Date: 2016-07-24T17:15:25+00:00

Actual source code: bmrm.c
petsc-3.7.3 2016-07-24
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      1: #include <../src/tao/unconstrained/impls/bmrm/bmrm.h>

      3: static PetscErrorCode init_df_solver(TAO_DF*);
      4: static PetscErrorCode ensure_df_space(PetscInt, TAO_DF*);
      5: static PetscErrorCode destroy_df_solver(TAO_DF*);
      6: static PetscReal phi(PetscReal*,PetscInt,PetscReal,PetscReal*,PetscReal,PetscReal*,PetscReal*,PetscReal*);
      7: static PetscInt project(PetscInt,PetscReal*,PetscReal,PetscReal*,PetscReal*,PetscReal*,PetscReal*,PetscReal*,TAO_DF*);
      8: static PetscErrorCode solve(TAO_DF*);

     11: /*------------------------------------------------------------*/
     12: /* The main solver function

     14:    f = Remp(W)          This is what the user provides us from the application layer
     15:    So the ComputeGradient function for instance should get us back the subgradient of Remp(W)

     17:    Regularizer assumed to be L2 norm = lambda*0.5*W'W ()
     18: */

     22: static PetscErrorCode make_grad_node(Vec X, Vec_Chain **p)
     23: {

     27:   PetscNew(p);
     28:   VecDuplicate(X, &(*p)->V);
     29:   VecCopy(X, (*p)->V);
     30:   (*p)->next = NULL;
     31:   return(0);
     32: }

     36: static PetscErrorCode destroy_grad_list(Vec_Chain *head)
     37: {
     39:   Vec_Chain      *p = head->next, *q;

     42:   while(p) {
     43:     q = p->next;
     44:     VecDestroy(&p->V);
     45:     PetscFree(p);
     46:     p = q;
     47:   }
     48:   head->next = NULL;
     49:   return(0);
     50: }

     55: static PetscErrorCode TaoSolve_BMRM(Tao tao)
     56: {
     57:   PetscErrorCode     ierr;
     58:   TaoConvergedReason reason;
     59:   TAO_DF             df;
     60:   TAO_BMRM           *bmrm = (TAO_BMRM*)tao->data;

     62:   /* Values and pointers to parts of the optimization problem */
     63:   PetscReal          f = 0.0;
     64:   Vec                W = tao->solution;
     65:   Vec                G = tao->gradient;
     66:   PetscReal          lambda;
     67:   PetscReal          bt;
     68:   Vec_Chain          grad_list, *tail_glist, *pgrad;
     69:   PetscInt           i;
     70:   PetscMPIInt        rank;

     72:   /* Used in converged criteria check */
     73:   PetscReal          reg;
     74:   PetscReal          jtwt, max_jtwt, pre_epsilon, epsilon, jw, min_jw;
     75:   PetscReal          innerSolverTol;
     76:   MPI_Comm           comm;

     79:   PetscObjectGetComm((PetscObject)tao,&comm);
     80:   MPI_Comm_rank(comm, &rank);
     81:   lambda = bmrm->lambda;

     83:   /* Check Stopping Condition */
     84:   tao->step = 1.0;
     85:   max_jtwt = -BMRM_INFTY;
     86:   min_jw = BMRM_INFTY;
     87:   innerSolverTol = 1.0;
     88:   epsilon = 0.0;

     90:   if (!rank) {
     91:     init_df_solver(&df);
     92:     grad_list.next = NULL;
     93:     tail_glist = &grad_list;
     94:   }

     96:   df.tol = 1e-6;
     97:   reason = TAO_CONTINUE_ITERATING;

     99:   /*-----------------Algorithm Begins------------------------*/
    100:   /* make the scatter */
    101:   VecScatterCreateToZero(W, &bmrm->scatter, &bmrm->local_w);
    102:   VecAssemblyBegin(bmrm->local_w);
    103:   VecAssemblyEnd(bmrm->local_w);

    105:   /* NOTE: In application pass the sub-gradient of Remp(W) */
    106:   TaoComputeObjectiveAndGradient(tao, W, &f, G);
    107:   TaoMonitor(tao,tao->niter,f,1.0,0.0,tao->step,&reason);
    108:   while (reason == TAO_CONTINUE_ITERATING) {
    109:     /* compute bt = Remp(Wt-1) - <Wt-1, At> */
    110:     VecDot(W, G, &bt);
    111:     bt = f - bt;

