1 #include <petsctaolinesearch.h> /*I "petsctaolinesearch.h" I*/
2 #include <../src/tao/unconstrained/impls/lmvm/lmvm.h>
3 #include <../src/tao/bound/impls/blmvm/blmvm.h>
4
5 /*------------------------------------------------------------*/
TaoSolve_BLMVM(Tao tao)6 static PetscErrorCode TaoSolve_BLMVM(Tao tao)
7 {
8 PetscErrorCode ierr;
9 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data;
10 TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
11 PetscReal f, fold, gdx, gnorm, gnorm2;
12 PetscReal stepsize = 1.0,delta;
13
14 PetscFunctionBegin;
15 /* Project initial point onto bounds */
16 ierr = TaoComputeVariableBounds(tao);CHKERRQ(ierr);
17 ierr = VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);CHKERRQ(ierr);
18 ierr = TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);CHKERRQ(ierr);
19
20
21 /* Check convergence criteria */
22 ierr = TaoComputeObjectiveAndGradient(tao, tao->solution,&f,blmP->unprojected_gradient);CHKERRQ(ierr);
23 ierr = VecBoundGradientProjection(blmP->unprojected_gradient,tao->solution, tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);
24
25 ierr = TaoGradientNorm(tao, tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
26 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_USER, "User provided compute function generated Inf or NaN");
27
28 tao->reason = TAO_CONTINUE_ITERATING;
29 ierr = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr);
30 ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr);
31 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
32 if (tao->reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);
33
34 /* Set counter for gradient/reset steps */
35 if (!blmP->recycle) {
36 blmP->grad = 0;
37 blmP->reset = 0;
38 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);
39 }
40
41 /* Have not converged; continue with Newton method */
42 while (tao->reason == TAO_CONTINUE_ITERATING) {
43 /* Call general purpose update function */
44 if (tao->ops->update) {
45 ierr = (*tao->ops->update)(tao, tao->niter, tao->user_update);CHKERRQ(ierr);
46 }
47 /* Compute direction */
48 gnorm2 = gnorm*gnorm;
49 if (gnorm2 == 0.0) gnorm2 = PETSC_MACHINE_EPSILON;
50 if (f == 0.0) {
51 delta = 2.0 / gnorm2;
52 } else {
53 delta = 2.0 * PetscAbsScalar(f) / gnorm2;
54 }
55 ierr = MatLMVMSymBroydenSetDelta(blmP->M, delta);CHKERRQ(ierr);
56 ierr = MatLMVMUpdate(blmP->M, tao->solution, tao->gradient);CHKERRQ(ierr);
57 ierr = MatSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
58 ierr = VecBoundGradientProjection(tao->stepdirection,tao->solution,tao->XL,tao->XU,tao->gradient);CHKERRQ(ierr);
59
60 /* Check for success (descent direction) */
61 ierr = VecDot(blmP->unprojected_gradient, tao->gradient, &gdx);CHKERRQ(ierr);
62 if (gdx <= 0) {
63 /* Step is not descent or solve was not successful
64 Use steepest descent direction (scaled) */
65 ++blmP->grad;
66
67 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);
68 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
69 ierr = MatSolve(blmP->M,blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
70 }
71 ierr = VecScale(tao->stepdirection,-1.0);CHKERRQ(ierr);
72
73 /* Perform the linesearch */
74 fold = f;
