Actual source code: bntl.c
petsc-3.10.5 2019-03-28
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      1:  #include <../src/tao/bound/impls/bnk/bnk.h>
      2:  #include <petscksp.h>

      4: /*
      5:  Implements Newton's Method with a trust region approach for solving
      6:  bound constrained minimization problems.
      7:
      8:  In this variant, the trust region failures trigger a line search with
      9:  the existing Newton step instead of re-solving the step with a
     10:  different radius.
     11:
     12:  ------------------------------------------------------------
     13:
     14:  x_0 = VecMedian(x_0)
     15:  f_0, g_0 = TaoComputeObjectiveAndGradient(x_0)
     16:  pg_0 = project(g_0)
     17:  check convergence at pg_0
     18:  needH = TaoBNKInitialize(default:BNK_INIT_INTERPOLATION)
     19:  niter = 0
     20:  step_accepted = true

     22:  while niter <= max_it
     23:     niter += 1
     24:
     25:     if needH
     26:       If max_cg_steps > 0
     27:         x_k, g_k, pg_k = TaoSolve(BNCG)
     28:       end

     30:       H_k = TaoComputeHessian(x_k)
     31:       if pc_type == BNK_PC_BFGS
     32:         add correction to BFGS approx
     33:         if scale_type == BNK_SCALE_AHESS
     34:           D = VecMedian(1e-6, abs(diag(H_k)), 1e6)
     35:           scale BFGS with VecReciprocal(D)
     36:         end
     37:       end
     38:       needH = False
     39:     end

     41:     if pc_type = BNK_PC_BFGS
     42:       B_k = BFGS
     43:     else
     44:       B_k = VecMedian(1e-6, abs(diag(H_k)), 1e6)
     45:       B_k = VecReciprocal(B_k)
     46:     end
     47:     w = x_k - VecMedian(x_k - 0.001*B_k*g_k)
     48:     eps = min(eps, norm2(w))
     49:     determine the active and inactive index sets such that
     50:       L = {i : (x_k)_i <= l_i + eps && (g_k)_i > 0}
     51:       U = {i : (x_k)_i >= u_i - eps && (g_k)_i < 0}
     52:       F = {i : l_i = (x_k)_i = u_i}
     53:       A = {L + U + F}
     54:       IA = {i : i not in A}

     56:     generate the reduced system Hr_k dr_k = -gr_k for variables in IA
     57:     if pc_type == BNK_PC_BFGS && scale_type == BNK_SCALE_PHESS
     58:       D = VecMedian(1e-6, abs(diag(Hr_k)), 1e6)
     59:       scale BFGS with VecReciprocal(D)
     60:     end
     61:     solve Hr_k dr_k = -gr_k
     62:     set d_k to (l - x) for variables in L, (u - x) for variables in U, and 0 for variables in F

     64:     x_{k+1} = VecMedian(x_k + d_k)
     65:     s = x_{k+1} - x_k
     66:     prered = dot(s, 0.5*gr_k - Hr_k*s)
     67:     f_{k+1} = TaoComputeObjective(x_{k+1})
     68:     actred = f_k - f_{k+1}

     70:     oldTrust = trust
     71:     step_accepted, trust = TaoBNKUpdateTrustRadius(default: BNK_UPDATE_REDUCTION)
     72:     if step_accepted
     73:       g_{k+1} = TaoComputeGradient(x_{k+1})
     74:       pg_{k+1} = project(g_{k+1})
     75:       count the accepted Newton step
     76:     else
     77:       if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     78:         dr_k = -BFGS*gr_k for variables in I
     79:         if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     80:           reset the BFGS preconditioner
     81:           calculate scale delta and apply it to BFGS
     82:           dr_k = -BFGS*gr_k for variables in I
     83:           if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     84:             dr_k = -gr_k for variables in I
     85:           end
     86:         end
     87:       end
     88:
     89:       x_{k+1}, f_{k+1}, g_{k+1}, ls_failed = TaoBNKPerformLineSearch()
     90:       if ls_failed
     91:         f_{k+1} = f_k
     92:         x_{k+1} = x_k
     93:         g_{k+1} = g_k
     94:         pg_{k+1} = pg_k
     95:         terminate
     96:       else
     97:         pg_{k+1} = project(g_{k+1})
     98:         trust = oldTrust
     99:         trust = TaoBNKUpdateTrustRadius(BNK_UPDATE_STEP)
    100:         count the accepted step type (Newton, BFGS, scaled grad or grad)
    101:       end
    102:     end

    104:     check convergence at pg_{k+1}
    105:  end
    106: */

    108: PetscErrorCode TaoSolve_BNTL(Tao tao)
    109: {
    110:   PetscErrorCode               ierr;
    111:   TAO_BNK                      *bnk = (TAO_BNK *)tao->data;
    112:   KSPConvergedReason           ksp_reason;
    113:   TaoLineSearchConvergedReason ls_reason;

    115:   PetscReal                    oldTrust, prered, actred, steplen, resnorm;
    116:   PetscBool                    cgTerminate, needH = PETSC_TRUE, stepAccepted, shift = PETSC_FALSE;
    117:   PetscInt                     stepType, nDiff;
    118:
    120:   /* Initialize the preconditioner, KSP solver and trust radius/line search */
    121:   tao->reason = TAO_CONTINUE_ITERATING;
    122:   TaoBNKInitialize(tao, bnk->init_type, &needH);
    123:   if (tao->reason != TAO_CONTINUE_ITERATING) return(0);