    113:     /* First gather the gradient to the master node */
    114:     VecScatterBegin(bmrm->scatter, G, bmrm->local_w, INSERT_VALUES, SCATTER_FORWARD);
    115:     VecScatterEnd(bmrm->scatter, G, bmrm->local_w, INSERT_VALUES, SCATTER_FORWARD);

    117:     /* Bring up the inner solver */
    118:     if (!rank) {
    119:       ensure_df_space(tao->niter+1, &df);
    120:       make_grad_node(bmrm->local_w, &pgrad);
    121:       tail_glist->next = pgrad;
    122:       tail_glist = pgrad;

    124:       df.a[tao->niter] = 1.0;
    125:       df.f[tao->niter] = -bt;
    126:       df.u[tao->niter] = 1.0;
    127:       df.l[tao->niter] = 0.0;

    129:       /* set up the Q */
    130:       pgrad = grad_list.next;
    131:       for (i=0; i<=tao->niter; i++) {
    132:         VecDot(pgrad->V, bmrm->local_w, &reg);
    133:         df.Q[i][tao->niter] = df.Q[tao->niter][i] = reg / lambda;
    134:         pgrad = pgrad->next;
    135:       }

    137:       if (tao->niter > 0) {
    138:         df.x[tao->niter] = 0.0;
    139:         solve(&df);
    140:       } else
    141:         df.x[0] = 1.0;

    143:       /* now computing Jt*(alpha_t) which should be = Jt(wt) to check convergence */
    144:       jtwt = 0.0;
    145:       VecSet(bmrm->local_w, 0.0);
    146:       pgrad = grad_list.next;
    147:       for (i=0; i<=tao->niter; i++) {
    148:         jtwt -= df.x[i] * df.f[i];
    149:         VecAXPY(bmrm->local_w, -df.x[i] / lambda, pgrad->V);
    150:         pgrad = pgrad->next;
    151:       }

    153:       VecNorm(bmrm->local_w, NORM_2, &reg);
    154:       reg = 0.5*lambda*reg*reg;
    155:       jtwt -= reg;
    156:     } /* end if rank == 0 */

    158:     /* scatter the new W to all nodes */
    159:     VecScatterBegin(bmrm->scatter,bmrm->local_w,W,INSERT_VALUES,SCATTER_REVERSE);
    160:     VecScatterEnd(bmrm->scatter,bmrm->local_w,W,INSERT_VALUES,SCATTER_REVERSE);

    162:     TaoComputeObjectiveAndGradient(tao, W, &f, G);

    164:     MPI_Bcast(&jtwt,1,MPIU_REAL,0,comm);
    165:     MPI_Bcast(&reg,1,MPIU_REAL,0,comm);

    167:     jw = reg + f;                                       /* J(w) = regularizer + Remp(w) */
    168:     if (jw < min_jw) min_jw = jw;
    169:     if (jtwt > max_jtwt) max_jtwt = jtwt;

    171:     pre_epsilon = epsilon;
    172:     epsilon = min_jw - jtwt;

    174:     if (!rank) {
    175:       if (innerSolverTol > epsilon) innerSolverTol = epsilon;
    176:       else if (innerSolverTol < 1e-7) innerSolverTol = 1e-7;

    178:       /* if the annealing doesn't work well, lower the inner solver tolerance */
    179:       if(pre_epsilon < epsilon) innerSolverTol *= 0.2;

    181:       df.tol = innerSolverTol*0.5;
    182:     }

    184:     tao->niter++;
    185:     TaoMonitor(tao,tao->niter,min_jw,epsilon,0.0,tao->step,&reason);
    186:   }

    188:   /* free all the memory */
    189:   if (!rank) {
    190:     destroy_grad_list(&grad_list);
    191:     destroy_df_solver(&df);
    192:   }

    194:   VecDestroy(&bmrm->local_w);
    195:   VecScatterDestroy(&bmrm->scatter);
    196:   return(0);
    197: }

    200: /* ---------------------------------------------------------- */

    204: static PetscErrorCode TaoSetup_BMRM(Tao tao)
    205: {

    210:   /* Allocate some arrays */
    211:   if (!tao->gradient) {
    212:     VecDuplicate(tao->solution, &tao->gradient);
    213:   }
    214:   return(0);
    215: }

