Actual source code: lmvm.c
1: #include <petsctaolinesearch.h>
2: #include <../src/tao/unconstrained/impls/lmvm/lmvm.h>
4: #define LMVM_STEP_BFGS 0
5: #define LMVM_STEP_GRAD 1
7: static PetscErrorCode TaoSolve_LMVM(Tao tao)
8: {
9: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
10: PetscReal f, fold, gdx, gnorm;
11: PetscReal step = 1.0;
12: PetscInt stepType = LMVM_STEP_GRAD, nupdates;
13: TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
15: PetscFunctionBegin;
16: if (tao->XL || tao->XU || tao->ops->computebounds) PetscCall(PetscInfo(tao, "WARNING: Variable bounds have been set but will be ignored by lmvm algorithm\n"));
18: /* Check convergence criteria */
19: PetscCall(TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient));
20: PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm));
22: PetscCheck(!PetscIsInfOrNanReal(f) && !PetscIsInfOrNanReal(gnorm), PetscObjectComm((PetscObject)tao), PETSC_ERR_USER, "User provided compute function generated Inf or NaN");
24: tao->reason = TAO_CONTINUE_ITERATING;
25: PetscCall(TaoLogConvergenceHistory(tao, f, gnorm, 0.0, tao->ksp_its));
26: PetscCall(TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step));
27: PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
28: if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(PETSC_SUCCESS);
30: /* Set counter for gradient/reset steps */
31: if (!lmP->recycle) {
32: lmP->bfgs = 0;
33: lmP->grad = 0;
34: PetscCall(MatLMVMReset(lmP->M, PETSC_FALSE));
35: }
37: /* Have not converged; continue with Newton method */
38: while (tao->reason == TAO_CONTINUE_ITERATING) {
39: /* Call general purpose update function */
40: PetscTryTypeMethod(tao, update, tao->niter, tao->user_update);
42: /* Compute direction */
43: if (lmP->H0) {
44: PetscCall(MatLMVMSetJ0(lmP->M, lmP->H0));
45: stepType = LMVM_STEP_BFGS;
46: }
47: PetscCall(MatLMVMUpdate(lmP->M, tao->solution, tao->gradient));
48: PetscCall(MatSolve(lmP->M, tao->gradient, lmP->D));
49: PetscCall(MatLMVMGetUpdateCount(lmP->M, &nupdates));
50: if (nupdates > 0) stepType = LMVM_STEP_BFGS;
52: /* Check for success (descent direction) */
53: PetscCall(VecDotRealPart(lmP->D, tao->gradient, &gdx));
54: if ((gdx <= 0.0) || PetscIsInfOrNanReal(gdx)) {
55: /* Step is not descent or direction produced not a number
56: We can assert bfgsUpdates > 1 in this case because
57: the first solve produces the scaled gradient direction,
58: which is guaranteed to be descent
60: Use steepest descent direction (scaled)
61: */
63: PetscCall(MatLMVMReset(lmP->M, PETSC_FALSE));
64: PetscCall(MatLMVMClearJ0(lmP->M));
65: PetscCall(MatLMVMUpdate(lmP->M, tao->solution, tao->gradient));
66: PetscCall(MatSolve(lmP->M, tao->gradient, lmP->D));
68: /* On a reset, the direction cannot be not a number; it is a
69: scaled gradient step. No need to check for this condition. */
70: stepType = LMVM_STEP_GRAD;
71: }
72: PetscCall(VecScale(lmP->D, -1.0));
74: /* Perform the linesearch */
75: fold = f;
76: PetscCall(VecCopy(tao->solution, lmP->Xold));
77: PetscCall(VecCopy(tao->gradient, lmP->Gold));
79: PetscCall(TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step, &ls_status));
80: PetscCall(TaoAddLineSearchCounts(tao));
82: if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER && (stepType != LMVM_STEP_GRAD)) {
83: /* Reset factors and use scaled gradient step */
84: f = fold;
85: PetscCall(VecCopy(lmP->Xold, tao->solution));
86: PetscCall(VecCopy(lmP->Gold, tao->gradient));
88: /* Failed to obtain acceptable iterate with BFGS step */
89: /* Attempt to use the scaled gradient direction */
91: PetscCall(MatLMVMReset(lmP->M, PETSC_FALSE));
