Actual source code: brgn.c

petsc-master 2020-08-09
Report Typos and Errors
  1:  #include <../src/tao/leastsquares/impls/brgn/brgn.h>

  3: #define BRGN_REGULARIZATION_USER    0
  4: #define BRGN_REGULARIZATION_L2PROX  1
  5: #define BRGN_REGULARIZATION_L2PURE  2
  6: #define BRGN_REGULARIZATION_L1DICT  3
  7: #define BRGN_REGULARIZATION_LM      4
  8: #define BRGN_REGULARIZATION_TYPES   5

 10: static const char *BRGN_REGULARIZATION_TABLE[64] = {"user","l2prox","l2pure","l1dict","lm"};

 12: static PetscErrorCode GNHessianProd(Mat H,Vec in,Vec out)
 13: {
 14:   TAO_BRGN              *gn;
 15:   PetscErrorCode        ierr;
 16:   
 18:   MatShellGetContext(H,&gn);
 19:   MatMult(gn->subsolver->ls_jac,in,gn->r_work);
 20:   MatMultTranspose(gn->subsolver->ls_jac,gn->r_work,out);
 21:   switch (gn->reg_type) {
 22:   case BRGN_REGULARIZATION_USER:
 23:     MatMult(gn->Hreg,in,gn->x_work);
 24:     VecAXPY(out,gn->lambda,gn->x_work);
 25:     break;
 26:   case BRGN_REGULARIZATION_L2PURE:
 27:     VecAXPY(out,gn->lambda,in);
 28:     break;
 29:   case BRGN_REGULARIZATION_L2PROX:
 30:     VecAXPY(out,gn->lambda,in);
 31:     break;
 32:   case BRGN_REGULARIZATION_L1DICT:
 33:     /* out = out + lambda*D'*(diag.*(D*in)) */
 34:     if (gn->D) {
 35:       MatMult(gn->D,in,gn->y);/* y = D*in */
 36:     } else {
 37:       VecCopy(in,gn->y);
 38:     }
 39:     VecPointwiseMult(gn->y_work,gn->diag,gn->y);   /* y_work = diag.*(D*in), where diag = epsilon^2 ./ sqrt(x.^2+epsilon^2).^3 */
 40:     if (gn->D) {
 41:       MatMultTranspose(gn->D,gn->y_work,gn->x_work); /* x_work = D'*(diag.*(D*in)) */
 42:     } else {
 43:       VecCopy(gn->y_work,gn->x_work);
 44:     }
 45:     VecAXPY(out,gn->lambda,gn->x_work);
 46:     break;
 47:   case BRGN_REGULARIZATION_LM:
 48:     VecPointwiseMult(gn->x_work,gn->damping,in);
 49:     VecAXPY(out,1,gn->x_work);
 50:     break;
 51:   }
 52:   return(0);
 53: }
 54: static PetscErrorCode ComputeDamping(TAO_BRGN *gn)
 55: {
 56:   const PetscScalar *diag_ary;
 57:   PetscScalar       *damping_ary;
 58:   PetscInt          i,n;
 59:   PetscErrorCode    ierr;

 62:   /* update damping */
 63:   VecGetArray(gn->damping,&damping_ary);
 64:   VecGetArrayRead(gn->diag,&diag_ary);
 65:   VecGetLocalSize(gn->damping,&n);
 66:   for (i=0; i<n; i++) {
 67:     damping_ary[i] = PetscClipInterval(diag_ary[i],PETSC_SQRT_MACHINE_EPSILON,PetscSqrtReal(PETSC_MAX_REAL));
 68:   }
 69:   VecScale(gn->damping,gn->lambda);
 70:   VecRestoreArray(gn->damping,&damping_ary);
 71:   VecRestoreArrayRead(gn->diag,&diag_ary);
 72:   return(0);
 73: }

 75: PetscErrorCode TaoBRGNGetDampingVector(Tao tao,Vec *d)
 76: {
 77:   TAO_BRGN *gn = (TAO_BRGN *)tao->data;

 80:   if (gn->reg_type != BRGN_REGULARIZATION_LM) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_SUP,"Damping vector is only available if regularization type is lm.");
 81:   *d = gn->damping;
 82:   return(0);
 83: }