    217: /*------------------------------------------------------------*/
    220: static PetscErrorCode TaoDestroy_BMRM(Tao tao)
    221: {

    225:   PetscFree(tao->data);
    226:   return(0);
    227: }

    231: static PetscErrorCode TaoSetFromOptions_BMRM(PetscOptionItems *PetscOptionsObject,Tao tao)
    232: {
    234:   TAO_BMRM*      bmrm = (TAO_BMRM*)tao->data;

    237:   PetscOptionsHead(PetscOptionsObject,"BMRM for regularized risk minimization");
    238:   PetscOptionsReal("-tao_bmrm_lambda", "regulariser weight","", 100,&bmrm->lambda,NULL);
    239:   PetscOptionsTail();
    240:   return(0);
    241: }

    243: /*------------------------------------------------------------*/
    246: static PetscErrorCode TaoView_BMRM(Tao tao, PetscViewer viewer)
    247: {
    248:   PetscBool      isascii;

    252:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
    253:   if (isascii) {
    254:     PetscViewerASCIIPushTab(viewer);
    255:     PetscViewerASCIIPopTab(viewer);
    256:   }
    257:   return(0);
    258: }

    260: /*------------------------------------------------------------*/
    261: /*MC
    262:   TAOBMRM - bundle method for regularized risk minimization

    264:   Options Database Keys:
    265: . - tao_bmrm_lambda - regulariser weight

    267:   Level: beginner
    268: M*/

    272: PETSC_EXTERN PetscErrorCode TaoCreate_BMRM(Tao tao)
    273: {
    274:   TAO_BMRM       *bmrm;

    278:   tao->ops->setup = TaoSetup_BMRM;
    279:   tao->ops->solve = TaoSolve_BMRM;
    280:   tao->ops->view  = TaoView_BMRM;
    281:   tao->ops->setfromoptions = TaoSetFromOptions_BMRM;
    282:   tao->ops->destroy = TaoDestroy_BMRM;

    284:   PetscNewLog(tao,&bmrm);
    285:   bmrm->lambda = 1.0;
    286:   tao->data = (void*)bmrm;

    288:   /* Override default settings (unless already changed) */
    289:   if (!tao->max_it_changed) tao->max_it = 2000;
    290:   if (!tao->max_funcs_changed) tao->max_funcs = 4000;
    291:   if (!tao->gatol_changed) tao->gatol = 1.0e-12;
    292:   if (!tao->grtol_changed) tao->grtol = 1.0e-12;

    294:   return(0);
    295: }

    299: PetscErrorCode init_df_solver(TAO_DF *df)
    300: {
    301:   PetscInt       i, n = INCRE_DIM;

    305:   /* default values */
    306:   df->maxProjIter = 200;
    307:   df->maxPGMIter = 300000;
    308:   df->b = 1.0;

    310:   /* memory space required by Dai-Fletcher */
    311:   df->cur_num_cp = n;
    312:   PetscMalloc1(n, &df->f);
    313:   PetscMalloc1(n, &df->a);
    314:   PetscMalloc1(n, &df->l);
    315:   PetscMalloc1(n, &df->u);
    316:   PetscMalloc1(n, &df->x);
    317:   PetscMalloc1(n, &df->Q);

    319:   for (i = 0; i < n; i ++) {
    320:     PetscMalloc1(n, &df->Q[i]);
    321:   }

    323:   PetscMalloc1(n, &df->g);
    324:   PetscMalloc1(n, &df->y);
    325:   PetscMalloc1(n, &df->tempv);
    326:   PetscMalloc1(n, &df->d);
    327:   PetscMalloc1(n, &df->Qd);
    328:   PetscMalloc1(n, &df->t);
    329:   PetscMalloc1(n, &df->xplus);
    330:   PetscMalloc1(n, &df->tplus);
    331:   PetscMalloc1(n, &df->sk);
    332:   PetscMalloc1(n, &df->yk);

    334:   PetscMalloc1(n, &df->ipt);
    335:   PetscMalloc1(n, &df->ipt2);
    336:   PetscMalloc1(n, &df->uv);
    337:   return(0);
    338: }

    342: PetscErrorCode ensure_df_space(PetscInt dim, TAO_DF *df)
    343: {
    345:   PetscReal      *tmp, **tmp_Q;
    346:   PetscInt       i, n, old_n;