75 ierr = VecCopy(tao->solution, blmP->Xold);CHKERRQ(ierr);
76 ierr = VecCopy(blmP->unprojected_gradient, blmP->Gold);CHKERRQ(ierr);
77 ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr);
78 ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr);
79 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
80
81 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
82 /* Linesearch failed
83 Reset factors and use scaled (projected) gradient step */
84 ++blmP->reset;
85
86 f = fold;
87 ierr = VecCopy(blmP->Xold, tao->solution);CHKERRQ(ierr);
88 ierr = VecCopy(blmP->Gold, blmP->unprojected_gradient);CHKERRQ(ierr);
89
90 ierr = MatLMVMReset(blmP->M, PETSC_FALSE);CHKERRQ(ierr);
91 ierr = MatLMVMUpdate(blmP->M, tao->solution, blmP->unprojected_gradient);CHKERRQ(ierr);
92 ierr = MatSolve(blmP->M, blmP->unprojected_gradient, tao->stepdirection);CHKERRQ(ierr);
93 ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
94
95 /* This may be incorrect; linesearch has values for stepmax and stepmin
96 that should be reset. */
97 ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);CHKERRQ(ierr);
98 ierr = TaoLineSearchApply(tao->linesearch,tao->solution,&f, blmP->unprojected_gradient, tao->stepdirection, &stepsize, &ls_status);CHKERRQ(ierr);
99 ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
100
101 if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
102 tao->reason = TAO_DIVERGED_LS_FAILURE;
103 break;
104 }
105 }
106
107 /* Check for converged */
108 ierr = VecBoundGradientProjection(blmP->unprojected_gradient, tao->solution, tao->XL, tao->XU, tao->gradient);CHKERRQ(ierr);
109 ierr = TaoGradientNorm(tao, tao->gradient, NORM_2, &gnorm);CHKERRQ(ierr);
110 if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_USER, "User provided compute function generated Not-a-Number");
111 tao->niter++;
112 ierr = TaoLogConvergenceHistory(tao,f,gnorm,0.0,tao->ksp_its);CHKERRQ(ierr);
113 ierr = TaoMonitor(tao,tao->niter,f,gnorm,0.0,stepsize);CHKERRQ(ierr);
114 ierr = (*tao->ops->convergencetest)(tao,tao->cnvP);CHKERRQ(ierr);
115 }
116 PetscFunctionReturn(0);
117 }
118
TaoSetup_BLMVM(Tao tao)119 static PetscErrorCode TaoSetup_BLMVM(Tao tao)
120 {
121 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data;
122 PetscErrorCode ierr;
123
124 PetscFunctionBegin;
125 /* Existence of tao->solution checked in TaoSetup() */
126 ierr = VecDuplicate(tao->solution,&blmP->Xold);CHKERRQ(ierr);
127 ierr = VecDuplicate(tao->solution,&blmP->Gold);CHKERRQ(ierr);
128 ierr = VecDuplicate(tao->solution, &blmP->unprojected_gradient);CHKERRQ(ierr);
129
130 if (!tao->stepdirection) {
131 ierr = VecDuplicate(tao->solution, &tao->stepdirection);CHKERRQ(ierr);
132 }
133 if (!tao->gradient) {
134 ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr);
135 }
136 if (!tao->XL) {
137 ierr = VecDuplicate(tao->solution,&tao->XL);CHKERRQ(ierr);
138 ierr = VecSet(tao->XL,PETSC_NINFINITY);CHKERRQ(ierr);
139 }
140 if (!tao->XU) {
141 ierr = VecDuplicate(tao->solution,&tao->XU);CHKERRQ(ierr);
142 ierr = VecSet(tao->XU,PETSC_INFINITY);CHKERRQ(ierr);
143 }
144 /* Allocate matrix for the limited memory approximation */
145 ierr = MatLMVMAllocate(blmP->M,tao->solution,blmP->unprojected_gradient);CHKERRQ(ierr);