 85: static PetscErrorCode GNObjectiveGradientEval(Tao tao,Vec X,PetscReal *fcn,Vec G,void *ptr)
 86: {
 87:   TAO_BRGN              *gn = (TAO_BRGN *)ptr;
 88:   PetscInt              K;                    /* dimension of D*X */
 89:   PetscScalar           yESum;
 90:   PetscErrorCode        ierr;
 91:   PetscReal             f_reg;
 92:   
 94:   /* compute objective *fcn*/
 95:   /* compute first term 0.5*||ls_res||_2^2 */
 96:   TaoComputeResidual(tao,X,tao->ls_res);
 97:   VecDot(tao->ls_res,tao->ls_res,fcn);
 98:   *fcn *= 0.5;
 99:   /* compute gradient G */
100:   TaoComputeResidualJacobian(tao,X,tao->ls_jac,tao->ls_jac_pre);
101:   MatMultTranspose(tao->ls_jac,tao->ls_res,G);
102:   /* add the regularization contribution */
103:   switch (gn->reg_type) {
104:   case BRGN_REGULARIZATION_USER:
105:     (*gn->regularizerobjandgrad)(tao,X,&f_reg,gn->x_work,gn->reg_obj_ctx);
106:     *fcn += gn->lambda*f_reg;
107:     VecAXPY(G,gn->lambda,gn->x_work);
108:     break;
109:   case BRGN_REGULARIZATION_L2PURE:
110:     /* compute f = f + lambda*0.5*xk'*xk */
111:     VecDot(X,X,&f_reg);
112:     *fcn += gn->lambda*0.5*f_reg;
113:     /* compute G = G + lambda*xk */
114:     VecAXPY(G,gn->lambda,X);
115:     break;
116:   case BRGN_REGULARIZATION_L2PROX:
117:     /* compute f = f + lambda*0.5*(xk - xkm1)'*(xk - xkm1) */
118:     VecAXPBYPCZ(gn->x_work,1.0,-1.0,0.0,X,gn->x_old); 
119:     VecDot(gn->x_work,gn->x_work,&f_reg);
120:     *fcn += gn->lambda*0.5*f_reg;
121:     /* compute G = G + lambda*(xk - xkm1) */
122:     VecAXPBYPCZ(G,gn->lambda,-gn->lambda,1.0,X,gn->x_old);
123:     break;
124:   case BRGN_REGULARIZATION_L1DICT:
125:     /* compute f = f + lambda*sum(sqrt(y.^2+epsilon^2) - epsilon), where y = D*x*/
126:     if (gn->D) {
127:       MatMult(gn->D,X,gn->y);/* y = D*x */
128:     } else {
129:       VecCopy(X,gn->y);
130:     }
131:     VecPointwiseMult(gn->y_work,gn->y,gn->y);
132:     VecShift(gn->y_work,gn->epsilon*gn->epsilon);
133:     VecSqrtAbs(gn->y_work);  /* gn->y_work = sqrt(y.^2+epsilon^2) */ 
134:     VecSum(gn->y_work,&yESum);
135:     VecGetSize(gn->y,&K);
136:     *fcn += gn->lambda*(yESum - K*gn->epsilon);
137:     /* compute G = G + lambda*D'*(y./sqrt(y.^2+epsilon^2)),where y = D*x */  
138:     VecPointwiseDivide(gn->y_work,gn->y,gn->y_work); /* reuse y_work = y./sqrt(y.^2+epsilon^2) */
139:     if (gn->D) {
140:       MatMultTranspose(gn->D,gn->y_work,gn->x_work);
141:     } else {
142:       VecCopy(gn->y_work,gn->x_work);
143:     }
144:     VecAXPY(G,gn->lambda,gn->x_work);
145:     break;
146:   }
147:   return(0);
148: }

150: static PetscErrorCode GNComputeHessian(Tao tao,Vec X,Mat H,Mat Hpre,void *ptr)
151: {
152:   TAO_BRGN       *gn = (TAO_BRGN *)ptr;
153:   PetscInt       i,n,cstart,cend;
154:   PetscScalar    *cnorms,*diag_ary;

158:   TaoComputeResidualJacobian(tao,X,tao->ls_jac,tao->ls_jac_pre);