    349:   df->dim = dim;
    350:   if (dim <= df->cur_num_cp) return(0);

    352:   old_n = df->cur_num_cp;
    353:   df->cur_num_cp += INCRE_DIM;
    354:   n = df->cur_num_cp;

    356:   /* memory space required by dai-fletcher */
    357:   PetscMalloc1(n, &tmp);
    358:   PetscMemcpy(tmp, df->f, sizeof(PetscReal)*old_n);
    359:   PetscFree(df->f);
    360:   df->f = tmp;

    362:   PetscMalloc1(n, &tmp);
    363:   PetscMemcpy(tmp, df->a, sizeof(PetscReal)*old_n);
    364:   PetscFree(df->a);
    365:   df->a = tmp;

    367:   PetscMalloc1(n, &tmp);
    368:   PetscMemcpy(tmp, df->l, sizeof(PetscReal)*old_n);
    369:   PetscFree(df->l);
    370:   df->l = tmp;

    372:   PetscMalloc1(n, &tmp);
    373:   PetscMemcpy(tmp, df->u, sizeof(PetscReal)*old_n);
    374:   PetscFree(df->u);
    375:   df->u = tmp;

    377:   PetscMalloc1(n, &tmp);
    378:   PetscMemcpy(tmp, df->x, sizeof(PetscReal)*old_n);
    379:   PetscFree(df->x);
    380:   df->x = tmp;

    382:   PetscMalloc1(n, &tmp_Q);
    383:   for (i = 0; i < n; i ++) {
    384:     PetscMalloc1(n, &tmp_Q[i]);
    385:     if (i < old_n) {
    386:       PetscMemcpy(tmp_Q[i], df->Q[i], sizeof(PetscReal)*old_n);
    387:       PetscFree(df->Q[i]);
    388:     }
    389:   }

    391:   PetscFree(df->Q);
    392:   df->Q = tmp_Q;

    394:   PetscFree(df->g);
    395:   PetscMalloc1(n, &df->g);

    397:   PetscFree(df->y);
    398:   PetscMalloc1(n, &df->y);

    400:   PetscFree(df->tempv);
    401:   PetscMalloc1(n, &df->tempv);

    403:   PetscFree(df->d);
    404:   PetscMalloc1(n, &df->d);

    406:   PetscFree(df->Qd);
    407:   PetscMalloc1(n, &df->Qd);

    409:   PetscFree(df->t);
    410:   PetscMalloc1(n, &df->t);

    412:   PetscFree(df->xplus);
    413:   PetscMalloc1(n, &df->xplus);

    415:   PetscFree(df->tplus);
    416:   PetscMalloc1(n, &df->tplus);

    418:   PetscFree(df->sk);
    419:   PetscMalloc1(n, &df->sk);

    421:   PetscFree(df->yk);
    422:   PetscMalloc1(n, &df->yk);

    424:   PetscFree(df->ipt);
    425:   PetscMalloc1(n, &df->ipt);

    427:   PetscFree(df->ipt2);
    428:   PetscMalloc1(n, &df->ipt2);

    430:   PetscFree(df->uv);
    431:   PetscMalloc1(n, &df->uv);
    432:   return(0);
    433: }

    437: PetscErrorCode destroy_df_solver(TAO_DF *df)
    438: {
    440:   PetscInt       i;

    443:   PetscFree(df->f);
    444:   PetscFree(df->a);
    445:   PetscFree(df->l);
    446:   PetscFree(df->u);
    447:   PetscFree(df->x);

    449:   for (i = 0; i < df->cur_num_cp; i ++) {
    450:     PetscFree(df->Q[i]);
    451:   }
    452:   PetscFree(df->Q);
    453:   PetscFree(df->ipt);
    454:   PetscFree(df->ipt2);
    455:   PetscFree(df->uv);
    456:   PetscFree(df->g);
    457:   PetscFree(df->y);
    458:   PetscFree(df->tempv);
    459:   PetscFree(df->d);
    460:   PetscFree(df->Qd);
    461:   PetscFree(df->t);
    462:   PetscFree(df->xplus);
    463:   PetscFree(df->tplus);
    464:   PetscFree(df->sk);
    465:   PetscFree(df->yk);
    466:   return(0);
    467: }

