Actual source code: armijo.c
petsc-3.14.6 2021-03-30
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,its=0;
101: PetscReal fact, ref, gdx;
102: PetscInt idx;
103: PetscBool g_computed=PETSC_FALSE; /* to prevent extra gradient computation */
106: TaoLineSearchMonitor(ls, 0, *f, 0.0);
108: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
109: if (!armP->work) {
110: VecDuplicate(x,&armP->work);
111: armP->x = x;
112: PetscObjectReference((PetscObject)armP->x);
113: } else if (x != armP->x) {
114: /* If x has changed, then recreate work */
115: VecDestroy(&armP->work);
116: VecDuplicate(x,&armP->work);
117: PetscObjectDereference((PetscObject)armP->x);
118: armP->x = x;
119: PetscObjectReference((PetscObject)armP->x);
120: }
122: /* Check linesearch parameters */
123: if (armP->alpha < 1) {
124: PetscInfo1(ls,"Armijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
125: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
126: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
127: PetscInfo1(ls,"Armijo line search error: beta (%g) invalid\n", (double)armP->beta);
128: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
129: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
130: PetscInfo1(ls,"Armijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
131: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
132: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
133: PetscInfo1(ls,"Armijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
134: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
135: } else if (armP->memorySize < 1) {
136: PetscInfo1(ls,"Armijo line search error: memory_size (%D) < 1\n", armP->memorySize);
137: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
138: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
139: PetscInfo(ls,"Armijo line search error: reference_policy invalid\n");
140: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
141: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
142: PetscInfo(ls,"Armijo line search error: replacement_policy invalid\n");
143: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
144: } else if (PetscIsInfOrNanReal(*f)) {
145: PetscInfo(ls,"Armijo line search error: initial function inf or nan\n");
146: ls->reason=TAOLINESEARCH_FAILED_BADPARAMETER;
147: }
149: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) {
150: return(0);
151: }
153: /* Check to see of the memory has been allocated. If not, allocate
154: the historical array and populate it with the initial function
155: values. */
156: if (!armP->memory) {
157: PetscMalloc1(armP->memorySize, &armP->memory);
158: }
160: if (!armP->memorySetup) {
161: for (i = 0; i < armP->memorySize; i++) {
162: armP->memory[i] = armP->alpha*(*f);
163: }
165: armP->current = 0;
166: armP->lastReference = armP->memory[0];
167: armP->memorySetup=PETSC_TRUE;
168: }
170: /* Calculate reference value (MAX) */
171: ref = armP->memory[0];
172: idx = 0;
174: for (i = 1; i < armP->memorySize; i++) {
175: if (armP->memory[i] > ref) {
176: ref = armP->memory[i];
177: idx = i;
178: }
179: }
181: if (armP->referencePolicy == REFERENCE_AVE) {
182: ref = 0;
183: for (i = 0; i < armP->memorySize; i++) {
184: ref += armP->memory[i];
185: }
186: ref = ref / armP->memorySize;
187: ref = PetscMax(ref, armP->memory[armP->current]);
188: } else if (armP->referencePolicy == REFERENCE_MEAN) {
189: ref = PetscMin(ref, 0.5*(armP->lastReference + armP->memory[armP->current]));
190: }
191: VecDot(g,s,&gdx);
193: if (PetscIsInfOrNanReal(gdx)) {
194: PetscInfo1(ls,"Initial Line Search step * g is Inf or Nan (%g)\n",(double)gdx);
195: ls->reason=TAOLINESEARCH_FAILED_INFORNAN;
196: return(0);
197: }
198: if (gdx >= 0.0) {
199: PetscInfo1(ls,"Initial Line Search step is not descent direction (g's=%g)\n",(double)gdx);