160:   switch (gn->reg_type) {
161:   case BRGN_REGULARIZATION_USER:
162:     (*gn->regularizerhessian)(tao,X,gn->Hreg,gn->reg_hess_ctx);
163:     break;
164:   case BRGN_REGULARIZATION_L2PURE:
165:     break;
166:   case BRGN_REGULARIZATION_L2PROX:
167:     break;
168:   case BRGN_REGULARIZATION_L1DICT:
169:     /* calculate and store diagonal matrix as a vector: diag = epsilon^2 ./ sqrt(x.^2+epsilon^2).^3* --> diag = epsilon^2 ./ sqrt(y.^2+epsilon^2).^3,where y = D*x */  
170:     if (gn->D) {
171:       MatMult(gn->D,X,gn->y);/* y = D*x */
172:     } else {
173:       VecCopy(X,gn->y);
174:     }
175:     VecPointwiseMult(gn->y_work,gn->y,gn->y);
176:     VecShift(gn->y_work,gn->epsilon*gn->epsilon);
177:     VecCopy(gn->y_work,gn->diag);                  /* gn->diag = y.^2+epsilon^2 */
178:     VecSqrtAbs(gn->y_work);                        /* gn->y_work = sqrt(y.^2+epsilon^2) */ 
179:     VecPointwiseMult(gn->diag,gn->y_work,gn->diag);/* gn->diag = sqrt(y.^2+epsilon^2).^3 */
180:     VecReciprocal(gn->diag);
181:     VecScale(gn->diag,gn->epsilon*gn->epsilon);
182:     break;
183:   case BRGN_REGULARIZATION_LM:
184:     /* compute diagonal of J^T J */
185:     MatGetSize(gn->parent->ls_jac,NULL,&n);
186:     PetscMalloc1(n,&cnorms);
187:     MatGetColumnNorms(gn->parent->ls_jac,NORM_2,cnorms);
188:     MatGetOwnershipRangeColumn(gn->parent->ls_jac,&cstart,&cend);
189:     VecGetArray(gn->diag,&diag_ary);
190:     for (i = 0; i < cend-cstart; i++) {
191:       diag_ary[i] = cnorms[cstart+i] * cnorms[cstart+i];
192:     }
193:     VecRestoreArray(gn->diag,&diag_ary);
194:     PetscFree(cnorms);
195:     ComputeDamping(gn);
196:     break;
197:   }
198:   return(0);
199: }

201: static PetscErrorCode GNHookFunction(Tao tao,PetscInt iter, void *ctx)
202: {
203:   TAO_BRGN              *gn = (TAO_BRGN *)ctx;
204:   PetscErrorCode        ierr;
205:   
207:   /* Update basic tao information from the subsolver */
208:   gn->parent->nfuncs = tao->nfuncs;
209:   gn->parent->ngrads = tao->ngrads;
210:   gn->parent->nfuncgrads = tao->nfuncgrads;
211:   gn->parent->nhess = tao->nhess;
212:   gn->parent->niter = tao->niter;
213:   gn->parent->ksp_its = tao->ksp_its;
214:   gn->parent->ksp_tot_its = tao->ksp_tot_its;
215:   gn->parent->fc = tao->fc;
216:   TaoGetConvergedReason(tao,&gn->parent->reason);
217:   /* Update the solution vectors */
218:   if (iter == 0) {
219:     VecSet(gn->x_old,0.0);
220:   } else {
221:     VecCopy(tao->solution,gn->x_old);
222:     VecCopy(tao->solution,gn->parent->solution);
223:   }
224:   /* Update the gradient */
225:   VecCopy(tao->gradient,gn->parent->gradient);

227:   /* Update damping parameter for LM */
228:   if (gn->reg_type == BRGN_REGULARIZATION_LM) {
229:     if (iter > 0) {
230:       if (gn->fc_old > tao->fc) {
231:         gn->lambda = gn->lambda * gn->downhill_lambda_change;
232:       } else {
233:         /* uphill step */
234:         gn->lambda = gn->lambda * gn->uphill_lambda_change;
235:       }
236:     }
237:     gn->fc_old = tao->fc;
238:   }

240:   /* Call general purpose update function */
241:   if (gn->parent->ops->update) {
242:     (*gn->parent->ops->update)(gn->parent,gn->parent->niter,gn->parent->user_update);
243:   }
244:   return(0);
245: }