146
147 /* If the user has set a matrix to solve as the initial H0, set the options prefix here, and set up the KSP */
148 if (blmP->H0) {
149 ierr = MatLMVMSetJ0(blmP->M, blmP->H0);CHKERRQ(ierr);
150 }
151 PetscFunctionReturn(0);
152 }
153
154 /* ---------------------------------------------------------- */
TaoDestroy_BLMVM(Tao tao)155 static PetscErrorCode TaoDestroy_BLMVM(Tao tao)
156 {
157 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data;
158 PetscErrorCode ierr;
159
160 PetscFunctionBegin;
161 if (tao->setupcalled) {
162 ierr = VecDestroy(&blmP->unprojected_gradient);CHKERRQ(ierr);
163 ierr = VecDestroy(&blmP->Xold);CHKERRQ(ierr);
164 ierr = VecDestroy(&blmP->Gold);CHKERRQ(ierr);
165 }
166 ierr = MatDestroy(&blmP->M);CHKERRQ(ierr);
167 if (blmP->H0) {
168 PetscObjectDereference((PetscObject)blmP->H0);
169 }
170 ierr = PetscFree(tao->data);CHKERRQ(ierr);
171 PetscFunctionReturn(0);
172 }
173
174 /*------------------------------------------------------------*/
TaoSetFromOptions_BLMVM(PetscOptionItems * PetscOptionsObject,Tao tao)175 static PetscErrorCode TaoSetFromOptions_BLMVM(PetscOptionItems* PetscOptionsObject,Tao tao)
176 {
177 TAO_BLMVM *blmP = (TAO_BLMVM *)tao->data;
178 PetscErrorCode ierr;
179 PetscBool is_spd;
180
181 PetscFunctionBegin;
182 ierr = PetscOptionsHead(PetscOptionsObject,"Limited-memory variable-metric method for bound constrained optimization");CHKERRQ(ierr);
183 ierr = PetscOptionsBool("-tao_blmvm_recycle","enable recycling of the BFGS matrix between subsequent TaoSolve() calls","",blmP->recycle,&blmP->recycle,NULL);CHKERRQ(ierr);
184 ierr = PetscOptionsTail();CHKERRQ(ierr);
185 ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
186 ierr = MatSetFromOptions(blmP->M);CHKERRQ(ierr);
187 ierr = MatGetOption(blmP->M, MAT_SPD, &is_spd);CHKERRQ(ierr);
188 if (!is_spd) SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite");
189 PetscFunctionReturn(0);
190 }
191
192
193 /*------------------------------------------------------------*/
TaoView_BLMVM(Tao tao,PetscViewer viewer)194 static PetscErrorCode TaoView_BLMVM(Tao tao, PetscViewer viewer)
195 {
196 TAO_BLMVM *lmP = (TAO_BLMVM *)tao->data;
197 PetscBool isascii;
198 PetscErrorCode ierr;
199
200 PetscFunctionBegin;
201 ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr);
202 if (isascii) {
203 ierr = PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", lmP->grad);CHKERRQ(ierr);
204 ierr = PetscViewerPushFormat(viewer, PETSC_VIEWER_ASCII_INFO);CHKERRQ(ierr);
205 ierr = MatView(lmP->M, viewer);CHKERRQ(ierr);
206 ierr = PetscViewerPopFormat(viewer);CHKERRQ(ierr);
207 }
208 PetscFunctionReturn(0);
209 }
210
TaoComputeDual_BLMVM(Tao tao,Vec DXL,Vec DXU)211 static PetscErrorCode TaoComputeDual_BLMVM(Tao tao, Vec DXL, Vec DXU)
212 {
213 TAO_BLMVM *blm = (TAO_BLMVM *) tao->data;
214 PetscErrorCode ierr;
215
216 PetscFunctionBegin;
217 PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
218 PetscValidHeaderSpecific(DXL,VEC_CLASSID,2);
219 PetscValidHeaderSpecific(DXU,VEC_CLASSID,3);
220 if (!tao->gradient || !blm->unprojected_gradient) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Dual variables don't exist yet or no longer exist.\n");
