Actual source code: owarmijo.c
2: #include <petsc/private/taolinesearchimpl.h>
3: #include <../src/tao/linesearch/impls/owarmijo/owarmijo.h>
5: #define REPLACE_FIFO 1
6: #define REPLACE_MRU 2
8: #define REFERENCE_MAX 1
9: #define REFERENCE_AVE 2
10: #define REFERENCE_MEAN 3
12: static PetscErrorCode ProjWork_OWLQN(Vec w, Vec x, Vec gv, PetscReal *gdx)
13: {
14: const PetscReal *xptr, *gptr;
15: PetscReal *wptr;
16: PetscInt low, high, low1, high1, low2, high2, i;
18: VecGetOwnershipRange(w, &low, &high);
19: VecGetOwnershipRange(x, &low1, &high1);
20: VecGetOwnershipRange(gv, &low2, &high2);
22: *gdx = 0.0;
23: VecGetArray(w, &wptr);
24: VecGetArrayRead(x, &xptr);
25: VecGetArrayRead(gv, &gptr);
27: for (i = 0; i < high - low; i++) {
28: if (xptr[i] * wptr[i] < 0.0) wptr[i] = 0.0;
29: *gdx = *gdx + gptr[i] * (wptr[i] - xptr[i]);
30: }
31: VecRestoreArray(w, &wptr);
32: VecRestoreArrayRead(x, &xptr);
33: VecRestoreArrayRead(gv, &gptr);
34: return 0;
35: }
37: static PetscErrorCode TaoLineSearchDestroy_OWArmijo(TaoLineSearch ls)
38: {
39: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
41: PetscFree(armP->memory);
42: if (armP->x) PetscObjectDereference((PetscObject)armP->x);
43: VecDestroy(&armP->work);
44: PetscFree(ls->data);
45: return 0;
46: }
48: static PetscErrorCode TaoLineSearchSetFromOptions_OWArmijo(TaoLineSearch ls, PetscOptionItems *PetscOptionsObject)
49: {
50: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
52: PetscOptionsHeadBegin(PetscOptionsObject, "OWArmijo linesearch options");
53: PetscOptionsReal("-tao_ls_OWArmijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha, NULL);
54: PetscOptionsReal("-tao_ls_OWArmijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf, NULL);
55: PetscOptionsReal("-tao_ls_OWArmijo_beta", "decrease constant", "", armP->beta, &armP->beta, NULL);
56: PetscOptionsReal("-tao_ls_OWArmijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma, NULL);
57: PetscOptionsInt("-tao_ls_OWArmijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize, NULL);
58: PetscOptionsInt("-tao_ls_OWArmijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy, NULL);
59: PetscOptionsInt("-tao_ls_OWArmijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy, NULL);
60: PetscOptionsBool("-tao_ls_OWArmijo_nondescending", "Use nondescending OWArmijo algorithm", "", armP->nondescending, &armP->nondescending, NULL);
61: PetscOptionsHeadEnd();
62: return 0;
63: }
65: static PetscErrorCode TaoLineSearchView_OWArmijo(TaoLineSearch ls, PetscViewer pv)
66: {
67: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
68: PetscBool isascii;
70: PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii);
71: if (isascii) {
72: PetscViewerASCIIPrintf(pv, " OWArmijo linesearch");
73: if (armP->nondescending) PetscViewerASCIIPrintf(pv, " (nondescending)");
74: PetscViewerASCIIPrintf(pv, ": alpha=%g beta=%g ", (double)armP->alpha, (double)armP->beta);
75: PetscViewerASCIIPrintf(pv, "sigma=%g ", (double)armP->sigma);
76: PetscViewerASCIIPrintf(pv, "memsize=%" PetscInt_FMT "\n", armP->memorySize);
77: }
78: return 0;
79: }
81: /* @ TaoApply_OWArmijo - This routine performs a linesearch. It
82: backtracks until the (nonmonotone) OWArmijo conditions are satisfied.
