii) {
73: PetscViewerASCIIPrintf(pv," maxf=%D, ftol=%g, gtol=%g\n",ls->max_funcs, (double)ls->rtol, (double)ls->ftol);
74: ierr=PetscViewerASCIIPrintf(pv," Armijo linesearch",armP->alpha);
75: if (armP->nondescending) {
76: PetscViewerASCIIPrintf(pv, " (nondescending)");
77: }
78: if (ls->bounded) {
79: PetscViewerASCIIPrintf(pv," (projected)");
80: }
81: ierr=PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
82: ierr=PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
83: ierr=PetscViewerASCIIPrintf(pv,"memsize=%D\n",armP->memorySize);
84: }
85: return(0);
86: }
90: /* @ TaoApply_Armijo - This routine performs a linesearch. It
91: backtracks until the (nonmonotone) Armijo conditions are satisfied.
93: Input Parameters:
94: + tao - Tao context
95: . X - current iterate (on output X contains new iterate, X + step*S)
96: . S - search direction
97: . f - merit function evaluated at X
98: . G - gradient of merit function evaluated at X
99: . W - work vector
100: - step - initial estimate of step length
102: Output parameters:
103: + f - merit function evaluated at new iterate, X + step*S
104: . G - gradient of merit function evaluated at new iterate, X + step*S
105: . X - new iterate
106: - step - final step length
108: @ */
109: static PetscErrorCode TaoLineSearchApply_Armijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
110: {
111: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
112: PetscErrorCode ierr;
113: PetscInt i;
114: PetscReal fact, ref, gdx;
115: PetscInt idx;
116: PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
120: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
121: if (!armP->work) {
122: VecDuplicate(x,&armP->work);
123: armP->x = x;
124: PetscObjectReference((PetscObject)armP->x);
125: } else if (x != armP->x) {
126: /* If x has changed, then recreate work */
127: VecDestroy(&armP->work);
128: VecDuplicate(x,&armP->work);
129: PetscObjectDereference((PetscObject)armP->x);
130: armP->x = x;
131: PetscObjectReference((PetscObject)armP->x);
132: }
134: /* Check linesearch parameters */
135: if (armP->alpha < 1) {
136: PetscInfo1(ls,"Armijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
137: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
138: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
139: PetscInfo1(ls,"Armijo line search error: beta (%g) invalid\n", (double)armP->beta);
140: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
141: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
142: PetscInfo1(ls,"Armijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
143: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
144: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
145: PetscInfo1(ls,"Armijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
146: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
147: } else if (armP->memorySize < 1) {
148: PetscInfo1(ls,"Armijo line search error: memory_size (%D) < 1\n", armP->memorySize);
149: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
150: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
151: PetscInfo(ls,"Armijo line search error: reference_policy invalid\n");
152: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
153: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
154: PetscInfo(ls,"Armijo line search error: replacement_policy invalid\n");
155: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
156: } else if (PetscIsInfOrNanReal(*f)) {
157: PetscInfo(ls,"Armijo line search error: initial function inf or nan\n");
158: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
159: }
161: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) {
162: return(0);
163: }
165: /* Check to see of the memory has been allocated. If not, allocate
166: the historical array and populate it with the initial function
167: values. */
168: if (!armP->memory) {
169: PetscMalloc1(armP->memorySize, &armP->memory );
170: }
172: if (!armP->memorySetup) {
173: for (i = 0; i < armP->memorySize; i++) {
174: armP->memory[i] = armP->alpha*(*f);
175: }
177: armP->current = 0;
178: armP->lastReference = armP->memory[0];
179: armP->memorySetup=PETSC_TRUE;
180: }