    469: /* Piecewise linear monotone target function for the Dai-Fletcher projector */
    472: PetscReal phi(PetscReal *x,PetscInt n,PetscReal lambda,PetscReal *a,PetscReal b,PetscReal *c,PetscReal *l,PetscReal *u)
    473: {
    474:   PetscReal r = 0.0;
    475:   PetscInt  i;

    477:   for (i = 0; i < n; i++){
    478:     x[i] = -c[i] + lambda*a[i];
    479:     if (x[i] > u[i])     x[i] = u[i];
    480:     else if(x[i] < l[i]) x[i] = l[i];
    481:     r += a[i]*x[i];
    482:   }
    483:   return r - b;
    484: }

    486: /** Modified Dai-Fletcher QP projector solves the problem:
    487:  *
    488:  *      minimise  0.5*x'*x - c'*x
    489:  *      subj to   a'*x = b
    490:  *                l \leq x \leq u
    491:  *
    492:  *  \param c The point to be projected onto feasible set
    493:  */
    496: PetscInt project(PetscInt n,PetscReal *a,PetscReal b,PetscReal *c,PetscReal *l,PetscReal *u,PetscReal *x,PetscReal *lam_ext,TAO_DF *df)
    497: {
    498:   PetscReal      lambda, lambdal, lambdau, dlambda, lambda_new;
    499:   PetscReal      r, rl, ru, s;
    500:   PetscInt       innerIter;
    501:   PetscBool      nonNegativeSlack = PETSC_FALSE;

    504:   *lam_ext = 0;
    505:   lambda  = 0;
    506:   dlambda = 0.5;
    507:   innerIter = 1;

    509:   /*  \phi(x;lambda) := 0.5*x'*x + c'*x - lambda*(a'*x-b)
    510:    *
    511:    *  Optimality conditions for \phi:
    512:    *
    513:    *  1. lambda   <= 0
    514:    *  2. r        <= 0
    515:    *  3. r*lambda == 0
    516:    */

    518:   /* Bracketing Phase */
    519:   r = phi(x, n, lambda, a, b, c, l, u);

    521:   if(nonNegativeSlack) {
    522:     /* inequality constraint, i.e., with \xi >= 0 constraint */
    523:     if (r < TOL_R) return 0;
    524:   } else  {
    525:     /* equality constraint ,i.e., without \xi >= 0 constraint */
    526:     if (fabs(r) < TOL_R) return 0;
    527:   }

    529:   if (r < 0.0){
    530:     lambdal = lambda;
    531:     rl      = r;
    532:     lambda  = lambda + dlambda;
    533:     r       = phi(x, n, lambda, a, b, c, l, u);
    534:     while (r < 0.0 && dlambda < BMRM_INFTY)  {
    535:       lambdal = lambda;
    536:       s       = rl/r - 1.0;
    537:       if (s < 0.1) s = 0.1;
    538:       dlambda = dlambda + dlambda/s;
    539:       lambda  = lambda + dlambda;
    540:       rl      = r;
    541:       r       = phi(x, n, lambda, a, b, c, l, u);
    542:     }
    543:     lambdau = lambda;
    544:     ru      = r;
    545:   } else {
    546:     lambdau = lambda;
    547:     ru      = r;
    548:     lambda  = lambda - dlambda;
    549:     r       = phi(x, n, lambda, a, b, c, l, u);
    550:     while (r > 0.0 && dlambda > -BMRM_INFTY) {
    551:       lambdau = lambda;
    552:       s       = ru/r - 1.0;
    553:       if (s < 0.1) s = 0.1;
    554:       dlambda = dlambda + dlambda/s;
    555:       lambda  = lambda - dlambda;
    556:       ru      = r;
    557:       r       = phi(x, n, lambda, a, b, c, l, u);
    558:     }
    559:     lambdal = lambda;
    560:     rl      = r;
    561:   }

    563:   if(fabs(dlambda) > BMRM_INFTY) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"L2N2_DaiFletcherPGM detected Infeasible QP problem!");

    565:   if(ru == 0){
    566:     return innerIter;
    567:   }

    569:   /* Secant Phase */
    570:   s       = 1.0 - rl/ru;
    571:   dlambda = dlambda/s;
    572:   lambda  = lambdau - dlambda;
    573:   r       = phi(x, n, lambda, a, b, c, l, u);