92: PetscCall(MatLMVMClearJ0(lmP->M));
93: PetscCall(MatLMVMUpdate(lmP->M, tao->solution, tao->gradient));
94: PetscCall(MatSolve(lmP->M, tao->solution, tao->gradient));
96: /* On a reset, the direction cannot be not a number; it is a
97: scaled gradient step. No need to check for this condition. */
98: stepType = LMVM_STEP_GRAD;
99: PetscCall(VecScale(lmP->D, -1.0));
101: /* Perform the linesearch */
102: PetscCall(TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, lmP->D, &step, &ls_status));
103: PetscCall(TaoAddLineSearchCounts(tao));
104: }
106: if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
107: /* Failed to find an improving point */
108: f = fold;
109: PetscCall(VecCopy(lmP->Xold, tao->solution));
110: PetscCall(VecCopy(lmP->Gold, tao->gradient));
111: step = 0.0;
112: tao->reason = TAO_DIVERGED_LS_FAILURE;
113: } else {
114: /* LS found valid step, so tally up step type */
115: switch (stepType) {
116: case LMVM_STEP_BFGS:
117: ++lmP->bfgs;
118: break;
119: case LMVM_STEP_GRAD:
120: ++lmP->grad;
121: break;
122: default:
123: break;
124: }
125: /* Compute new gradient norm */
126: PetscCall(TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm));
127: }
129: /* Check convergence */
130: tao->niter++;
131: PetscCall(TaoLogConvergenceHistory(tao, f, gnorm, 0.0, tao->ksp_its));
132: PetscCall(TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step));
133: PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
134: }
135: PetscFunctionReturn(PETSC_SUCCESS);
136: }
138: static PetscErrorCode TaoSetUp_LMVM(Tao tao)
139: {
140: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
141: PetscInt n, N;
142: PetscBool is_set, is_spd;
144: PetscFunctionBegin;
145: /* Existence of tao->solution checked in TaoSetUp() */
146: if (!tao->gradient) PetscCall(VecDuplicate(tao->solution, &tao->gradient));
147: if (!tao->stepdirection) PetscCall(VecDuplicate(tao->solution, &tao->stepdirection));
148: if (!lmP->D) PetscCall(VecDuplicate(tao->solution, &lmP->D));
149: if (!lmP->Xold) PetscCall(VecDuplicate(tao->solution, &lmP->Xold));
150: if (!lmP->Gold) PetscCall(VecDuplicate(tao->solution, &lmP->Gold));
152: /* Create matrix for the limited memory approximation */
153: PetscCall(VecGetLocalSize(tao->solution, &n));
154: PetscCall(VecGetSize(tao->solution, &N));
155: PetscCall(MatSetSizes(lmP->M, n, n, N, N));
156: PetscCall(MatLMVMAllocate(lmP->M, tao->solution, tao->gradient));
157: PetscCall(MatIsSPDKnown(lmP->M, &is_set, &is_spd));
158: PetscCheck(is_set && is_spd, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix is not symmetric positive-definite.");
160: /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */
161: if (lmP->H0) PetscCall(MatLMVMSetJ0(lmP->M, lmP->H0));
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: /* ---------------------------------------------------------- */
166: static PetscErrorCode TaoDestroy_LMVM(Tao tao)
167: {
168: TAO_LMVM *lmP = (TAO_LMVM *)tao->data;
170: PetscFunctionBegin;
171: if (tao->setupcalled) {
172: PetscCall(VecDestroy(&lmP->Xold));
173: PetscCall(VecDestroy(&lmP->Gold));
174: PetscCall(VecDestroy(&lmP->D));
175: }
176: PetscCall(MatDestroy(&lmP->M));
177: if (lmP->H0) PetscCall(PetscObjectDereference((PetscObject)lmP->H0));
178: PetscCall(PetscFree(tao->data));
179: PetscFunctionReturn(PETSC_SUCCESS);
180: }
182: /*------------------------------------------------------------*/
183: static PetscErrorCode TaoSetFromOptions_LMVM(Tao tao, PetscOptionItems *PetscOptionsObject)
184: {
185: TAO_LMVM *lm = (TAO_LMVM *)tao->data;
187: PetscFunctionBegin;