247: static PetscErrorCode TaoSolve_BRGN(Tao tao)
248: {
249:   TAO_BRGN              *gn = (TAO_BRGN *)tao->data;
250:   PetscErrorCode        ierr;

253:   TaoSolve(gn->subsolver);
254:   /* Update basic tao information from the subsolver */
255:   tao->nfuncs = gn->subsolver->nfuncs;
256:   tao->ngrads = gn->subsolver->ngrads;
257:   tao->nfuncgrads = gn->subsolver->nfuncgrads;
258:   tao->nhess = gn->subsolver->nhess;
259:   tao->niter = gn->subsolver->niter;
260:   tao->ksp_its = gn->subsolver->ksp_its;
261:   tao->ksp_tot_its = gn->subsolver->ksp_tot_its;
262:   TaoGetConvergedReason(gn->subsolver,&tao->reason);
263:   /* Update vectors */
264:   VecCopy(gn->subsolver->solution,tao->solution);
265:   VecCopy(gn->subsolver->gradient,tao->gradient);
266:   return(0);
267: }

269: static PetscErrorCode TaoSetFromOptions_BRGN(PetscOptionItems *PetscOptionsObject,Tao tao)
270: {
271:   TAO_BRGN              *gn = (TAO_BRGN *)tao->data;
272:   TaoLineSearch         ls;
273:   PetscErrorCode        ierr;

276:   PetscOptionsHead(PetscOptionsObject,"least-squares problems with regularizer: ||f(x)||^2 + lambda*g(x), g(x) = ||xk-xkm1||^2 or ||Dx||_1 or user defined function.");
277:   PetscOptionsReal("-tao_brgn_regularizer_weight","regularizer weight (default 1e-4)","",gn->lambda,&gn->lambda,NULL);
278:   PetscOptionsReal("-tao_brgn_l1_smooth_epsilon","L1-norm smooth approximation parameter: ||x||_1 = sum(sqrt(x.^2+epsilon^2)-epsilon) (default 1e-6)","",gn->epsilon,&gn->epsilon,NULL);
279:   PetscOptionsReal("-tao_brgn_lm_downhill_lambda_change","Factor to decrease trust region by on downhill steps","",gn->downhill_lambda_change,&gn->downhill_lambda_change,NULL);
280:   PetscOptionsReal("-tao_brgn_lm_uphill_lambda_change","Factor to increase trust region by on uphill steps","",gn->uphill_lambda_change,&gn->uphill_lambda_change,NULL);
281:   PetscOptionsEList("-tao_brgn_regularization_type","regularization type", "",BRGN_REGULARIZATION_TABLE,BRGN_REGULARIZATION_TYPES,BRGN_REGULARIZATION_TABLE[gn->reg_type],&gn->reg_type,NULL);
282:   PetscOptionsTail();
283:   /* set unit line search direction as the default when using the lm regularizer */
284:   if (gn->reg_type == BRGN_REGULARIZATION_LM) {
285:     TaoGetLineSearch(gn->subsolver,&ls);
286:     TaoLineSearchSetType(ls,TAOLINESEARCHUNIT);
287:   }
288:   TaoSetFromOptions(gn->subsolver);
289:   return(0);
290: }

292: static PetscErrorCode TaoView_BRGN(Tao tao,PetscViewer viewer)
293: {
294:   TAO_BRGN              *gn = (TAO_BRGN *)tao->data;
295:   PetscErrorCode        ierr;

298:   PetscViewerASCIIPushTab(viewer);
299:   TaoView(gn->subsolver,viewer);
300:   PetscViewerASCIIPopTab(viewer);
301:   return(0);
302: }

304: static PetscErrorCode TaoSetUp_BRGN(Tao tao)
305: {
306:   TAO_BRGN              *gn = (TAO_BRGN *)tao->data;
307:   PetscErrorCode        ierr;
308:   PetscBool             is_bnls,is_bntr,is_bntl;
309:   PetscInt              i,n,N,K; /* dict has size K*N*/