    575:   while (fabs(r) > TOL_R
    576:          && dlambda > TOL_LAM * (1.0 + fabs(lambda))
    577:          && innerIter < df->maxProjIter){
    578:     innerIter++;
    579:     if (r > 0.0){
    580:       if (s <= 2.0){
    581:         lambdau = lambda;
    582:         ru      = r;
    583:         s       = 1.0 - rl/ru;
    584:         dlambda = (lambdau - lambdal) / s;
    585:         lambda  = lambdau - dlambda;
    586:       } else {
    587:         s          = ru/r-1.0;
    588:         if (s < 0.1) s = 0.1;
    589:         dlambda    = (lambdau - lambda) / s;
    590:         lambda_new = 0.75*lambdal + 0.25*lambda;
    591:         if (lambda_new < (lambda - dlambda))
    592:           lambda_new = lambda - dlambda;
    593:         lambdau    = lambda;
    594:         ru         = r;
    595:         lambda     = lambda_new;
    596:         s          = (lambdau - lambdal) / (lambdau - lambda);
    597:       }
    598:     } else {
    599:       if (s >= 2.0){
    600:         lambdal = lambda;
    601:         rl      = r;
    602:         s       = 1.0 - rl/ru;
    603:         dlambda = (lambdau - lambdal) / s;
    604:         lambda  = lambdau - dlambda;
    605:       } else {
    606:         s          = rl/r - 1.0;
    607:         if (s < 0.1) s = 0.1;
    608:         dlambda    = (lambda-lambdal) / s;
    609:         lambda_new = 0.75*lambdau + 0.25*lambda;
    610:         if (lambda_new > (lambda + dlambda))
    611:           lambda_new = lambda + dlambda;
    612:         lambdal    = lambda;
    613:         rl         = r;
    614:         lambda     = lambda_new;
    615:         s          = (lambdau - lambdal) / (lambdau-lambda);
    616:       }
    617:     }
    618:     r = phi(x, n, lambda, a, b, c, l, u);
    619:   }

    621:   *lam_ext = lambda;
    622:   if(innerIter >= df->maxProjIter) {
    623:     PetscPrintf(PETSC_COMM_SELF, "WARNING: DaiFletcher max iterations\n");
    624:   }
    625:   return innerIter;
    626: }

    631: PetscErrorCode solve(TAO_DF *df)
    632: {
    634:   PetscInt       i, j, innerIter, it, it2, luv, info, lscount = 0, projcount = 0;
    635:   PetscReal      gd, max, ak, bk, akold, bkold, lamnew, alpha, kktlam=0.0, lam_ext;
    636:   PetscReal      DELTAsv, ProdDELTAsv;
    637:   PetscReal      c, *tempQ;
    638:   PetscReal      *x = df->x, *a = df->a, b = df->b, *l = df->l, *u = df->u, tol = df->tol;
    639:   PetscReal      *tempv = df->tempv, *y = df->y, *g = df->g, *d = df->d, *Qd = df->Qd;
    640:   PetscReal      *xplus = df->xplus, *tplus = df->tplus, *sk = df->sk, *yk = df->yk;
    641:   PetscReal      **Q = df->Q, *f = df->f, *t = df->t;
    642:   PetscInt       dim = df->dim, *ipt = df->ipt, *ipt2 = df->ipt2, *uv = df->uv;

    644:   /*** variables for the adaptive nonmonotone linesearch ***/
    645:   PetscInt    L, llast;
    646:   PetscReal   fr, fbest, fv, fc, fv0;

    648:   c = BMRM_INFTY;

    650:   DELTAsv = EPS_SV;
    651:   if (tol <= 1.0e-5 || dim <= 20) ProdDELTAsv = 0.0F;
    652:   else  ProdDELTAsv = EPS_SV;

    654:   for (i = 0; i < dim; i++)  tempv[i] = -x[i];

    656:   lam_ext = 0.0;

    658:   /* Project the initial solution */
    659:   projcount += project(dim, a, b, tempv, l, u, x, &lam_ext, df);

    661:   /* Compute gradient
    662:      g = Q*x + f; */

    664:   it = 0;
    665:   for (i = 0; i < dim; i++) {
    666:     if (fabs(x[i]) > ProdDELTAsv) ipt[it++] = i;
    667:   }

