Actual source code: gpcg.c
petsc-3.8.4 2018-03-24
1: #include <petscksp.h>
2: #include <../src/tao/bound/impls/gpcg/gpcg.h>
5: static PetscErrorCode GPCGGradProjections(Tao tao);
6: static PetscErrorCode GPCGObjectiveAndGradient(TaoLineSearch,Vec,PetscReal*,Vec,void*);
8: /*------------------------------------------------------------*/
9: static PetscErrorCode TaoDestroy_GPCG(Tao tao)
10: {
11: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
14: /* Free allocated memory in GPCG structure */
16: VecDestroy(&gpcg->B);
17: VecDestroy(&gpcg->Work);
18: VecDestroy(&gpcg->X_New);
19: VecDestroy(&gpcg->G_New);
20: VecDestroy(&gpcg->DXFree);
21: VecDestroy(&gpcg->R);
22: VecDestroy(&gpcg->PG);
23: MatDestroy(&gpcg->Hsub);
24: MatDestroy(&gpcg->Hsub_pre);
25: ISDestroy(&gpcg->Free_Local);
26: PetscFree(tao->data);
27: return(0);
28: }
30: /*------------------------------------------------------------*/
31: static PetscErrorCode TaoSetFromOptions_GPCG(PetscOptionItems *PetscOptionsObject,Tao tao)
32: {
33: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
35: PetscBool flg;
38: PetscOptionsHead(PetscOptionsObject,"Gradient Projection, Conjugate Gradient method for bound constrained optimization");
39: ierr=PetscOptionsInt("-tao_gpcg_maxpgits","maximum number of gradient projections per GPCG iterate",NULL,gpcg->maxgpits,&gpcg->maxgpits,&flg);
40: PetscOptionsTail();
41: KSPSetFromOptions(tao->ksp);
42: TaoLineSearchSetFromOptions(tao->linesearch);
43: return(0);
44: }
46: /*------------------------------------------------------------*/
47: static PetscErrorCode TaoView_GPCG(Tao tao, PetscViewer viewer)
48: {
49: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
50: PetscBool isascii;
54: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
55: if (isascii) {
56: PetscViewerASCIIPushTab(viewer);
57: PetscViewerASCIIPrintf(viewer,"Total PG its: %D,",gpcg->total_gp_its);
58: PetscViewerASCIIPrintf(viewer,"PG tolerance: %g \n",(double)gpcg->pg_ftol);
59: PetscViewerASCIIPopTab(viewer);
60: }
61: TaoLineSearchView(tao->linesearch,viewer);
62: return(0);
63: }
65: /* GPCGObjectiveAndGradient()
66: Compute f=0.5 * x'Hx + b'x + c
67: g=Hx + b
68: */
69: static PetscErrorCode GPCGObjectiveAndGradient(TaoLineSearch ls, Vec X, PetscReal *f, Vec G, void*tptr)
70: {
71: Tao tao = (Tao)tptr;
72: TAO_GPCG *gpcg = (TAO_GPCG*)tao->data;
74: PetscReal f1,f2;
77: MatMult(tao->hessian,X,G);
78: VecDot(G,X,&f1);
79: VecDot(gpcg->B,X,&f2);
80: VecAXPY(G,1.0,gpcg->B);
81: *f=f1/2.0 + f2 + gpcg->c;
82: return(0);
83: }
85: /* ---------------------------------------------------------- */
86: static PetscErrorCode TaoSetup_GPCG(Tao tao)
87: {
89: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
92: /* Allocate some arrays */
93: if (!tao->gradient) {
94: VecDuplicate(tao->solution, &tao->gradient);
95: }
96: if (!tao->stepdirection) {
97: VecDuplicate(tao->solution, &tao->stepdirection);
98: }
