Actual source code: lmvm.c
1: #include "taolinesearch.h"
2: #include "src/matrix/lmvmmat.h"
3: #include "lmvm.h"
5: #define LMVM_BFGS 0
6: #define LMVM_SCALED_GRADIENT 1
7: #define LMVM_GRADIENT 2
11: static PetscErrorCode TaoSolve_LMVM(TaoSolver tao)
12: {
14: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
15:
16: PetscReal f, fold, gdx, gnorm;
17: PetscReal step = 1.0;
19: PetscReal delta;
22: PetscInt stepType;
23: PetscInt iter = 0;
24: TaoSolverTerminationReason reason = TAO_CONTINUE_ITERATING;
25: TaoLineSearchTerminationReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
29: if (tao->XL || tao->XU || tao->ops->computebounds) {
30: PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by lmvm algorithm\n");
31: }
33: /* Check convergence criteria */
34: TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);
35: VecNorm(tao->gradient,NORM_2,&gnorm);
36: if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) {
37: SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
38: }
40: TaoMonitor(tao, iter, f, gnorm, 0.0, step, &reason);
41: if (reason != TAO_CONTINUE_ITERATING) {
42: return(0);
43: }
45: /* Set initial scaling for the function */
46: if (f != 0.0) {
47: delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
48: }
49: else {
50: delta = 2.0 / (gnorm*gnorm);
51: }
52: MatLMVMSetDelta(lmP->M,delta);
54: /* Set counter for gradient/reset steps */
55: lmP->bfgs = 0;
56: lmP->sgrad = 0;
57: lmP->grad = 0;
59: /* Have not converged; continue with Newton method */
60: while (reason == TAO_CONTINUE_ITERATING) {
61: /* Compute direction */
62: MatLMVMUpdate(lmP->M,tao->solution,tao->gradient);
63: MatLMVMSolve(lmP->M, tao->gradient, lmP->D);
64: ++lmP->bfgs;
66: /* Check for success (descent direction) */
67: VecDot(lmP->D, tao->gradient, &gdx);
68: if ((gdx <= 0.0) || PetscIsInfOrNanReal(gdx)) {
69: /* Step is not descent or direction produced not a number
70: We can assert bfgsUpdates > 1 in this case because
71: the first solve produces the scaled gradient direction,
72: which is guaranteed to be descent
73:
74: Use steepest descent direction (scaled)
75: */
77: ++lmP->grad;
79: if (f != 0.0) {
80: delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
81: }
82: else {
83: delta = 2.0 / (gnorm*gnorm);
84: }
85: MatLMVMSetDelta(lmP->M, delta);
86: MatLMVMReset(lmP->M);
87: MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
88: MatLMVMSolve(lmP->M,tao->gradient, lmP->D);
90: /* On a reset, the direction cannot be not a number; it is a
91: scaled gradient step. No need to check for this condition. */
93: lmP->bfgs = 1;
94: ++lmP->sgrad;
95: stepType = LMVM_SCALED_GRADIENT;
96: }
97: else {
98: if (1 == lmP->bfgs) {
99: /* The first BFGS direction is always the scaled gradient */
100: ++lmP->sgrad;
101: stepType = LMVM_SCALED_GRADIENT;
102: }
103: else {
104: ++lmP->bfgs;
105: stepType = LMVM_BFGS;
106: }
107: }
108: VecScale(lmP->D, -1.0);
109:
110: /* Perform the linesearch */
111: fold = f;
112: VecCopy(tao->solution, lmP->Xold);
113: VecCopy(tao->gradient, lmP->Gold);
115: TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step,&ls_status);
116: TaoAddLineSearchCounts(tao);
117:
119: while (((int)ls_status < 0) && (stepType != LMVM_GRADIENT)) {
120: /* Linesearch failed */
121: /* Reset factors and use scaled gradient step */
122: f = fold;
123: VecCopy(lmP->Xold, tao->solution);
124: VecCopy(lmP->Gold, tao->gradient);
125:
126: switch(stepType) {
127: case LMVM_BFGS:
128: /* Failed to obtain acceptable iterate with BFGS step */
129: /* Attempt to use the scaled gradient direction */
131: if (f != 0.0) {
132: delta = 2.0 * PetscAbsScalar(f) / (gnorm*gnorm);
133: }
134: else {
135: delta = 2.0 / (gnorm*gnorm);
136: }
137: MatLMVMSetDelta(lmP->M, delta);
138: MatLMVMReset(lmP->M);
139: MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
140: MatLMVMSolve(lmP->M, tao->gradient, lmP->D);
142: /* On a reset, the direction cannot be not a number; it is a
143: scaled gradient step. No need to check for this condition. */
144:
145: lmP->bfgs = 1;
146: ++lmP->sgrad;
147: stepType = LMVM_SCALED_GRADIENT;
148: break;
150: case LMVM_SCALED_GRADIENT:
151: /* The scaled gradient step did not produce a new iterate;
152: attempt to use the gradient direction.
