Actual source code: armijo.c
petsc-3.9.4 2018-09-11
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
11: static PetscErrorCode TaoLineSearchDestroy_Armijo(TaoLineSearch ls)
12: {
13: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
14: PetscErrorCode ierr;
17: PetscFree(armP->memory);
18: VecDestroy(&armP->x);
19: VecDestroy(&armP->work);
20: PetscFree(ls->data);
21: return(0);
22: }
24: static PetscErrorCode TaoLineSearchReset_Armijo(TaoLineSearch ls)
25: {
26: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
27: PetscErrorCode ierr;
30: PetscFree(armP->memory);
31: armP->memorySetup = PETSC_FALSE;
32: return(0);
33: }
35: static PetscErrorCode TaoLineSearchSetFromOptions_Armijo(PetscOptionItems *PetscOptionsObject,TaoLineSearch ls)
36: {
37: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
38: PetscErrorCode ierr;
41: PetscOptionsHead(PetscOptionsObject,"Armijo linesearch options");
42: PetscOptionsReal("-tao_ls_armijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha,NULL);
43: PetscOptionsReal("-tao_ls_armijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf,NULL);
44: PetscOptionsReal("-tao_ls_armijo_beta", "decrease constant", "", armP->beta, &armP->beta,NULL);
45: PetscOptionsReal("-tao_ls_armijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma,NULL);
46: PetscOptionsInt("-tao_ls_armijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize,NULL);
47: PetscOptionsInt("-tao_ls_armijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy,NULL);
48: PetscOptionsInt("-tao_ls_armijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy,NULL);
49: PetscOptionsBool("-tao_ls_armijo_nondescending","Use nondescending armijo algorithm","",armP->nondescending,&armP->nondescending,NULL);
50: PetscOptionsTail();
51: return(0);
52: }
54: static PetscErrorCode TaoLineSearchView_Armijo(TaoLineSearch ls, PetscViewer pv)
55: {
56: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
57: PetscBool isascii;
58: PetscErrorCode ierr;
61: PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
62: if (isascii) {
63: ierr=PetscViewerASCIIPrintf(pv," Armijo linesearch",armP->alpha);
64: if (armP->nondescending) {
65: PetscViewerASCIIPrintf(pv, " (nondescending)");
66: }
67: if (ls->bounded) {
68: PetscViewerASCIIPrintf(pv," (projected)");
69: }
70: ierr=PetscViewerASCIIPrintf(pv,": alpha=%g beta=%g ",(double)armP->alpha,(double)armP->beta);
71: ierr=PetscViewerASCIIPrintf(pv,"sigma=%g ",(double)armP->sigma);
72: ierr=PetscViewerASCIIPrintf(pv,"memsize=%D\n",armP->memorySize);
73: }
74: return(0);
75: }
77: /* @ TaoApply_Armijo - This routine performs a linesearch. It
78: backtracks until the (nonmonotone) Armijo conditions are satisfied.
80: Input Parameters:
81: + tao - Tao context
82: . X - current iterate (on output X contains new iterate, X + step*S)
83: . S - search direction
84: . f - merit function evaluated at X
85: . G - gradient of merit function evaluated at X
86: . W - work vector
87: - step - initial estimate of step length
89: Output parameters:
90: + f - merit function evaluated at new iterate, X + step*S
91: . G - gradient of merit function evaluated at new iterate, X + step*S
92: . X - new iterate
93: - step - final step length
95: @ */
96: static PetscErrorCode TaoLineSearchApply_Armijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
97: {
98: TaoLineSearch_ARMIJO *armP = (TaoLineSearch_ARMIJO *)ls->data;
99: PetscErrorCode ierr;
100: PetscInt i;
101: PetscReal fact, ref, gdx;
102: PetscInt idx;
103: PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
107: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
108: if (!armP->work) {
109: VecDuplicate(x,&armP->work);
110: armP->x = x;
111: PetscObjectReference((PetscObject)armP->x);
112: } else if (x != armP->x) {
113: /* If x has changed, then recreate work */
114: VecDestroy(&armP->work);
115: VecDuplicate(x,&armP->work);
116: PetscObjectDereference((PetscObject)armP->x);
117: armP->x = x;
118: PetscObjectReference((PetscObject)armP->x);
119: }
121: /* Check linesearch parameters */
122: if (armP->alpha < 1) {
123: PetscInfo1(ls,"Armijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
124: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
125: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
126: PetscInfo1(ls,"Armijo line search error: beta (%g) invalid\n", (double)armP->beta);
127: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
128: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
129: PetscInfo1(ls,"Armijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
130: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
131: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
132: PetscInfo1(ls,"Armijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
133: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
134: } else if (armP->memorySize < 1) {
135: PetscInfo1(ls,"Armijo line search error: memory_size (%D) < 1\n", armP->memorySize);
136: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
137: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
138: PetscInfo(ls,"Armijo line search error: reference_policy invalid\n");