99: if (!tao->XL) {
100: VecDuplicate(tao->solution,&tao->XL);
101: VecSet(tao->XL,PETSC_NINFINITY);
102: }
103: if (!tao->XU) {
104: VecDuplicate(tao->solution,&tao->XU);
105: VecSet(tao->XU,PETSC_INFINITY);
106: }
108: ierr=VecDuplicate(tao->solution,&gpcg->B);
109: ierr=VecDuplicate(tao->solution,&gpcg->Work);
110: ierr=VecDuplicate(tao->solution,&gpcg->X_New);
111: ierr=VecDuplicate(tao->solution,&gpcg->G_New);
112: ierr=VecDuplicate(tao->solution,&gpcg->DXFree);
113: ierr=VecDuplicate(tao->solution,&gpcg->R);
114: ierr=VecDuplicate(tao->solution,&gpcg->PG);
115: /*
116: if (gpcg->ksp_type == GPCG_KSP_NASH) {
117: KSPSetType(tao->ksp,KSPCGNASH);
118: } else if (gpcg->ksp_type == GPCG_KSP_STCG) {
119: KSPSetType(tao->ksp,KSPCGSTCG);
120: } else {
121: KSPSetType(tao->ksp,KSPCGGLTR);
122: }
123: if (tao->ksp->ops->setfromoptions) {
124: (*tao->ksp->ops->setfromoptions)(tao->ksp);
125: }
127: }
128: */
129: return(0);
130: }
132: static PetscErrorCode TaoSolve_GPCG(Tao tao)
133: {
134: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
135: PetscErrorCode ierr;
136: PetscInt its;
137: PetscReal actred,f,f_new,gnorm,gdx,stepsize,xtb;
138: PetscReal xtHx;
139: TaoConvergedReason reason = TAO_CONTINUE_ITERATING;
140: TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
144: TaoComputeVariableBounds(tao);
145: VecMedian(tao->XL,tao->solution,tao->XU,tao->solution);
146: TaoLineSearchSetVariableBounds(tao->linesearch,tao->XL,tao->XU);
148: /* Using f = .5*x'Hx + x'b + c and g=Hx + b, compute b,c */
149: TaoComputeHessian(tao,tao->solution,tao->hessian,tao->hessian_pre);
150: TaoComputeObjectiveAndGradient(tao,tao->solution,&f,tao->gradient);
151: VecCopy(tao->gradient, gpcg->B);
152: MatMult(tao->hessian,tao->solution,gpcg->Work);
153: VecDot(gpcg->Work, tao->solution, &xtHx);
154: VecAXPY(gpcg->B,-1.0,gpcg->Work);
155: VecDot(gpcg->B,tao->solution,&xtb);
156: gpcg->c=f-xtHx/2.0-xtb;
157: if (gpcg->Free_Local) {
158: ISDestroy(&gpcg->Free_Local);
159: }
160: VecWhichBetween(tao->XL,tao->solution,tao->XU,&gpcg->Free_Local);
162: /* Project the gradient and calculate the norm */
163: VecCopy(tao->gradient,gpcg->G_New);
164: VecBoundGradientProjection(tao->gradient,tao->solution,tao->XL,tao->XU,gpcg->PG);
165: VecNorm(gpcg->PG,NORM_2,&gpcg->gnorm);
166: tao->step=1.0;
167: gpcg->f = f;
169: /* Check Stopping Condition */
170: ierr=TaoMonitor(tao,tao->niter,f,gpcg->gnorm,0.0,tao->step,&reason);
172: while (reason == TAO_CONTINUE_ITERATING){
173: tao->ksp_its=0;
175: GPCGGradProjections(tao);
176: ISGetSize(gpcg->Free_Local,&gpcg->n_free);
178: f=gpcg->f; gnorm=gpcg->gnorm;
180: KSPReset(tao->ksp);
182: if (gpcg->n_free > 0){
183: /* Create a reduced linear system */
184: VecDestroy(&gpcg->R);
185: VecDestroy(&gpcg->DXFree);
186: TaoVecGetSubVec(tao->gradient,gpcg->Free_Local, tao->subset_type, 0.0, &gpcg->R);
187: VecScale(gpcg->R, -1.0);
