Actual source code: owarmijo.c
1: #include <petsc/private/taolinesearchimpl.h>
2: #include <../src/tao/linesearch/impls/owarmijo/owarmijo.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 ProjWork_OWLQN(Vec w, Vec x, Vec gv, PetscReal *gdx)
12: {
13: const PetscReal *xptr, *gptr;
14: PetscReal *wptr;
15: PetscInt low, high, low1, high1, low2, high2, i;
17: PetscFunctionBegin;
18: PetscCall(VecGetOwnershipRange(w, &low, &high));
19: PetscCall(VecGetOwnershipRange(x, &low1, &high1));
20: PetscCall(VecGetOwnershipRange(gv, &low2, &high2));
22: *gdx = 0.0;
23: PetscCall(VecGetArray(w, &wptr));
24: PetscCall(VecGetArrayRead(x, &xptr));
25: PetscCall(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: PetscCall(VecRestoreArray(w, &wptr));
32: PetscCall(VecRestoreArrayRead(x, &xptr));
33: PetscCall(VecRestoreArrayRead(gv, &gptr));
34: PetscFunctionReturn(PETSC_SUCCESS);
35: }
37: static PetscErrorCode TaoLineSearchDestroy_OWArmijo(TaoLineSearch ls)
38: {
39: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
41: PetscFunctionBegin;
42: PetscCall(PetscFree(armP->memory));
43: if (armP->x) PetscCall(PetscObjectDereference((PetscObject)armP->x));
44: PetscCall(VecDestroy(&armP->work));
45: PetscCall(PetscFree(ls->data));
46: PetscFunctionReturn(PETSC_SUCCESS);
47: }
49: static PetscErrorCode TaoLineSearchSetFromOptions_OWArmijo(TaoLineSearch ls, PetscOptionItems *PetscOptionsObject)
50: {
51: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
53: PetscFunctionBegin;
54: PetscOptionsHeadBegin(PetscOptionsObject, "OWArmijo linesearch options");
55: PetscCall(PetscOptionsReal("-tao_ls_OWArmijo_alpha", "initial reference constant", "", armP->alpha, &armP->alpha, NULL));
56: PetscCall(PetscOptionsReal("-tao_ls_OWArmijo_beta_inf", "decrease constant one", "", armP->beta_inf, &armP->beta_inf, NULL));
57: PetscCall(PetscOptionsReal("-tao_ls_OWArmijo_beta", "decrease constant", "", armP->beta, &armP->beta, NULL));
58: PetscCall(PetscOptionsReal("-tao_ls_OWArmijo_sigma", "acceptance constant", "", armP->sigma, &armP->sigma, NULL));
59: PetscCall(PetscOptionsInt("-tao_ls_OWArmijo_memory_size", "number of historical elements", "", armP->memorySize, &armP->memorySize, NULL));
60: PetscCall(PetscOptionsInt("-tao_ls_OWArmijo_reference_policy", "policy for updating reference value", "", armP->referencePolicy, &armP->referencePolicy, NULL));
61: PetscCall(PetscOptionsInt("-tao_ls_OWArmijo_replacement_policy", "policy for updating memory", "", armP->replacementPolicy, &armP->replacementPolicy, NULL));
62: PetscCall(PetscOptionsBool("-tao_ls_OWArmijo_nondescending", "Use nondescending OWArmijo algorithm", "", armP->nondescending, &armP->nondescending, NULL));
63: PetscOptionsHeadEnd();
64: PetscFunctionReturn(PETSC_SUCCESS);
65: }
67: static PetscErrorCode TaoLineSearchView_OWArmijo(TaoLineSearch ls, PetscViewer pv)
68: {
69: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
70: PetscBool isascii;
72: PetscFunctionBegin;
73: PetscCall(PetscObjectTypeCompare((PetscObject)pv, PETSCVIEWERASCII, &isascii));
74: if (isascii) {
75: PetscCall(PetscViewerASCIIPrintf(pv, " OWArmijo linesearch"));
76: if (armP->nondescending) PetscCall(PetscViewerASCIIPrintf(pv, " (nondescending)"));
77: PetscCall(PetscViewerASCIIPrintf(pv, ": alpha=%g beta=%g ", (double)armP->alpha, (double)armP->beta));
78: PetscCall(PetscViewerASCIIPrintf(pv, "sigma=%g ", (double)armP->sigma));
79: PetscCall(PetscViewerASCIIPrintf(pv, "memsize=%" PetscInt_FMT "\n", armP->memorySize));
80: }
81: PetscFunctionReturn(PETSC_SUCCESS);
82: }
84: /* @ TaoApply_OWArmijo - This routine performs a linesearch. It
85: backtracks until the (nonmonotone) OWArmijo conditions are satisfied.