84: Input Parameters:
85: + tao - TAO_SOLVER context
86: . X - current iterate (on output X contains new iterate, X + step*S)
87: . S - search direction
88: . f - merit function evaluated at X
89: . G - gradient of merit function evaluated at X
90: . W - work vector
91: - step - initial estimate of step length
93: Output parameters:
94: + f - merit function evaluated at new iterate, X + step*S
95: . G - gradient of merit function evaluated at new iterate, X + step*S
96: . X - new iterate
97: - step - final step length
99: Info is set to one of:
100: . 0 - the line search succeeds; the sufficient decrease
101: condition and the directional derivative condition hold
103: negative number if an input parameter is invalid
104: - -1 - step < 0
106: positive number > 1 if the line search otherwise terminates
107: + 1 - Step is at the lower bound, stepmin.
108: @ */
109: static PetscErrorCode TaoLineSearchApply_OWArmijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
110: {
111: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
112: PetscInt i, its = 0;
113: PetscReal fact, ref, gdx;
114: PetscInt idx;
115: PetscBool g_computed = PETSC_FALSE; /* to prevent extra gradient computation */
116: Vec g_old;
117: PetscReal owlqn_minstep = 0.005;
118: PetscReal partgdx;
119: MPI_Comm comm;
121: PetscObjectGetComm((PetscObject)ls, &comm);
122: fact = 0.0;
123: ls->nfeval = 0;
124: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
125: if (!armP->work) {
126: VecDuplicate(x, &armP->work);
127: armP->x = x;
128: PetscObjectReference((PetscObject)armP->x);
129: } else if (x != armP->x) {
130: VecDestroy(&armP->work);
131: VecDuplicate(x, &armP->work);
132: PetscObjectDereference((PetscObject)armP->x);
133: armP->x = x;
134: PetscObjectReference((PetscObject)armP->x);
135: }
137: TaoLineSearchMonitor(ls, 0, *f, 0.0);
139: /* Check linesearch parameters */
140: if (armP->alpha < 1) {
141: PetscInfo(ls, "OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha);
142: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
143: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
144: PetscInfo(ls, "OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta);
145: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
146: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
147: PetscInfo(ls, "OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf);
148: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
149: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
150: PetscInfo(ls, "OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma);
151: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
152: } else if (armP->memorySize < 1) {
153: PetscInfo(ls, "OWArmijo line search error: memory_size (%" PetscInt_FMT ") < 1\n", armP->memorySize);
154: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
155: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
156: PetscInfo(ls, "OWArmijo line search error: reference_policy invalid\n");
157: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
158: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
159: PetscInfo(ls, "OWArmijo line search error: replacement_policy invalid\n");
160: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
161: } else if (PetscIsInfOrNanReal(*f)) {
162: PetscInfo(ls, "OWArmijo line search error: initial function inf or nan\n");
163: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
164: }
166: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) return 0;
168: /* Check to see of the memory has been allocated. If not, allocate
169: the historical array and populate it with the initial function
170: values. */
171: if (!armP->memory) PetscMalloc1(armP->memorySize, &armP->memory);
173: if (!armP->memorySetup) {
174: for (i = 0; i < armP->memorySize; i++) armP->memory[i] = armP->alpha * (*f);
175: armP->current = 0;
176: armP->lastReference = armP->memory[0];
177: armP->memorySetup = PETSC_TRUE;
178: }
180: /* Calculate reference value (MAX) */
181: ref = armP->memory[0];
182: idx = 0;