139: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
140: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
141: PetscInfo(ls,"Armijo line search error: replacement_policy invalid\n");
142: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
143: } else if (PetscIsInfOrNanReal(*f)) {
144: PetscInfo(ls,"Armijo line search error: initial function inf or nan\n");
145: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
146: }
148: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) {
149: return(0);
150: }
152: /* Check to see of the memory has been allocated. If not, allocate
153: the historical array and populate it with the initial function
154: values. */
155: if (!armP->memory) {
156: PetscMalloc1(armP->memorySize, &armP->memory );
157: }
159: if (!armP->memorySetup) {
160: for (i = 0; i < armP->memorySize; i++) {
161: armP->memory[i] = armP->alpha*(*f);
162: }
164: armP->current = 0;
165: armP->lastReference = armP->memory[0];
166: armP->memorySetup=PETSC_TRUE;
167: }
169: /* Calculate reference value (MAX) */
170: ref = armP->memory[0];
171: idx = 0;
173: for (i = 1; i < armP->memorySize; i++) {
174: if (armP->memory[i] > ref) {
175: ref = armP->memory[i];
176: idx = i;
177: }
178: }
180: if (armP->referencePolicy == REFERENCE_AVE) {
181: ref = 0;
182: for (i = 0; i < armP->memorySize; i++) {
183: ref += armP->memory[i];
184: }
185: ref = ref / armP->memorySize;
186: ref = PetscMax(ref, armP->memory[armP->current]);
187: } else if (armP->referencePolicy == REFERENCE_MEAN) {
188: ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
189: }
190: VecDot(g,s,&gdx);
192: if (PetscIsInfOrNanReal(gdx)) {
193: PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
194: ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
195: return(0);
196: }
197: if (gdx >= 0.0) {
198: PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
199: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
200: return(0);
201: }
203: if (armP->nondescending) {
204: fact = armP->sigma;
205: } else {
206: fact = armP->sigma * gdx;
207: }
208: ls->step = ls->initstep;
209: while (ls->step >= ls->stepmin && (ls->nfeval+ls->nfgeval) < ls->max_funcs) {
210: /* Calculate iterate */
211: VecCopy(x,armP->work);
212: VecAXPY(armP->work,ls->step,s);
213: if (ls->bounded) {
214: VecMedian(ls->lower,armP->work,ls->upper,armP->work);
215: }
217: /* Calculate function at new iterate */
218: if (ls->hasobjective) {
219: TaoLineSearchComputeObjective(ls,armP->work,f);
220: g_computed=PETSC_FALSE;
221: } else if (ls->usegts) {
222: TaoLineSearchComputeObjectiveAndGTS(ls,armP->work,f,&gdx);
223: g_computed=PETSC_FALSE;
224: } else {
225: TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
226: g_computed=PETSC_TRUE;
227: }
228: if (ls->step == ls->initstep) {
229: ls->f_fullstep = *f;
230: }
232: if (PetscIsInfOrNanReal(*f)) {
233: ls->step *= armP->beta_inf;
234: } else {
235: /* Check descent condition */
236: if (armP->nondescending && *f <= ref - ls->step*fact*ref)
237: break;
238: if (!armP->nondescending && *f <= ref + ls->step*fact) {
239: break;
240: }
242: ls->step *= armP->beta;
243: }
244: }
246: /* Check termination */
247: if (PetscIsInfOrNanReal(*f)) {
248: PetscInfo(ls, "Function is inf or nan.\n");
249: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
250: } else if (ls->step < ls->stepmin) {
251: PetscInfo(ls, "Step length is below tolerance.\n");
252: ls->reason = TAOLINESEARCH_HALTED_RTOL;
253: } else if ((ls->nfeval+ls->nfgeval) >= ls->max_funcs) {
254: PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval+ls->nfgeval, ls->max_funcs);
255: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
256: }
257: if (ls->reason) {
258: return(0);
259: }
261: /* Successful termination, update memory */
262: ls->reason = TAOLINESEARCH_SUCCESS;
263: armP->lastReference = ref;
264: if (armP->replacementPolicy == REPLACE_FIFO) {
265: armP->memory[armP->current++] = *f;
266: if (armP->current >= armP->memorySize) {
267: armP->current = 0;
268: }
269: } else {
270: armP->current = idx;
271: armP->memory[idx] = *f;
272: }
274: /* Update iterate and compute gradient */
275: VecCopy(armP->work,x);
276: if (!g_computed) {
277: TaoLineSearchComputeGradient(ls, x, g);
278: }
279: PetscInfo2(ls, "%D function evals in line search, step = %g\n",ls->nfeval, (double)ls->step);
280: return(0);
281: }
283: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_Armijo(TaoLineSearch ls)
284: {
285: TaoLineSearch_ARMIJO *armP;
286: PetscErrorCode ierr;
290: PetscNewLog(ls,&armP);
292: armP->memory = NULL;
293: armP->alpha = 1.0;
294: armP->beta = 0.5;
295: armP->beta_inf = 0.5;
296: armP->sigma = 1e-4;
297: armP->memorySize = 1;
298: armP->referencePolicy = REFERENCE_MAX;
299: armP->replacementPolicy = REPLACE_MRU;
300: armP->nondescending=PETSC_FALSE;
301: ls->data = (void*)armP;
302: ls->initstep=1.0;
303: ls->ops->setup=0;
304: ls->ops->apply=TaoLineSearchApply_Armijo;
305: ls->ops->view = TaoLineSearchView_Armijo;
306: ls->ops->destroy = TaoLineSearchDestroy_Armijo;
307: ls->ops->reset = TaoLineSearchReset_Armijo;
308: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_Armijo;
309: return(0);
310: }