    669:   PetscMemzero(t, dim*sizeof(PetscReal));
    670:   for (i = 0; i < it; i++){
    671:     tempQ = Q[ipt[i]];
    672:     for (j = 0; j < dim; j++) t[j] += (tempQ[j]*x[ipt[i]]);
    673:   }
    674:   for (i = 0; i < dim; i++){
    675:     g[i] = t[i] + f[i];
    676:   }

    679:   /* y = -(x_{k} - g_{k}) */
    680:   for (i = 0; i < dim; i++){
    681:     y[i] = g[i] - x[i];
    682:   }

    684:   /* Project x_{k} - g_{k} */
    685:   projcount += project(dim, a, b, y, l, u, tempv, &lam_ext, df);

    687:   /* y = P(x_{k} - g_{k}) - x_{k} */
    688:   max = ALPHA_MIN;
    689:   for (i = 0; i < dim; i++){
    690:     y[i] = tempv[i] - x[i];
    691:     if (fabs(y[i]) > max) max = fabs(y[i]);
    692:   }

    694:   if (max < tol*1e-3){
    695:     return 0;
    696:   }

    698:   alpha = 1.0 / max;

    700:   /* fv0 = f(x_{0}). Recall t = Q x_{k}  */
    701:   fv0   = 0.0;
    702:   for (i = 0; i < dim; i++) fv0 += x[i] * (0.5*t[i] + f[i]);

    704:   /*** adaptive nonmonotone linesearch ***/
    705:   L     = 2;
    706:   fr    = ALPHA_MAX;
    707:   fbest = fv0;
    708:   fc    = fv0;
    709:   llast = 0;
    710:   akold = bkold = 0.0;

    712:   /***      Iterator begins     ***/
    713:   for (innerIter = 1; innerIter <= df->maxPGMIter; innerIter++) {

    715:     /* tempv = -(x_{k} - alpha*g_{k}) */
    716:     for (i = 0; i < dim; i++)  tempv[i] = alpha*g[i] - x[i];

    718:     /* Project x_{k} - alpha*g_{k} */
    719:     projcount += project(dim, a, b, tempv, l, u, y, &lam_ext, df);

    722:     /* gd = \inner{d_{k}}{g_{k}}
    723:         d = P(x_{k} - alpha*g_{k}) - x_{k}
    724:     */
    725:     gd = 0.0;
    726:     for (i = 0; i < dim; i++){
    727:       d[i] = y[i] - x[i];
    728:       gd  += d[i] * g[i];
    729:     }

    731:     /* Gradient computation  */

    733:     /* compute Qd = Q*d  or  Qd = Q*y - t depending on their sparsity */

    735:     it = it2 = 0;
    736:     for (i = 0; i < dim; i++){
    737:       if (fabs(d[i]) > (ProdDELTAsv*1.0e-2)) ipt[it++]   = i;
    738:     }
    739:     for (i = 0; i < dim; i++) {
    740:       if (fabs(y[i]) > ProdDELTAsv) ipt2[it2++] = i;
    741:     }

    743:     PetscMemzero(Qd, dim*sizeof(PetscReal));
    744:     /* compute Qd = Q*d */
    745:     if (it < it2){
    746:       for (i = 0; i < it; i++){
    747:         tempQ = Q[ipt[i]];
    748:         for (j = 0; j < dim; j++) Qd[j] += (tempQ[j] * d[ipt[i]]);
    749:       }
    750:     } else { /* compute Qd = Q*y-t */
    751:       for (i = 0; i < it2; i++){
    752:         tempQ = Q[ipt2[i]];
    753:         for (j = 0; j < dim; j++) Qd[j] += (tempQ[j] * y[ipt2[i]]);
    754:       }
    755:       for (j = 0; j < dim; j++) Qd[j] -= t[j];
    756:     }

    758:     /* ak = inner{d_{k}}{d_{k}} */
    759:     ak = 0.0;
    760:     for (i = 0; i < dim; i++) ak += d[i] * d[i];

    762:     bk = 0.0;
    763:     for (i = 0; i < dim; i++) bk += d[i]*Qd[i];

    765:     if (bk > EPS*ak && gd < 0.0)  lamnew = -gd/bk;
    766:     else lamnew = 1.0;

    768:     /* fv is computing f(x_{k} + d_{k}) */
    769:     fv = 0.0;
    770:     for (i = 0; i < dim; i++){
    771:       xplus[i] = x[i] + d[i];
    772:       tplus[i] = t[i] + Qd[i];
    773:       fv      += xplus[i] * (0.5*tplus[i] + f[i]);
    774:     }

