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: 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: MPI_Comm comm;
123: PetscFunctionBegin;
124: PetscCall(PetscObjectGetComm((PetscObject)ls, &comm));
125: fact = 0.0;
126: ls->nfeval = 0;
127: ls->reason = TAOLINESEARCH_CONTINUE_ITERATING;
128: if (!armP->work) {
129: PetscCall(VecDuplicate(x, &armP->work));
130: armP->x = x;
131: PetscCall(PetscObjectReference((PetscObject)armP->x));
132: } else if (x != armP->x) {
133: PetscCall(VecDestroy(&armP->work));
134: PetscCall(VecDuplicate(x, &armP->work));
135: PetscCall(PetscObjectDereference((PetscObject)armP->x));
136: armP->x = x;
137: PetscCall(PetscObjectReference((PetscObject)armP->x));
138: }
140: PetscCall(TaoLineSearchMonitor(ls, 0, *f, 0.0));
142: /* Check linesearch parameters */
143: if (armP->alpha < 1) {
144: PetscCall(PetscInfo(ls, "OWArmijo line search error: alpha (%g) < 1\n", (double)armP->alpha));
145: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
146: } else if ((armP->beta <= 0) || (armP->beta >= 1)) {
147: PetscCall(PetscInfo(ls, "OWArmijo line search error: beta (%g) invalid\n", (double)armP->beta));
148: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
149: } else if ((armP->beta_inf <= 0) || (armP->beta_inf >= 1)) {
150: PetscCall(PetscInfo(ls, "OWArmijo line search error: beta_inf (%g) invalid\n", (double)armP->beta_inf));
151: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
152: } else if ((armP->sigma <= 0) || (armP->sigma >= 0.5)) {
153: PetscCall(PetscInfo(ls, "OWArmijo line search error: sigma (%g) invalid\n", (double)armP->sigma));
154: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
155: } else if (armP->memorySize < 1) {
156: PetscCall(PetscInfo(ls, "OWArmijo line search error: memory_size (%" PetscInt_FMT ") < 1\n", armP->memorySize));
157: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
158: } else if ((armP->referencePolicy != REFERENCE_MAX) && (armP->referencePolicy != REFERENCE_AVE) && (armP->referencePolicy != REFERENCE_MEAN)) {
159: PetscCall(PetscInfo(ls, "OWArmijo line search error: reference_policy invalid\n"));
160: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
161: } else if ((armP->replacementPolicy != REPLACE_FIFO) && (armP->replacementPolicy != REPLACE_MRU)) {
162: PetscCall(PetscInfo(ls, "OWArmijo line search error: replacement_policy invalid\n"));
163: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
164: } else if (PetscIsInfOrNanReal(*f)) {
165: PetscCall(PetscInfo(ls, "OWArmijo line search error: initial function inf or nan\n"));
166: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
167: }
169: if (ls->reason != TAOLINESEARCH_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);
171: /* Check to see of the memory has been allocated. If not, allocate
172: the historical array and populate it with the initial function
173: values. */
174: if (!armP->memory) PetscCall(PetscMalloc1(armP->memorySize, &armP->memory));
176: if (!armP->memorySetup) {
177: for (i = 0; i < armP->memorySize; i++) armP->memory[i] = armP->alpha * (*f);
178: armP->current = 0;
179: armP->lastReference = armP->memory[0];
180: armP->memorySetup = PETSC_TRUE;
181: }
183: /* Calculate reference value (MAX) */
184: ref = armP->memory[0];
185: idx = 0;
187: for (i = 1; i < armP->memorySize; i++) {
188: if (armP->memory[i] > ref) {
189: ref = armP->memory[i];
190: idx = i;
191: }
192: }
194: if (armP->referencePolicy == REFERENCE_AVE) {
195: ref = 0;
196: for (i = 0; i < armP->memorySize; i++) ref += armP->memory[i];
197: ref = ref / armP->memorySize;
198: ref = PetscMax(ref, armP->memory[armP->current]);
199: } else if (armP->referencePolicy == REFERENCE_MEAN) {
200: ref = PetscMin(ref, 0.5 * (armP->lastReference + armP->memory[armP->current]));
201: }
203: if (armP->nondescending) fact = armP->sigma;
205: PetscCall(VecDuplicate(g, &g_old));
206: PetscCall(VecCopy(g, g_old));
208: ls->step = ls->initstep;