188: PetscOptionsHeadBegin(PetscOptionsObject, "Limited-memory variable-metric method for unconstrained optimization");
189: PetscCall(PetscOptionsBool("-tao_lmvm_recycle", "enable recycling of the BFGS matrix between subsequent TaoSolve() calls", "", lm->recycle, &lm->recycle, NULL));
190: PetscCall(TaoLineSearchSetFromOptions(tao->linesearch));
191: PetscCall(MatSetFromOptions(lm->M));
192: PetscOptionsHeadEnd();
193: PetscFunctionReturn(PETSC_SUCCESS);
194: }
196: /*------------------------------------------------------------*/
197: static PetscErrorCode TaoView_LMVM(Tao tao, PetscViewer viewer)
198: {
199: TAO_LMVM *lm = (TAO_LMVM *)tao->data;
200: PetscBool isascii;
201: PetscInt recycled_its;
203: PetscFunctionBegin;
204: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
205: if (isascii) {
206: PetscCall(PetscViewerASCIIPushTab(viewer));
207: PetscCall(PetscViewerASCIIPrintf(viewer, "Gradient steps: %" PetscInt_FMT "\n", lm->grad));
208: if (lm->recycle) {
209: PetscCall(PetscViewerASCIIPrintf(viewer, "Recycle: on\n"));
210: recycled_its = lm->bfgs + lm->grad;
211: PetscCall(PetscViewerASCIIPrintf(viewer, "Total recycled iterations: %" PetscInt_FMT "\n", recycled_its));
212: }
213: PetscCall(PetscViewerASCIIPrintf(viewer, "LMVM Matrix:\n"));
214: PetscCall(PetscViewerASCIIPushTab(viewer));
215: PetscCall(MatView(lm->M, viewer));
216: PetscCall(PetscViewerASCIIPopTab(viewer));
217: PetscCall(PetscViewerASCIIPopTab(viewer));
218: }
219: PetscFunctionReturn(PETSC_SUCCESS);
220: }
222: /* ---------------------------------------------------------- */
224: /*MC
225: TAOLMVM - Limited Memory Variable Metric method is a quasi-Newton
226: optimization solver for unconstrained minimization. It solves
227: the Newton step
228: Hkdk = - gk
230: using an approximation Bk in place of Hk, where Bk is composed using
231: the BFGS update formula. A More-Thuente line search is then used
232: to computed the steplength in the dk direction
234: Options Database Keys:
235: + -tao_lmvm_recycle - enable recycling LMVM updates between TaoSolve() calls
236: - -tao_lmvm_no_scale - (developer) disables diagonal Broyden scaling on the LMVM approximation
238: Level: beginner
239: M*/
241: PETSC_EXTERN PetscErrorCode TaoCreate_LMVM(Tao tao)
242: {
243: TAO_LMVM *lmP;
244: const char *morethuente_type = TAOLINESEARCHMT;
246: PetscFunctionBegin;
247: tao->ops->setup = TaoSetUp_LMVM;
248: tao->ops->solve = TaoSolve_LMVM;
249: tao->ops->view = TaoView_LMVM;
250: tao->ops->setfromoptions = TaoSetFromOptions_LMVM;
251: tao->ops->destroy = TaoDestroy_LMVM;
253: PetscCall(PetscNew(&lmP));
254: lmP->D = NULL;
255: lmP->M = NULL;
256: lmP->Xold = NULL;
257: lmP->Gold = NULL;
258: lmP->H0 = NULL;
259: lmP->recycle = PETSC_FALSE;
261: tao->data = (void *)lmP;
262: /* Override default settings (unless already changed) */
263: if (!tao->max_it_changed) tao->max_it = 2000;
264: if (!tao->max_funcs_changed) tao->max_funcs = 4000;
266: PetscCall(TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch));
267: PetscCall(PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1));
268: PetscCall(TaoLineSearchSetType(tao->linesearch, morethuente_type));
269: PetscCall(TaoLineSearchUseTaoRoutines(tao->linesearch, tao));
270: PetscCall(TaoLineSearchSetOptionsPrefix(tao->linesearch, tao->hdr.prefix));
272: PetscCall(KSPInitializePackage());
273: PetscCall(MatCreate(((PetscObject)tao)->comm, &lmP->M));
274: PetscCall(PetscObjectIncrementTabLevel((PetscObject)lmP->M, (PetscObject)tao, 1));
275: PetscCall(MatSetType(lmP->M, MATLMVMBFGS));
276: PetscCall(MatSetOptionsPrefix(lmP->M, "tao_lmvm_"));
277: PetscFunctionReturn(PETSC_SUCCESS);
278: }