221
222 ierr = VecCopy(tao->gradient,DXL);CHKERRQ(ierr);
223 ierr = VecAXPY(DXL,-1.0,blm->unprojected_gradient);CHKERRQ(ierr);
224 ierr = VecSet(DXU,0.0);CHKERRQ(ierr);
225 ierr = VecPointwiseMax(DXL,DXL,DXU);CHKERRQ(ierr);
226
227 ierr = VecCopy(blm->unprojected_gradient,DXU);CHKERRQ(ierr);
228 ierr = VecAXPY(DXU,-1.0,tao->gradient);CHKERRQ(ierr);
229 ierr = VecAXPY(DXU,1.0,DXL);CHKERRQ(ierr);
230 PetscFunctionReturn(0);
231 }
232
233 /* ---------------------------------------------------------- */
234 /*MC
235 TAOBLMVM - Bounded limited memory variable metric is a quasi-Newton method
236 for nonlinear minimization with bound constraints. It is an extension
237 of TAOLMVM
238
239 Options Database Keys:
240 . -tao_lmm_recycle - enable recycling of LMVM information between subsequent TaoSolve calls
241
242 Level: beginner
243 M*/
TaoCreate_BLMVM(Tao tao)244 PETSC_EXTERN PetscErrorCode TaoCreate_BLMVM(Tao tao)
245 {
246 TAO_BLMVM *blmP;
247 const char *morethuente_type = TAOLINESEARCHMT;
248 PetscErrorCode ierr;
249
250 PetscFunctionBegin;
251 tao->ops->setup = TaoSetup_BLMVM;
252 tao->ops->solve = TaoSolve_BLMVM;
253 tao->ops->view = TaoView_BLMVM;
254 tao->ops->setfromoptions = TaoSetFromOptions_BLMVM;
255 tao->ops->destroy = TaoDestroy_BLMVM;
256 tao->ops->computedual = TaoComputeDual_BLMVM;
257
258 ierr = PetscNewLog(tao,&blmP);CHKERRQ(ierr);
259 blmP->H0 = NULL;
260 blmP->recycle = PETSC_FALSE;
261 tao->data = (void*)blmP;
262
263 /* Override default settings (unless already changed) */
264 if (!tao->max_it_changed) tao->max_it = 2000;
265 if (!tao->max_funcs_changed) tao->max_funcs = 4000;
266
267 ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
268 ierr = PetscObjectIncrementTabLevel((PetscObject)tao->linesearch, (PetscObject)tao, 1);CHKERRQ(ierr);
269 ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr);
270 ierr = TaoLineSearchUseTaoRoutines(tao->linesearch,tao);CHKERRQ(ierr);
271 ierr = TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);CHKERRQ(ierr);
272
273 ierr = KSPInitializePackage();CHKERRQ(ierr);
274 ierr = MatCreate(((PetscObject)tao)->comm, &blmP->M);CHKERRQ(ierr);
275 ierr = MatSetType(blmP->M, MATLMVMBFGS);CHKERRQ(ierr);
276 ierr = PetscObjectIncrementTabLevel((PetscObject)blmP->M, (PetscObject)tao, 1);CHKERRQ(ierr);
277 ierr = MatSetOptionsPrefix(blmP->M, "tao_blmvm_");CHKERRQ(ierr);
278 PetscFunctionReturn(0);
279 }
280
281 /*@
282 TaoLMVMRecycle - Enable/disable recycling of the QN history between subsequent TaoSolve calls.
283
284 Input Parameters:
285 + tao - the Tao solver context
286 - flg - Boolean flag for recycling (PETSC_TRUE or PETSC_FALSE)
287
288 Level: intermediate
289 @*/
TaoLMVMRecycle(Tao tao,PetscBool flg)290 PetscErrorCode TaoLMVMRecycle(Tao tao, PetscBool flg)
291 {
292 TAO_LMVM *lmP;
293 TAO_BLMVM *blmP;
294 PetscBool is_lmvm, is_blmvm;
295 PetscErrorCode ierr;
296
297 PetscFunctionBegin;
298 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOLMVM,&is_lmvm);CHKERRQ(ierr);
299 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOBLMVM,&is_blmvm);CHKERRQ(ierr);
300 if (is_lmvm) {
301 lmP = (TAO_LMVM *)tao->data;
302 lmP->recycle = flg;
303 } else if (is_blmvm) {
304 blmP = (TAO_BLMVM *)tao->data;