188: TaoVecGetSubVec(tao->stepdirection,gpcg->Free_Local,tao->subset_type, 0.0, &gpcg->DXFree);
189: VecSet(gpcg->DXFree,0.0);
191: TaoMatGetSubMat(tao->hessian, gpcg->Free_Local, gpcg->Work, tao->subset_type, &gpcg->Hsub);
193: if (tao->hessian_pre == tao->hessian) {
194: MatDestroy(&gpcg->Hsub_pre);
195: PetscObjectReference((PetscObject)gpcg->Hsub);
196: gpcg->Hsub_pre = gpcg->Hsub;
197: } else {
198: TaoMatGetSubMat(tao->hessian, gpcg->Free_Local, gpcg->Work, tao->subset_type, &gpcg->Hsub_pre);
199: }
201: KSPReset(tao->ksp);
202: KSPSetOperators(tao->ksp,gpcg->Hsub,gpcg->Hsub_pre);
204: KSPSolve(tao->ksp,gpcg->R,gpcg->DXFree);
205: KSPGetIterationNumber(tao->ksp,&its);
206: tao->ksp_its+=its;
207: tao->ksp_tot_its+=its;
208: VecSet(tao->stepdirection,0.0);
209: VecISAXPY(tao->stepdirection,gpcg->Free_Local,1.0,gpcg->DXFree);
211: VecDot(tao->stepdirection,tao->gradient,&gdx);
212: TaoLineSearchSetInitialStepLength(tao->linesearch,1.0);
213: f_new=f;
214: TaoLineSearchApply(tao->linesearch,tao->solution,&f_new,tao->gradient,tao->stepdirection,&stepsize,&ls_status);
216: actred = f_new - f;
218: /* Evaluate the function and gradient at the new point */
219: VecBoundGradientProjection(tao->gradient,tao->solution,tao->XL,tao->XU, gpcg->PG);
220: VecNorm(gpcg->PG, NORM_2, &gnorm);
221: f=f_new;
222: ISDestroy(&gpcg->Free_Local);
223: VecWhichBetween(tao->XL,tao->solution,tao->XU,&gpcg->Free_Local);
224: } else {
225: actred = 0; gpcg->step=1.0;
226: /* if there were no free variables, no cg method */
227: }
229: tao->niter++;
230: TaoMonitor(tao,tao->niter,f,gnorm,0.0,gpcg->step,&reason);
231: gpcg->f=f;gpcg->gnorm=gnorm; gpcg->actred=actred;
232: if (reason!=TAO_CONTINUE_ITERATING) break;
233: } /* END MAIN LOOP */
235: return(0);
236: }
238: static PetscErrorCode GPCGGradProjections(Tao tao)
239: {
240: PetscErrorCode ierr;
241: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
242: PetscInt i;
243: PetscReal actred=-1.0,actred_max=0.0, gAg,gtg=gpcg->gnorm,alpha;
244: PetscReal f_new,gdx,stepsize;
245: Vec DX=tao->stepdirection,XL=tao->XL,XU=tao->XU,Work=gpcg->Work;
246: Vec X=tao->solution,G=tao->gradient;
247: TaoLineSearchConvergedReason lsflag=TAOLINESEARCH_CONTINUE_ITERATING;
249: /*
250: The free, active, and binding variables should be already identified
251: */
253: for (i=0;i<gpcg->maxgpits;i++){
254: if ( -actred <= (gpcg->pg_ftol)*actred_max) break;
255: VecBoundGradientProjection(G,X,XL,XU,DX);
256: VecScale(DX,-1.0);
257: VecDot(DX,G,&gdx);
259: MatMult(tao->hessian,DX,Work);
260: VecDot(DX,Work,&gAg);
262: gpcg->gp_iterates++;
263: gpcg->total_gp_its++;
265: gtg=-gdx;
266: if (PetscAbsReal(gAg) == 0.0) {
267: alpha = 1.0;
268: } else {
269: alpha = PetscAbsReal(gtg/gAg);
270: }
271: TaoLineSearchSetInitialStepLength(tao->linesearch,alpha);
272: f_new=gpcg->f;
273: TaoLineSearchApply(tao->linesearch,X,&f_new,G,DX,&stepsize,&lsflag);
275: /* Update the iterate */
276: actred = f_new - gpcg->f;