    776:     /* fr is fref */
    777:     if ((innerIter == 1 && fv >= fv0) || (innerIter > 1 && fv >= fr)){
    778:       lscount++;
    779:       fv = 0.0;
    780:       for (i = 0; i < dim; i++){
    781:         xplus[i] = x[i] + lamnew*d[i];
    782:         tplus[i] = t[i] + lamnew*Qd[i];
    783:         fv      += xplus[i] * (0.5*tplus[i] + f[i]);
    784:       }
    785:     }

    787:     for (i = 0; i < dim; i++){
    788:       sk[i] = xplus[i] - x[i];
    789:       yk[i] = tplus[i] - t[i];
    790:       x[i]  = xplus[i];
    791:       t[i]  = tplus[i];
    792:       g[i]  = t[i] + f[i];
    793:     }

    795:     /* update the line search control parameters */
    796:     if (fv < fbest){
    797:       fbest = fv;
    798:       fc    = fv;
    799:       llast = 0;
    800:     } else {
    801:       fc = (fc > fv ? fc : fv);
    802:       llast++;
    803:       if (llast == L){
    804:         fr    = fc;
    805:         fc    = fv;
    806:         llast = 0;
    807:       }
    808:     }

    810:     ak = bk = 0.0;
    811:     for (i = 0; i < dim; i++){
    812:       ak += sk[i] * sk[i];
    813:       bk += sk[i] * yk[i];
    814:     }

    816:     if (bk <= EPS*ak) alpha = ALPHA_MAX;
    817:     else {
    818:       if (bkold < EPS*akold) alpha = ak/bk;
    819:       else alpha = (akold+ak)/(bkold+bk);

    821:       if (alpha > ALPHA_MAX) alpha = ALPHA_MAX;
    822:       else if (alpha < ALPHA_MIN) alpha = ALPHA_MIN;
    823:     }

    825:     akold = ak;
    826:     bkold = bk;

    828:     /*** stopping criterion based on KKT conditions ***/
    829:     /* at optimal, gradient of lagrangian w.r.t. x is zero */

    831:     bk = 0.0;
    832:     for (i = 0; i < dim; i++) bk +=  x[i] * x[i];

    834:     if (PetscSqrtReal(ak) < tol*10 * PetscSqrtReal(bk)){
    835:       it     = 0;
    836:       luv    = 0;
    837:       kktlam = 0.0;
    838:       for (i = 0; i < dim; i++){
    839:         /* x[i] is active hence lagrange multipliers for box constraints
    840:                 are zero. The lagrange multiplier for ineq. const. is then
    841:                 defined as below
    842:         */
    843:         if ((x[i] > DELTAsv) && (x[i] < c-DELTAsv)){
    844:           ipt[it++] = i;
    845:           kktlam    = kktlam - a[i]*g[i];
    846:         } else  uv[luv++] = i;
    847:       }

    849:       if (it == 0 && PetscSqrtReal(ak) < tol*0.5 * PetscSqrtReal(bk)) return 0;
    850:       else {
    851:         kktlam = kktlam/it;
    852:         info   = 1;
    853:         for (i = 0; i < it; i++) {
    854:           if (fabs(a[ipt[i]] * g[ipt[i]] + kktlam) > tol) {
    855:             info = 0;
    856:             break;
    857:           }
    858:         }
    859:         if (info == 1)  {
    860:           for (i = 0; i < luv; i++)  {
    861:             if (x[uv[i]] <= DELTAsv){
    862:               /* x[i] == lower bound, hence, lagrange multiplier (say, beta) for lower bound may
    863:                      not be zero. So, the gradient without beta is > 0
    864:               */
    865:               if (g[uv[i]] + kktlam*a[uv[i]] < -tol){
    866:                 info = 0;
    867:                 break;
    868:               }
    869:             } else {
    870:               /* x[i] == upper bound, hence, lagrange multiplier (say, eta) for upper bound may
    871:                      not be zero. So, the gradient without eta is < 0
    872:               */
    873:               if (g[uv[i]] + kktlam*a[uv[i]] > tol) {
    874:                 info = 0;
    875:                 break;
    876:               }
    877:             }
    878:           }
    879:         }

    881:         if (info == 1) return 0;
    882:       }
    883:     }
    884:   }
    885:   return 0;
    886: }