312:   if (!tao->ls_res) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_ORDER,"TaoSetResidualRoutine() must be called before setup!");
313:   PetscObjectTypeCompare((PetscObject)gn->subsolver,TAOBNLS,&is_bnls);
314:   PetscObjectTypeCompare((PetscObject)gn->subsolver,TAOBNTR,&is_bntr);
315:   PetscObjectTypeCompare((PetscObject)gn->subsolver,TAOBNTL,&is_bntl);
316:   if ((is_bnls || is_bntr || is_bntl) && !tao->ls_jac) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_ORDER,"TaoSetResidualJacobianRoutine() must be called before setup!");
317:   if (!tao->gradient) {
318:     VecDuplicate(tao->solution,&tao->gradient);
319:   }
320:   if (!gn->x_work) {
321:     VecDuplicate(tao->solution,&gn->x_work);
322:   }
323:   if (!gn->r_work) {
324:     VecDuplicate(tao->ls_res,&gn->r_work);
325:   }
326:   if (!gn->x_old) {
327:     VecDuplicate(tao->solution,&gn->x_old);
328:     VecSet(gn->x_old,0.0);
329:   }
330:     
331:   if (BRGN_REGULARIZATION_L1DICT == gn->reg_type) {
332:     if (gn->D) {
333:       MatGetSize(gn->D,&K,&N); /* Shell matrices still must have sizes defined. K = N for identity matrix, K=N-1 or N for gradient matrix */
334:     } else {
335:       VecGetSize(tao->solution,&K); /* If user does not setup dict matrix, use identiy matrix, K=N */
336:     }
337:     if (!gn->y) {    
338:       VecCreate(PETSC_COMM_SELF,&gn->y);
339:       VecSetSizes(gn->y,PETSC_DECIDE,K);
340:       VecSetFromOptions(gn->y);
341:       VecSet(gn->y,0.0);

343:     }
344:     if (!gn->y_work) {
345:       VecDuplicate(gn->y,&gn->y_work);
346:     }
347:     if (!gn->diag) {
348:       VecDuplicate(gn->y,&gn->diag);
349:       VecSet(gn->diag,0.0);
350:     }
351:   }
352:   if (BRGN_REGULARIZATION_LM == gn->reg_type) {
353:     if (!gn->diag) {
354:       MatCreateVecs(gn->parent->ls_jac,&gn->diag,NULL);
355:     }
356:     if (!gn->damping) {
357:       MatCreateVecs(gn->parent->ls_jac,&gn->damping,NULL);
358:     }
359:   }

361:   if (!tao->setupcalled) {
362:     /* Hessian setup */
363:     VecGetLocalSize(tao->solution,&n);
364:     VecGetSize(tao->solution,&N);
365:     MatSetSizes(gn->H,n,n,N,N);
366:     MatSetType(gn->H,MATSHELL);
367:     MatSetUp(gn->H);
368:     MatShellSetOperation(gn->H,MATOP_MULT,(void (*)(void))GNHessianProd);
369:     MatShellSetContext(gn->H,(void*)gn);
370:     /* Subsolver setup,include initial vector and dicttionary D */
371:     TaoSetUpdate(gn->subsolver,GNHookFunction,(void*)gn);
372:     TaoSetInitialVector(gn->subsolver,tao->solution);
373:     if (tao->bounded) {
374:       TaoSetVariableBounds(gn->subsolver,tao->XL,tao->XU);
375:     }
376:     TaoSetResidualRoutine(gn->subsolver,tao->ls_res,tao->ops->computeresidual,tao->user_lsresP);
377:     TaoSetJacobianResidualRoutine(gn->subsolver,tao->ls_jac,tao->ls_jac,tao->ops->computeresidualjacobian,tao->user_lsjacP);
378:     TaoSetObjectiveAndGradientRoutine(gn->subsolver,GNObjectiveGradientEval,(void*)gn);
379:     TaoSetHessianRoutine(gn->subsolver,gn->H,gn->H,GNComputeHessian,(void*)gn);
380:     /* Propagate some options down */
381:     TaoSetTolerances(gn->subsolver,tao->gatol,tao->grtol,tao->gttol);
382:     TaoSetMaximumIterations(gn->subsolver,tao->max_it);
383:     TaoSetMaximumFunctionEvaluations(gn->subsolver,tao->max_funcs);
384:     for (i=0; i<tao->numbermonitors; ++i) {
385:       TaoSetMonitor(gn->subsolver,tao->monitor[i],tao->monitorcontext[i],tao->monitordestroy[i]);
386:       PetscObjectReference((PetscObject)(tao->monitorcontext[i]));
387:     }
388:     TaoSetUp(gn->subsolver);
389:   }
390:   return(0);
391: }