277: actred_max = PetscMax(actred_max,-(f_new - gpcg->f));
278: gpcg->f = f_new;
279: ISDestroy(&gpcg->Free_Local);
280: VecWhichBetween(XL,X,XU,&gpcg->Free_Local);
281: }
283: gpcg->gnorm=gtg;
284: return(0);
285: } /* End gradient projections */
287: static PetscErrorCode TaoComputeDual_GPCG(Tao tao, Vec DXL, Vec DXU)
288: {
289: TAO_GPCG *gpcg = (TAO_GPCG *)tao->data;
293: VecBoundGradientProjection(tao->gradient, tao->solution, tao->XL, tao->XU, gpcg->Work);
294: VecCopy(gpcg->Work, DXL);
295: VecAXPY(DXL,-1.0,tao->gradient);
296: VecSet(DXU,0.0);
297: VecPointwiseMax(DXL,DXL,DXU);
299: VecCopy(tao->gradient,DXU);
300: VecAXPY(DXU,-1.0,gpcg->Work);
301: VecSet(gpcg->Work,0.0);
302: VecPointwiseMin(DXU,gpcg->Work,DXU);
303: return(0);
304: }
306: /*------------------------------------------------------------*/
307: /*MC
308: TAOGPCG - gradient projected conjugate gradient algorithm is an active-set
309: conjugate-gradient based method for bound-constrained minimization
311: Options Database Keys:
312: + -tao_gpcg_maxpgits - maximum number of gradient projections for GPCG iterate
313: - -tao_subset_type - "subvec","mask","matrix-free", strategies for handling active-sets
315: Level: beginner
316: M*/
317: PETSC_EXTERN PetscErrorCode TaoCreate_GPCG(Tao tao)
318: {
319: TAO_GPCG *gpcg;
323: tao->ops->setup = TaoSetup_GPCG;
324: tao->ops->solve = TaoSolve_GPCG;
325: tao->ops->view = TaoView_GPCG;
326: tao->ops->setfromoptions = TaoSetFromOptions_GPCG;
327: tao->ops->destroy = TaoDestroy_GPCG;
328: tao->ops->computedual = TaoComputeDual_GPCG;
330: PetscNewLog(tao,&gpcg);
331: tao->data = (void*)gpcg;
333: /* Override default settings (unless already changed) */
334: if (!tao->max_it_changed) tao->max_it=500;
335: if (!tao->max_funcs_changed) tao->max_funcs = 100000;
336: #if defined(PETSC_USE_REAL_SINGLE)
337: if (!tao->gatol_changed) tao->gatol=1e-6;
338: if (!tao->grtol_changed) tao->grtol=1e-6;
339: #else
340: if (!tao->gatol_changed) tao->gatol=1e-12;
341: if (!tao->grtol_changed) tao->grtol=1e-12;
342: #endif
344: /* Initialize pointers and variables */
345: gpcg->n=0;
346: gpcg->maxgpits = 8;
347: gpcg->pg_ftol = 0.1;
349: gpcg->gp_iterates=0; /* Cumulative number */
350: gpcg->total_gp_its = 0;
352: /* Initialize pointers and variables */
353: gpcg->n_bind=0;
354: gpcg->n_free = 0;
355: gpcg->n_upper=0;
356: gpcg->n_lower=0;
357: gpcg->subset_type = TAO_SUBSET_MASK;
358: gpcg->Hsub=NULL;
359: gpcg->Hsub_pre=NULL;
361: KSPCreate(((PetscObject)tao)->comm, &tao->ksp);
362: KSPSetOptionsPrefix(tao->ksp, tao->hdr.prefix);
363: KSPSetType(tao->ksp,KSPCGNASH);
365: TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);
366: TaoLineSearchSetType(tao->linesearch, TAOLINESEARCHGPCG);
367: TaoLineSearchSetObjectiveAndGradientRoutine(tao->linesearch, GPCGObjectiveAndGradient, tao);
368: TaoLineSearchSetOptionsPrefix(tao->linesearch,tao->hdr.prefix);
369: return(0);
370: }