87: Input Parameters:
88: + tao - TAO_SOLVER context
89: . X - current iterate (on output X contains new iterate, X + step*S)
90: . S - search direction
91: . f - merit function evaluated at X
92: . G - gradient of merit function evaluated at X
93: . W - work vector
94: - step - initial estimate of step length
96: Output parameters:
97: + f - merit function evaluated at new iterate, X + step*S
98: . G - gradient of merit function evaluated at new iterate, X + step*S
99: . X - new iterate
100: - step - final step length
102: Info is set to one of:
103: . 0 - the line search succeeds; the sufficient decrease
104: condition and the directional derivative condition hold
106: negative number if an input parameter is invalid
107: - -1 - step < 0
109: positive number > 1 if the line search otherwise terminates
110: + 1 - Step is at the lower bound, stepmin.
111: @ */
112: static PetscErrorCode TaoLineSearchApply_OWArmijo(TaoLineSearch ls, Vec x, PetscReal *f, Vec g, Vec s)
113: {
114: TaoLineSearch_OWARMIJO *armP = (TaoLineSearch_OWARMIJO *)ls->data;
115: PetscInt i, its = 0;
116: PetscReal fact, ref, gdx;
117: PetscInt idx;
118: PetscBool g_computed = PETSC_FALSE; /* to prevent extra gradient computation */
119: Vec g_old;
120: PetscReal owlqn_minstep = 0.005;
121: PetscReal partgdx;
122: MPI_Comm comm;
124: PetscFunctionBegin;
125: PetscCall(PetscObjectGetComm((PetscObject)ls, &comm));
126: fact = 0.0;
127: ls->nfeval = 0;
128: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
129: if (!armP->work) {
130: PetscCall(VecDuplicate(x, &armP->work));
131: armP->x = x;
132: PetscCall(PetscObjectReference((PetscObject)armP->x));
133: } else if (x != armP->x) {
134: PetscCall(VecDestroy(&armP->work));
135: PetscCall(VecDuplicate(x, &armP->work));
136: PetscCall(PetscObjectDereference((PetscObject)armP->x));
137: armP->x = x;
138: PetscCall(PetscObjectReference((PetscObject)armP->x));
139: }
141: PetscCall(TaoLineSearchMonitor(ls, 0, *f, 0.0));
143: /* Check linesearch parameters */
144: if (armP->alpha < 1) {
145: PetscCall(PetscInfo(ls, "OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha));
146: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
147: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
148: PetscCall(PetscInfo(ls, "OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta));
149: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
150: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
151: PetscCall(PetscInfo(ls, "OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf));
152: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
153: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
154: PetscCall(PetscInfo(ls, "OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma));
155: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
156: } else if (armP->memorySize < 1) {
157: PetscCall(PetscInfo(ls, "OWArmijo line search error: memory_size (%" PetscInt_FMT ") < 1\n", armP->memorySize));
158: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
159: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
160: PetscCall(PetscInfo(ls, "OWArmijo line search error: reference_policy invalid\n"));
161: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
162: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
163: PetscCall(PetscInfo(ls, "OWArmijo line search error: replacement_policy invalid\n"));
164: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
165: } else if (PetscIsInfOrNanReal(*f)) {
166: PetscCall(PetscInfo(ls, "OWArmijo line search error: initial function inf or nan\n"));
167: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
168: }
170: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);
172: /* Check to see of the memory has been allocated. If not, allocate
173: the historical array and populate it with the initial function
174: values. */
175: if (!armP->memory) PetscCall(PetscMalloc1(armP->memorySize, &armP->memory));
177: if (!armP->memorySetup) {
178: for (i = 0; i < armP->memorySize; i++) armP->memory[i] = armP->alpha * (*f);
179: armP->current = 0;
180: armP->lastReference = armP->memory[0];
181: armP->memorySetup = PETSC_TRUE;
182: }
184: /* Calculate reference value (MAX) */
185: ref = armP->memory[0];
186: idx = 0;
188: for (i = 1; i < armP->memorySize; i++) {
189: if (armP->memory[i] > ref) {