393: static PetscErrorCode TaoDestroy_BRGN(Tao tao)
394: {
395:   TAO_BRGN              *gn = (TAO_BRGN *)tao->data;
396:   PetscErrorCode        ierr;

399:   if (tao->setupcalled) {
400:     VecDestroy(&tao->gradient);
401:     VecDestroy(&gn->x_work);
402:     VecDestroy(&gn->r_work);
403:     VecDestroy(&gn->x_old);
404:     VecDestroy(&gn->diag);
405:     VecDestroy(&gn->y);
406:     VecDestroy(&gn->y_work);
407:   }
408:   VecDestroy(&gn->damping);
409:   VecDestroy(&gn->diag);
410:   MatDestroy(&gn->H);
411:   MatDestroy(&gn->D);
412:   MatDestroy(&gn->Hreg);
413:   TaoDestroy(&gn->subsolver);
414:   gn->parent = NULL;
415:   PetscFree(tao->data);
416:   return(0);
417: }

419: /*MC
420:   TAOBRGN - Bounded Regularized Gauss-Newton method for solving nonlinear least-squares 
421:             problems with bound constraints. This algorithm is a thin wrapper around TAOBNTL 
422:             that constructs the Gauss-Newton problem with the user-provided least-squares 
423:             residual and Jacobian. The algorithm offers an L2-norm ("l2pure"), L2-norm proximal point ("l2prox") 
424:             regularizer, and L1-norm dictionary regularizer ("l1dict"), where we approximate the 
425:             L1-norm ||x||_1 by sum_i(sqrt(x_i^2+epsilon^2)-epsilon) with a small positive number epsilon.
426:             Also offered is the "lm" regularizer which uses a scaled diagonal of J^T J.
427:             With the "lm" regularizer, BRGN is a Levenberg-Marquardt optimizer.
428:             The user can also provide own regularization function.

430:   Options Database Keys:
431: + -tao_brgn_regularization_type - regularization type ("user", "l2prox", "l2pure", "l1dict", "lm") (default "l2prox")
432: . -tao_brgn_regularizer_weight  - regularizer weight (default 1e-4)
433: - -tao_brgn_l1_smooth_epsilon   - L1-norm smooth approximation parameter: ||x||_1 = sum(sqrt(x.^2+epsilon^2)-epsilon) (default 1e-6)

435:   Level: beginner
436: M*/
437: PETSC_EXTERN PetscErrorCode TaoCreate_BRGN(Tao tao)
438: {
439:   TAO_BRGN       *gn;
441:   
443:   PetscNewLog(tao,&gn);
444:   
445:   tao->ops->destroy = TaoDestroy_BRGN;
446:   tao->ops->setup = TaoSetUp_BRGN;
447:   tao->ops->setfromoptions = TaoSetFromOptions_BRGN;
448:   tao->ops->view = TaoView_BRGN;
449:   tao->ops->solve = TaoSolve_BRGN;
450:   
451:   tao->data = (void*)gn;
452:   gn->reg_type = BRGN_REGULARIZATION_L2PROX;
453:   gn->lambda = 1e-4;
454:   gn->epsilon = 1e-6;
455:   gn->downhill_lambda_change = 1./5.;
456:   gn->uphill_lambda_change = 1.5;
457:   gn->parent = tao;
458:   
459:   MatCreate(PetscObjectComm((PetscObject)tao),&gn->H);
460:   MatSetOptionsPrefix(gn->H,"tao_brgn_hessian_");
461:   
462:   TaoCreate(PetscObjectComm((PetscObject)tao),&gn->subsolver);
463:   TaoSetType(gn->subsolver,TAOBNLS);
464:   TaoSetOptionsPrefix(gn->subsolver,"tao_brgn_subsolver_");
465:   return(0);
466: }

468: /*@
469:   TaoBRGNGetSubsolver - Get the pointer to the subsolver inside BRGN

471:   Collective on Tao

473:   Level: advanced
474:   
475:   Input Parameters:
476: +  tao - the Tao solver context
477: -  subsolver - the Tao sub-solver context
478: @*/
479: PetscErrorCode TaoBRGNGetSubsolver(Tao tao,Tao *subsolver)
480: {
481:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;
482:   
484:   *subsolver = gn->subsolver;
485:   return(0);
486: }