209: while (ls->step >= owlqn_minstep && ls->nfeval < ls->max_funcs) {
210: /* Calculate iterate */
211: ++its;
212: PetscCall(VecWAXPY(armP->work, ls->step, s, x));
214: gdx = 0.0;
215: PetscCall(ProjWork_OWLQN(armP->work, x, g_old, &gdx));
216: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &gdx, 1, MPIU_REAL, MPIU_SUM, comm));
218: /* Check the condition of gdx */
219: if (PetscIsInfOrNanReal(gdx)) {
220: PetscCall(PetscInfo(ls, "Initial Line Search step * g is infinity or NaN (%g)\n", (double)gdx));
221: ls->reason = TAOLINESEARCH_FAILED_INFORNAN;
222: PetscFunctionReturn(PETSC_SUCCESS);
223: }
224: if (gdx >= 0.0) {
225: PetscCall(PetscInfo(ls, "Initial Line Search step is not descent direction (g's=%g)\n", (double)gdx));
226: ls->reason = TAOLINESEARCH_FAILED_ASCENT;
227: PetscFunctionReturn(PETSC_SUCCESS);
228: }
230: /* Calculate function at new iterate */
231: PetscCall(VecLockReadPush(x));
232: PetscCall(TaoLineSearchComputeObjectiveAndGradient(ls, armP->work, f, g));
233: PetscCall(VecLockReadPop(x));
234: g_computed = PETSC_TRUE;
236: PetscCall(TaoLineSearchMonitor(ls, its, *f, ls->step));
238: if (ls->step == ls->initstep) ls->f_fullstep = *f;
240: if (PetscIsInfOrNanReal(*f)) {
241: ls->step *= armP->beta_inf;
242: } else {
243: /* Check descent condition */
244: if (armP->nondescending && *f <= ref - ls->step * fact * ref) break;
245: if (!armP->nondescending && *f <= ref + armP->sigma * gdx) break;
246: ls->step *= armP->beta;
247: }
248: }
249: PetscCall(VecDestroy(&g_old));
251: /* Check termination */
252: if (PetscIsInfOrNanReal(*f)) {
253: PetscCall(PetscInfo(ls, "Function is inf or nan.\n"));
254: ls->reason = TAOLINESEARCH_FAILED_BADPARAMETER;
255: } else if (ls->step < owlqn_minstep) {
256: PetscCall(PetscInfo(ls, "Step length is below tolerance.\n"));
257: ls->reason = TAOLINESEARCH_HALTED_RTOL;
258: } else if (ls->nfeval >= ls->max_funcs) {
259: PetscCall(PetscInfo(ls, "Number of line search function evals (%" PetscInt_FMT ") > maximum allowed (%" PetscInt_FMT ")\n", ls->nfeval, ls->max_funcs));
260: ls->reason = TAOLINESEARCH_HALTED_MAXFCN;
261: }
262: if (ls->reason) PetscFunctionReturn(PETSC_SUCCESS);
264: /* Successful termination, update memory */
265: ls->reason = TAOLINESEARCH_SUCCESS;
266: armP->lastReference = ref;
267: if (armP->replacementPolicy == REPLACE_FIFO) {
268: armP->memory[armP->current++] = *f;
269: if (armP->current >= armP->memorySize) armP->current = 0;
270: } else {
271: armP->current = idx;
272: armP->memory[idx] = *f;
273: }
275: /* Update iterate and compute gradient */
276: PetscCall(VecCopy(armP->work, x));
277: if (!g_computed) PetscCall(TaoLineSearchComputeGradient(ls, x, g));
278: PetscCall(PetscInfo(ls, "%" PetscInt_FMT " function evals in line search, step = %10.4f\n", ls->nfeval, (double)ls->step));
279: PetscFunctionReturn(PETSC_SUCCESS);
280: }
282: /*MC
283: TAOLINESEARCHOWARMIJO - Special line-search type for the Orthant-Wise Limited Quasi-Newton (`TAOOWLQN`) algorithm.
284: Should not be used with any other algorithm.
286: Level: developer
288: .seealso: `TaoLineSearch`, `TAOOWLQN`, `Tao`
289: M*/
290: PETSC_EXTERN PetscErrorCode TaoLineSearchCreate_OWArmijo(TaoLineSearch ls)
291: {
292: TaoLineSearch_OWARMIJO *armP;
294: PetscFunctionBegin;
296: PetscCall(PetscNew(&armP));
298: armP->memory = NULL;
299: armP->alpha = 1.0;
300: armP->beta = 0.25;
301: armP->beta_inf = 0.25;
302: armP->sigma = 1e-4;
303: armP->memorySize = 1;
304: armP->referencePolicy = REFERENCE_MAX;
305: armP->replacementPolicy = REPLACE_MRU;
306: armP->nondescending = PETSC_FALSE;
307: ls->data = (void *)armP;
308: ls->initstep = 0.1;
309: ls->ops->monitor = NULL;
310: ls->ops->setup = NULL;
311: ls->ops->reset = NULL;
312: ls->ops->apply = TaoLineSearchApply_OWArmijo;
313: ls->ops->view = TaoLineSearchView_OWArmijo;
314: ls->ops->destroy = TaoLineSearchDestroy_OWArmijo;
315: ls->ops->setfromoptions = TaoLineSearchSetFromOptions_OWArmijo;
316: PetscFunctionReturn(PETSC_SUCCESS);
317: }