184: for (i = 1; i < armP->memorySize; i++) {
185: if (armP->memory[i] > ref) {
186: ref = armP->memory[i];
187: idx = i;
188: }
189: }
191: if (armP->referencePolicy == REFERENCE_AVE) {
192: ref = 0;
193: for (i = 0; i < armP->memorySize; i++) ref += armP->memory[i];
194: ref = ref / armP->memorySize;
195: ref = PetscMax(ref, armP->memory[armP->current]);
196: } else if (armP->referencePolicy == REFERENCE_MEAN) {
197: ref = PetscMin(ref, 0.5 * (armP->lastReference + armP->memory[armP->current]));
198: }
200: if (armP->nondescending) fact = armP->sigma;
202: VecDuplicate(g, &g_old);
203: VecCopy(g, g_old);
205: ls->step = ls->initstep;
206: while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
207: /* Calculate iterate */
208: ++its;
209: VecWAXPY(armP->work, ls->step, s, x);
211: partgdx = 0.0;
212: ProjWork_OWLQN(armP->work, x, g_old, &partgdx);
213: MPIU_Allreduce(&partgdx, &gdx, 1, MPIU_REAL, MPIU_SUM, comm);
215: /* Check the condition of gdx */
216: if (PetscIsInfOrNanReal(gdx)) {
217: PetscInfo(ls, "Initial Line Search step * g is Inf or Nan (%g)\n", (double)gdx);
218: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
219: return 0;
220: }
221: if (gdx >= 0.0) {
222: PetscInfo(ls, "Initial Line Search step is not descent direction (g's=%g)\n", (double)gdx);
223: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
224: return 0;
225: }
227: /* Calculate function at new iterate */
228: TaoLineSearchComputeObjectiveAndGradient(ls, armP->work, f, g);
229: g_computed = PETSC_TRUE;
231: TaoLineSearchMonitor(ls, its, *f, ls->step);
233: if (ls->step == ls->initstep) ls->f_fullstep = *f;
235: if (PetscIsInfOrNanReal(*f)) {
236: ls->step *= armP->beta_inf;
237: } else {
238: /* Check descent condition */
239: if (armP->nondescending && *f <= ref - ls->step * fact * ref) break;
240: if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
241: ls->step *= armP->beta;
242: }
243: }
244: VecDestroy(&g_old);
246: /* Check termination */
247: if (PetscIsInfOrNanReal(*f)) {
248: PetscInfo(ls, "Function is inf or nan.\n");
249: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
250: } else if (ls->step < owlqn_minstep) {
251: PetscInfo(ls, "Step length is below tolerance.\n");
252: ls->reason = TAOLINESEARCH_HALTED_RTOL;
253: } else if (ls->nfeval >= ls->max_funcs) {
254: PetscInfo(ls, "Number of line search function evals (%" PetscInt_FMT ") > maximum allowed (%" PetscInt_FMT ")\n", ls->nfeval, ls->max_funcs);
255: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
256: }
257: if (ls->reason) return 0;
259: /* Successful termination, update memory */
260: ls->reason = TAOLINESEARCH_SUCCESS;
261: armP->lastReference = ref;
262: if (armP->replacementPolicy == REPLACE_FIFO) {
263: armP->memory[armP->current++] = *f;
264: if (armP->current >= armP->memorySize) armP->current = 0;
265: } else {
266: armP->current = idx;
267: armP->memory[idx] = *f;
268: }
270: /* Update iterate and compute gradient */
271: VecCopy(armP->work, x);
272: if (!g_computed) TaoLineSearchComputeGradient(ls, x, g);
273: PetscInfo(ls, "%" PetscInt_FMT " function evals in line search, step = %10.4f\n", ls->nfeval, (double)ls->step);
274: return 0;
275: }
277: /*MC
278: TAOLINESEARCHOWARMIJO - Special line-search type for the Orthant-Wise Limited Quasi-Newton (TAOOWLQN) algorithm.
279: Should not be used with any other algorithm.
281: Level: developer
283: .keywords: Tao, linesearch
284: M*/
285: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
286: {
287: TaoLineSearch_OWARMIJO *armP;
290: PetscNew(&armP);
292: armP->memory = NULL;
293: armP->alpha = 1.0;
294: armP->beta = 0.25;
295: armP->beta_inf = 0.25;
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 = 0.1;
303: ls->ops->monitor = NULL;
304: ls->ops->setup = NULL;
305: ls->ops->reset = NULL;
306: ls->ops->apply = TaoLineSearchApply_OWArmijo;
307: ls->ops->view = TaoLineSearchView_OWArmijo;
308: ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
309: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
310: return 0;
311: }