488: /*@
489:   TaoBRGNSetRegularizerWeight - Set the regularizer weight for the Gauss-Newton least-squares algorithm

491:   Collective on Tao
492:   
493:   Input Parameters:
494: +  tao - the Tao solver context
495: -  lambda - L1-norm regularizer weight

497:   Level: beginner
498: @*/
499: PetscErrorCode TaoBRGNSetRegularizerWeight(Tao tao,PetscReal lambda)
500: {
501:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;
502:   
503:   /* Initialize lambda here */

506:   gn->lambda = lambda;
507:   return(0);
508: }

510: /*@
511:   TaoBRGNSetL1SmoothEpsilon - Set the L1-norm smooth approximation parameter for L1-regularized least-squares algorithm

513:   Collective on Tao
514:   
515:   Input Parameters:
516: +  tao - the Tao solver context
517: -  epsilon - L1-norm smooth approximation parameter

519:   Level: advanced
520: @*/
521: PetscErrorCode TaoBRGNSetL1SmoothEpsilon(Tao tao,PetscReal epsilon)
522: {
523:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;
524:   
525:   /* Initialize epsilon here */

528:   gn->epsilon = epsilon;
529:   return(0);
530: }

532: /*@
533:    TaoBRGNSetDictionaryMatrix - bind the dictionary matrix from user Section 1.5 Writing Application Codes with PETSc context to gn->D, for compressed sensing (with least-squares problem)

535:    Input Parameters:
536: +  tao  - the Tao context
537: -  dict - the user specified dictionary matrix.  We allow to set a null dictionary, which means identity matrix by default

539:     Level: advanced
540: @*/
541: PetscErrorCode TaoBRGNSetDictionaryMatrix(Tao tao,Mat dict)  
542: {
543:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;
547:   if (dict) {
550:     PetscObjectReference((PetscObject)dict);
551:   }
552:   MatDestroy(&gn->D);
553:   gn->D = dict;
554:   return(0);
555: }

557: /*@C
558:    TaoBRGNSetRegularizerObjectiveAndGradientRoutine - Sets the user-defined regularizer call-back 
559:    function into the algorithm.

561:    Input Parameters:
562:    + tao - the Tao context
563:    . func - function pointer for the regularizer value and gradient evaluation
564:    - ctx - user context for the regularizer

566:    Level: advanced
567: @*/
568: PetscErrorCode TaoBRGNSetRegularizerObjectiveAndGradientRoutine(Tao tao,PetscErrorCode (*func)(Tao,Vec,PetscReal *,Vec,void*),void *ctx)
569: {
570:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;

574:   if (ctx) {
575:     gn->reg_obj_ctx = ctx;
576:   }
577:   if (func) {
578:     gn->regularizerobjandgrad = func;
579:   }
580:   return(0);
581: }

583: /*@C
584:    TaoBRGNSetRegularizerHessianRoutine - Sets the user-defined regularizer call-back 
585:    function into the algorithm.

587:    Input Parameters:
588:    + tao - the Tao context
589:    . Hreg - user-created matrix for the Hessian of the regularization term
590:    . func - function pointer for the regularizer Hessian evaluation
591:    - ctx - user context for the regularizer Hessian

593:    Level: advanced
594: @*/
595: PetscErrorCode TaoBRGNSetRegularizerHessianRoutine(Tao tao,Mat Hreg,PetscErrorCode (*func)(Tao,Vec,Mat,void*),void *ctx)
596: {
597:   TAO_BRGN       *gn = (TAO_BRGN *)tao->data;

602:   if (Hreg) {
605:   } else SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_ARG_WRONG,"NULL Hessian detected! User must provide valid Hessian for the regularizer.");
606:   if (ctx) {
607:     gn->reg_hess_ctx = ctx;
608:   }
609:   if (func) {
610:     gn->regularizerhessian = func;
611:   }
612:   if (Hreg) {
613:     PetscObjectReference((PetscObject)Hreg);
614:     MatDestroy(&gn->Hreg);
615:     gn->Hreg = Hreg;
616:   }
617:   return(0);
618: }