153: Need to make sure we are not using a different diagonal scaling */
154: MatLMVMSetDelta(lmP->M, 1.0);
155: MatLMVMReset(lmP->M);
156: MatLMVMUpdate(lmP->M, tao->solution, tao->gradient);
157: MatLMVMSolve(lmP->M, tao->gradient, lmP->D);
159: lmP->bfgs = 1;
160: ++lmP->grad;
161: stepType = LMVM_GRADIENT;
162: break;
163: }
164: VecScale(lmP->D, -1.0);
165:
166: /* Perform the linesearch */
167: TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step, &ls_status);
168: TaoAddLineSearchCounts(tao);
169:
170: }
172: if ((int)ls_status < 0) {
173: /* Failed to find an improving point */
174: f = fold;
175: VecCopy(lmP->Xold, tao->solution);
176: VecCopy(lmP->Gold, tao->gradient);
177: step = 0.0;
178: }
179: /* Check for termination */
180: VecNorm(tao->gradient, NORM_2, &gnorm);
181: iter++;
182: TaoMonitor(tao,iter,f,gnorm,0.0,step,&reason);
183: }
184: return(0);
185: }
188: static PetscErrorCode TaoSetUp_LMVM(TaoSolver tao)
189: {
190: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
191: PetscInt n,N;
195: /* Existence of tao->solution checked in TaoSetUp() */
196: if (!tao->gradient) {VecDuplicate(tao->solution,&tao->gradient); }
197: if (!tao->stepdirection) {VecDuplicate(tao->solution,&tao->stepdirection); }
198: if (!lmP->D) {VecDuplicate(tao->solution,&lmP->D); }
199: if (!lmP->Xold) {VecDuplicate(tao->solution,&lmP->Xold); }
200: if (!lmP->Gold) {VecDuplicate(tao->solution,&lmP->Gold); }
201:
202: /* Create matrix for the limited memory approximation */
203: VecGetLocalSize(tao->solution,&n);
204: VecGetSize(tao->solution,&N);
205: MatCreateLMVM(((PetscObject)tao)->comm,n,N,&lmP->M);
206: MatLMVMAllocateVectors(lmP->M,tao->solution);
207:
209: return(0);
210: }
212: /* ---------------------------------------------------------- */
215: static PetscErrorCode TaoDestroy_LMVM(TaoSolver tao)
216: {
218: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
222: if (tao->setupcalled) {
223: VecDestroy(&lmP->Xold);
224: VecDestroy(&lmP->Gold);
225: VecDestroy(&lmP->D);
226: MatDestroy(&lmP->M);
227: }
228: PetscFree(tao->data);
229: tao->data = PETSC_NULL;
231: return(0);
232: }
234: /*------------------------------------------------------------*/
237: static PetscErrorCode TaoSetFromOptions_LMVM(TaoSolver tao)
238: {
243: PetscOptionsHead("Limited-memory variable-metric method for unconstrained optimization");
244: TaoLineSearchSetFromOptions(tao->linesearch);
245: PetscOptionsTail();
246: return(0);
248: return(0);
249: }
251: /*------------------------------------------------------------*/
254: static PetscErrorCode TaoView_LMVM(TaoSolver tao, PetscViewer viewer)
255: {
257: TAO_LMVM *lm = (TAO_LMVM *)tao->data;
258: PetscBool isascii;
263: PetscTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);
264: if (isascii) {
266: PetscViewerASCIIPushTab(viewer);
267: PetscViewerASCIIPrintf(viewer, "BFGS steps: %D\n", lm->bfgs);
268: PetscViewerASCIIPrintf(viewer, "Scaled gradient steps: %D\n", lm->sgrad);
269: PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lm->grad);
270: PetscViewerASCIIPopTab(viewer);
271: } else {
272: SETERRQ1(((PetscObject)tao)->comm,PETSC_ERR_SUP,"Viewer type %s not supported for TAO LMVM",((PetscObject)viewer)->type_name);
273: }
274: return(0);
275: }
277: /* ---------------------------------------------------------- */
282: PetscErrorCode TaoCreate_LMVM(TaoSolver tao)
283: {
284:
285: TAO_LMVM *lmP;
286: const char *morethuente_type = TAOLINESEARCH_MT;
290: tao->ops->setup = TaoSetUp_LMVM;
291: tao->ops->solve = TaoSolve_LMVM;
292: tao->ops->view = TaoView_LMVM;
293: tao->ops->setfromoptions = TaoSetFromOptions_LMVM;
294: tao->ops->destroy = TaoDestroy_LMVM;
296: PetscNewLog(tao,TAO_LMVM, &lmP);
297: lmP->D = 0;
298: lmP->M = 0;
299: lmP->Xold = 0;
300: lmP->Gold = 0;
302: tao->data = (void*)lmP;
303: tao->max_it = 2000;
304: tao->max_funcs = 4000;
305: tao->fatol = 1e-4;
306: tao->frtol = 1e-4;
308: TaoLineSearchCreate(((PetscObject)tao)->comm,&tao->linesearch);
309: TaoLineSearchSetType(tao->linesearch,morethuente_type);
310: TaoLineSearchUseTaoSolverRoutines(tao->linesearch,tao);
312: return(0);
313: }