200: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
201: return(0);
202: }
204: if (armP->nondescending) {
205: fact = armP->sigma;
206: } else {
207: fact = armP->sigma * gdx;
208: }
209: ls->step = ls->initstep;
210: while (ls->step >= ls->stepmin && (ls->nfeval+ls->nfgeval) < ls->max_funcs) {
211: /* Calculate iterate */
212: ++its;
213: VecCopy(x,armP->work);
214: VecAXPY(armP->work,ls->step,s);
215: if (ls->bounded) {
216: VecMedian(ls->lower,armP->work,ls->upper,armP->work);
217: }
219: /* Calculate function at new iterate */
220: if (ls->hasobjective) {
221: TaoLineSearchComputeObjective(ls,armP->work,f);
222: g_computed=PETSC_FALSE;
223: } else if (ls->usegts) {
224: TaoLineSearchComputeObjectiveAndGTS(ls,armP->work,f,&gdx);
225: g_computed=PETSC_FALSE;
226: } else {
227: TaoLineSearchComputeObjectiveAndGradient(ls,armP->work,f,g);
228: g_computed=PETSC_TRUE;
229: }
230: if (ls->step == ls->initstep) {
231: ls->f_fullstep = *f;
232: }
234: TaoLineSearchMonitor(ls, its, *f, ls->step);
236: if (PetscIsInfOrNanReal(*f)) {
237: ls->step *= armP->beta_inf;
238: } else {
239: /* Check descent condition */
240: if (armP->nondescending && *f <= ref - ls->step*fact*ref)
241: break;
242: if (!armP->nondescending && *f <= ref + ls->step*fact) {
243: break;
244: }
246: ls->step *= armP->beta;
247: }
248: }
250: /* Check termination */
251: if (PetscIsInfOrNanReal(*f)) {
252: PetscInfo(ls, "Function is inf or nan.\n");
253: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
254: } else if (ls->step < ls->stepmin) {
255: PetscInfo(ls, "Step length is below tolerance.\n");
256: ls->reason = TAOLINESEARCH_HALTED_RTOL;
257: } else if ((ls->nfeval+ls->nfgeval) >= ls->max_funcs) {
258: PetscInfo2(ls, "Number of line search function evals (%D) > maximum allowed (%D)\n",ls->nfeval+ls->nfgeval, ls->max_funcs);
259: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
260: }
261: if (ls->reason) {
262: return(0);
263: }
265: /* Successful termination, update memory */
266: ls->reason = TAOLINESEARCH_SUCCESS;
267: armP->lastReference = ref;
268: if (armP->replacementPolicy == REPLACE_FIFO) {
269: armP->memory[armP->current++] = *f;
270: if (armP->current >= armP->memorySize) {
271: armP->current = 0;
272: }
273: } else {
274: armP->current = idx;
275: armP->memory[idx] = *f;
276: }
278: /* Update iterate and compute gradient */
279: VecCopy(armP->work,x);
280: if (!g_computed) {
281: TaoLineSearchComputeGradient(ls, x, g);
282: }
283: PetscInfo2(ls, "%D function evals in line search, step = %g\n",ls->nfeval, (double)ls->step);
284: return(0);
285: }
287: /*MC
288: TAOLINESEARCHARMIJO - Backtracking line-search that satisfies only the (nonmonotone) Armijo condition
289: (i.e., sufficient decrease).
291: Armijo line-search type can be selected with "-tao_ls_type armijo".
293: Level: developer
295: .seealso: TaoLineSearchCreate(), TaoLineSearchSetType(), TaoLineSearchApply()
297: .keywords: Tao, linesearch
298: M*/
299: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_Armijo(TaoLineSearch ls)
300: {
301: TaoLineSearch_ARMIJO *armP;
302: PetscErrorCode ierr;
306: PetscNewLog(ls,&armP);
308: armP->memory = NULL;
309: armP->alpha = 1.0;
310: armP->beta = 0.5;
311: armP->beta_inf = 0.5;
312: armP->sigma = 1e-4;
313: armP->memorySize = 1;
314: armP->referencePolicy = REFERENCE_MAX;
315: armP->replacementPolicy = REPLACE_MRU;
316: armP->nondescending=PETSC_FALSE;
317: ls->data = (void*)armP;
318: ls->initstep=1.0;
319: ls->ops->setup = NULL;
320: ls->ops->apply = TaoLineSearchApply_Armijo;
321: ls->ops->view = TaoLineSearchView_Armijo;
322: ls->ops->destroy = TaoLineSearchDestroy_Armijo;
323: ls->ops->reset = TaoLineSearchReset_Armijo;
324: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_Armijo;
325: return(0);
326: }