305 blmP->recycle = flg;
306 }
307 PetscFunctionReturn(0);
308 }
309
310 /*@
311 TaoLMVMSetH0 - Set the initial Hessian for the QN approximation
312
313 Input Parameters:
314 + tao - the Tao solver context
315 - H0 - Mat object for the initial Hessian
316
317 Level: advanced
318
319 .seealso: TaoLMVMGetH0(), TaoLMVMGetH0KSP()
320 @*/
TaoLMVMSetH0(Tao tao,Mat H0)321 PetscErrorCode TaoLMVMSetH0(Tao tao, Mat H0)
322 {
323 TAO_LMVM *lmP;
324 TAO_BLMVM *blmP;
325 PetscBool is_lmvm, is_blmvm;
326 PetscErrorCode ierr;
327
328 PetscFunctionBegin;
329 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOLMVM,&is_lmvm);CHKERRQ(ierr);
330 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOBLMVM,&is_blmvm);CHKERRQ(ierr);
331 if (is_lmvm) {
332 lmP = (TAO_LMVM *)tao->data;
333 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr);
334 lmP->H0 = H0;
335 } else if (is_blmvm) {
336 blmP = (TAO_BLMVM *)tao->data;
337 ierr = PetscObjectReference((PetscObject)H0);CHKERRQ(ierr);
338 blmP->H0 = H0;
339 }
340 PetscFunctionReturn(0);
341 }
342
343 /*@
344 TaoLMVMGetH0 - Get the matrix object for the QN initial Hessian
345
346 Input Parameters:
347 . tao - the Tao solver context
348
349 Output Parameters:
350 . H0 - Mat object for the initial Hessian
351
352 Level: advanced
353
354 .seealso: TaoLMVMSetH0(), TaoLMVMGetH0KSP()
355 @*/
TaoLMVMGetH0(Tao tao,Mat * H0)356 PetscErrorCode TaoLMVMGetH0(Tao tao, Mat *H0)
357 {
358 TAO_LMVM *lmP;
359 TAO_BLMVM *blmP;
360 PetscBool is_lmvm, is_blmvm;
361 Mat M;
362 PetscErrorCode ierr;
363
364 PetscFunctionBegin;
365 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOLMVM,&is_lmvm);CHKERRQ(ierr);
366 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOBLMVM,&is_blmvm);CHKERRQ(ierr);
367 if (is_lmvm) {
368 lmP = (TAO_LMVM *)tao->data;
369 M = lmP->M;
370 } else if (is_blmvm) {
371 blmP = (TAO_BLMVM *)tao->data;
372 M = blmP->M;
373 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONG, "This routine applies to TAO_LMVM and TAO_BLMVM.");
374 ierr = MatLMVMGetJ0(M, H0);CHKERRQ(ierr);
375 PetscFunctionReturn(0);
376 }
377
378 /*@
379 TaoLMVMGetH0KSP - Get the iterative solver for applying the inverse of the QN initial Hessian
380
381 Input Parameters:
382 . tao - the Tao solver context
383
384 Output Parameters:
385 . ksp - KSP solver context for the initial Hessian
386
387 Level: advanced
388
389 .seealso: TaoLMVMGetH0(), TaoLMVMGetH0KSP()
390 @*/
TaoLMVMGetH0KSP(Tao tao,KSP * ksp)391 PetscErrorCode TaoLMVMGetH0KSP(Tao tao, KSP *ksp)
392 {
393 TAO_LMVM *lmP;
394 TAO_BLMVM *blmP;
395 PetscBool is_lmvm, is_blmvm;
396 Mat M;
397 PetscErrorCode ierr;
398
399 PetscFunctionBegin;
400 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOLMVM,&is_lmvm);CHKERRQ(ierr);
401 ierr = PetscObjectTypeCompare((PetscObject)tao,TAOBLMVM,&is_blmvm);CHKERRQ(ierr);
402 if (is_lmvm) {
403 lmP = (TAO_LMVM *)tao->data;
404 M = lmP->M;
405 } else if (is_blmvm) {
406 blmP = (TAO_BLMVM *)tao->data;
407 M = blmP->M;
408 } else SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_WRONG, "This routine applies to TAO_LMVM and TAO_BLMVM.");
409 ierr = MatLMVMGetJ0KSP(M, ksp);CHKERRQ(ierr);
410 PetscFunctionReturn(0);
411 }
412