190: ref = armP->memory[i];
191: idx = i;
192: }
193: }
195: if (armP->referencePolicy == REFERENCE_AVE) {
196: ref = 0;
197: for (i = 0; i < armP->memorySize; i++) ref += armP->memory[i];
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: }
204: if (armP->nondescending) fact = armP->sigma;
206: PetscCall(VecDuplicate(g, &g_old));
207: PetscCall(VecCopy(g, g_old));
209: ls->step = ls->initstep;
210: while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
211: /* Calculate iterate */
212: ++its;
213: PetscCall(VecWAXPY(armP->work, ls->step, s, x));
215: partgdx = 0.0;
216: PetscCall(ProjWork_OWLQN(armP->work, x, g_old, &partgdx));
217: PetscCall(MPIU_Allreduce(&partgdx, &gdx, 1, MPIU_REAL, MPIU_SUM, comm));
219: /* Check the condition of gdx */
220: if (PetscIsInfOrNanReal(gdx)) {
221: PetscCall(PetscInfo(ls, "Initial Line Search step * g is Inf or Nan (%g)\n", (double)gdx));
222: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
223: PetscFunctionReturn(PETSC_SUCCESS);
224: }
225: if (gdx >= 0.0) {
226: PetscCall(PetscInfo(ls, "Initial Line Search step is not descent direction (g's=%g)\n", (double)gdx));
227: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
228: PetscFunctionReturn(PETSC_SUCCESS);
229: }
231: /* Calculate function at new iterate */
232: PetscCall(TaoLineSearchComputeObjectiveAndGradient(ls, armP->work, f, g));
233: g_computed = PETSC_TRUE;
235: PetscCall(TaoLineSearchMonitor(ls, its, *f, ls->step));
237: if (ls->step == ls->initstep) ls->f_fullstep = *f;
239: if (PetscIsInfOrNanReal(*f)) {
240: ls->step *= armP->beta_inf;
241: } else {
242: /* Check descent condition */
243: if (armP->nondescending && *f <= ref - ls->step * fact * ref) break;
244: if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
245: ls->step *= armP->beta;
246: }
247: }
248: PetscCall(VecDestroy(&g_old));
250: /* Check termination */
251: if (PetscIsInfOrNanReal(*f)) {
252: PetscCall(PetscInfo(ls, "Function is inf or nan.\n"));
253: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
254: } else if (ls->step < owlqn_minstep) {
255: PetscCall(PetscInfo(ls, "Step length is below tolerance.\n"));
256: ls->reason = TAOLINESEARCH_HALTED_RTOL;
257: } else if (ls->nfeval >= ls->max_funcs) {
258: PetscCall(PetscInfo(ls, "Number of line search function evals (%" PetscInt_FMT ") > maximum allowed (%" PetscInt_FMT ")\n", ls->nfeval, ls->max_funcs));
259: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
260: }
261: if (ls->reason) PetscFunctionReturn(PETSC_SUCCESS);
263: /* Successful termination, update memory */
264: ls->reason = TAOLINESEARCH_SUCCESS;
265: armP->lastReference = ref;
266: if (armP->replacementPolicy == REPLACE_FIFO) {
267: armP->memory[armP->current++] = *f;
268: if (armP->current >= armP->memorySize) armP->current = 0;
269: } else {
270: armP->current = idx;
271: armP->memory[idx] = *f;
272: }
274: /* Update iterate and compute gradient */
275: PetscCall(VecCopy(armP->work, x));
276: if (!g_computed) PetscCall(TaoLineSearchComputeGradient(ls, x, g));
277: PetscCall(PetscInfo(ls, "%" PetscInt_FMT " function evals in line search, step = %10.4f\n", ls->nfeval, (double)ls->step));
278: PetscFunctionReturn(PETSC_SUCCESS);
279: }
281: /*MC
282: TAOLINESEARCHOWARMIJO - Special line-search type for the Orthant-Wise Limited Quasi-Newton (`TAOOWLQN`) algorithm.
283: Should not be used with any other algorithm.
285: Level: developer
287: seealso: `TaoLineSearch`, `TAOOWLQN`, `Tao`
288: M*/
289: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
290: {
291: TaoLineSearch_OWARMIJO *armP;
293: PetscFunctionBegin;
295: PetscCall(PetscNew(&armP));
297: armP->memory = NULL;
298: armP->alpha = 1.0;
299: armP->beta = 0.25;
300: armP->beta_inf = 0.25;
301: armP->sigma = 1e-4;
302: armP->memorySize = 1;
303: armP->referencePolicy = REFERENCE_MAX;
304: armP->replacementPolicy = REPLACE_MRU;
305: armP->nondescending = PETSC_FALSE;
306: ls->data = (void *)armP;
307: ls->initstep = 0.1;
308: ls->ops->monitor = NULL;
309: ls->ops->setup = NULL;
310: ls->ops->reset = NULL;
311: ls->ops->apply = TaoLineSearchApply_OWArmijo;
312: ls->ops->view = TaoLineSearchView_OWArmijo;
313: ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
314: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
315: PetscFunctionReturn(PETSC_SUCCESS);
316: }