182: /* Calculate reference value (MAX) */
183: ref = armP->memory[0];
184: idx = 0;
186: for (i = 1; i < armP->memorySize; i++) {
187: if (armP->memory[i] > ref) {
188: ref = armP->memory[i];
189: idx = i;
190: }
191: }
193: if (armP->referencePolicy == REFERENCE_AVE) {
194: ref = 0;
195: for (i = 0; i < armP->memorySize; i++) {
196: ref += armP->memory[i];
197: }
198: ref = ref / armP->memorySize;
199: ref = PetscMax(ref, armP->memory[armP->current]);
200: } else if (armP->referencePolicy == REFERENCE_MEAN) {
201: ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
202: }
203: VecDot(g,s,&gdx);
205: if (PetscIsInfOrNanReal(gdx)) {
206: PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
207: ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
208: return(0);
209: }
210: if (gdx >= 0.0) {
211: PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
212: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
213: return(0);
214: }
216: if (armP->nondescending) {
217: fact = armP->sigma;
218: } else {
219: fact = armP->sigma * gdx;
220: }
221: ls->step = ls->initstep;
222: while (ls->step >= ls->stepmin && (ls->nfeval+ls->nfgeval) < ls->max_funcs) {
223: /* Calculate iterate */
224: VecCopy(x,armP->work);
225: VecAXPY(armP->work,ls->step,s);
226: if (ls->bounded) {
227: VecMedian(ls->lower,armP->work,ls->upper,armP->work);
228: }
230: /* Calculate function at new iterate */
231: if (ls->hasobjective) {
232: TaoLineSearchComputeObjective(ls,armP->work,f);
233: g_computed=PETSC_FALSE;
234: } else if (ls->usegts) {
235: TaoLineSearchComputeObjectiveAndGTS(ls,armP->work,f,&gdx);
236: g_computed=PETSC_FALSE;
237: } else {
238: TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
239: g_computed=PETSC_TRUE;
240: }
241: if (ls->step == ls->initstep) {
242: ls->f_fullstep = *f;
243: }
245: if (PetscIsInfOrNanReal(*f)) {
246: ls->step *= armP->beta_inf;
247: } else {
248: /* Check descent condition */
249: if (armP->nondescending && *f <= ref - ls->step*fact*ref)
250: break;
251: if (!armP->nondescending && *f <= ref + ls->step*fact) {
252: break;
253: }
255: ls->step *= armP->beta;
256: }
257: }
259: /* Check termination */
260: if (PetscIsInfOrNanReal(*f)) {
261: PetscInfo(ls, "Function is inf or nan.\n");
262: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
263: } else if (ls->step < ls->stepmin) {
264: PetscInfo(ls, "Step length is below tolerance.\n");
265: ls->reason = TAOLINESEARCH_HALTED_RTOL;
266: } else if ((ls->nfeval+ls->nfgeval) >= ls->max_funcs) {
267: PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval+ls->nfgeval, ls->max_funcs);
268: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
269: }
270: if (ls->reason) {
271: return(0);
272: }
274: /* Successful termination, update memory */
275: ls->reason = TAOLINESEARCH_SUCCESS;
276: armP->lastReference = ref;
277: if (armP->replacementPolicy == REPLACE_FIFO) {
278: armP->memory[armP->current++] = *f;
279: if (armP->current >= armP->memorySize) {
280: armP->current = 0;
281: }
282: } else {
283: armP->current = idx;
284: armP->memory[idx] = *f;
285: }
287: /* Update iterate and compute gradient */
288: VecCopy(armP->work,x);
289: if (!g_computed) {
290: TaoLineSearchComputeGradient(ls, x, g);
291: }
292: PetscInfo2(ls, "%D function evals in line search, step = %g\n",ls->nfeval, (double)ls->step);
293: return(0);
294: }
298: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_Armijo(TaoLineSearch ls)
299: {
300: TaoLineSearch_ARMIJO *armP;
301: PetscErrorCode ierr;
305: PetscNewLog(ls,&armP);
307: armP->memory = NULL;
308: armP->alpha = 1.0;
309: armP->beta = 0.5;
310: armP->beta_inf = 0.5;
311: armP->sigma = 1e-4;
312: armP->memorySize = 1;
313: armP->referencePolicy = REFERENCE_MAX;
314: armP->replacementPolicy = REPLACE_MRU;
315: armP->nondescending=PETSC_FALSE;
316: ls->data = (void*)armP;
317: ls->initstep=1.0;
318: ls->ops->setup=0;
319: ls->ops->apply=TaoLineSearchApply_Armijo;
320: ls->ops->view = TaoLineSearchView_Armijo;
321: ls->ops->destroy = TaoLineSearchDestroy_Armijo;
322: ls->ops->reset = TaoLineSearchReset_Armijo;
323: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_Armijo;
324: return(0);
325: }