    125:   /* Have not converged; continue with Newton method */
    126:   while (tao->reason == TAO_CONTINUE_ITERATING) {
    127:     ++tao->niter;
    128:
    129:     if (needH && bnk->inactive_idx) {
    130:       /* Take BNCG steps (if enabled) to trade-off Hessian evaluations for more gradient evaluations */
    131:       TaoBNKTakeCGSteps(tao, &cgTerminate);
    132:       if (cgTerminate) {
    133:         tao->reason = bnk->bncg->reason;
    134:         return(0);
    135:       }
    136:       /* Compute the hessian and update the BFGS preconditioner at the new iterate */
    137:       (*bnk->computehessian)(tao);
    138:       needH = PETSC_FALSE;
    139:     }
    140:
    141:     /* Use the common BNK kernel to compute the Newton step (for inactive variables only) */
    142:     (*bnk->computestep)(tao, shift, &ksp_reason, &stepType);

    144:     /* Store current solution before it changes */
    145:     oldTrust = tao->trust;
    146:     bnk->fold = bnk->f;
    147:     VecCopy(tao->solution, bnk->Xold);
    148:     VecCopy(tao->gradient, bnk->Gold);
    149:     VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);
    150:
    151:     /* Temporarily accept the step and project it into the bounds */
    152:     VecAXPY(tao->solution, 1.0, tao->stepdirection);
    153:     TaoBoundSolution(tao->solution, tao->XL,tao->XU, 0.0, &nDiff, tao->solution);
    154:
    155:     /* Check if the projection changed the step direction */
    156:     if (nDiff > 0) {
    157:       /* Projection changed the step, so we have to recompute the step and
    158:          the predicted reduction. Leave the trust radius unchanged. */
    159:       VecCopy(tao->solution, tao->stepdirection);
    160:       VecAXPY(tao->stepdirection, -1.0, bnk->Xold);
    161:       TaoBNKRecomputePred(tao, tao->stepdirection, &prered);
    162:     } else {
    163:       /* Step did not change, so we can just recover the pre-computed prediction */
    164:       KSPCGGetObjFcn(tao->ksp, &prered);
    165:     }
    166:     prered = -prered;
    167:
    168:     /* Compute the actual reduction and update the trust radius */
    169:     TaoComputeObjective(tao, tao->solution, &bnk->f);
    170:     if (PetscIsInfOrNanReal(bnk->f)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
    171:     actred = bnk->fold - bnk->f;
    172:     TaoBNKUpdateTrustRadius(tao, prered, actred, bnk->update_type, stepType, &stepAccepted);
    173:
    174:     if (stepAccepted) {
    175:       /* Step is good, evaluate the gradient and the hessian */
    176:       steplen = 1.0;
    177:       needH = PETSC_TRUE;
    178:       ++bnk->newt;
    179:       TaoComputeGradient(tao, tao->solution, bnk->unprojected_gradient);
    180:       TaoBNKEstimateActiveSet(tao, bnk->as_type);
    181:       VecCopy(bnk->unprojected_gradient, tao->gradient);
    182:       VecISSet(tao->gradient, bnk->active_idx, 0.0);
    183:       TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);
    184:     } else {
    185:       /* Trust-region rejected the step. Revert the solution. */
    186:       bnk->f = bnk->fold;
    187:       VecCopy(bnk->Xold, tao->solution);
    188:       /* Trigger the line search */
    189:       TaoBNKSafeguardStep(tao, ksp_reason, &stepType);
    190:       TaoBNKPerformLineSearch(tao, &stepType, &steplen, &ls_reason);
    191:       if (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER) {
    192:         /* Line search failed, revert solution and terminate */
    193:         stepAccepted = PETSC_FALSE;
    194:         needH = PETSC_FALSE;
    195:         bnk->f = bnk->fold;
    196:         VecCopy(bnk->Xold, tao->solution);
    197:         VecCopy(bnk->Gold, tao->gradient);
    198:         VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);
    199:         tao->trust = 0.0;
    200:         tao->reason = TAO_DIVERGED_LS_FAILURE;
    201:       } else {
    202:         /* new iterate so we need to recompute the Hessian */
    203:         needH = PETSC_TRUE;
    204:         /* compute the projected gradient */
    205:         TaoBNKEstimateActiveSet(tao, bnk->as_type);
    206:         VecCopy(bnk->unprojected_gradient, tao->gradient);
    207:         VecISSet(tao->gradient, bnk->active_idx, 0.0);
    208:         TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);
    209:         /* Line search succeeded so we should update the trust radius based on the LS step length */
    210:         tao->trust = oldTrust;
    211:         TaoBNKUpdateTrustRadius(tao, prered, actred, BNK_UPDATE_STEP, stepType, &stepAccepted);
    212:         /* count the accepted step type */
    213:         TaoBNKAddStepCounts(tao, stepType);
    214:       }
    215:     }

    217:     /*  Check for termination */
    218:     VecFischer(tao->solution, bnk->unprojected_gradient, tao->XL, tao->XU, bnk->W);
    219:     VecNorm(bnk->W, NORM_2, &resnorm);
    220:     if (PetscIsInfOrNanReal(resnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
    221:     TaoLogConvergenceHistory(tao, bnk->f, resnorm, 0.0, tao->ksp_its);
    222:     TaoMonitor(tao, tao->niter, bnk->f, resnorm, 0.0, steplen);
    223:     (*tao->ops->convergencetest)(tao, tao->cnvP);
    224:   }
    225:   return(0);
    226: }

    228: /*------------------------------------------------------------*/

    230: static PetscErrorCode TaoSetFromOptions_BNTL(PetscOptionItems *PetscOptionsObject,Tao tao)
    231: {
    232:   TAO_BNK        *bnk = (TAO_BNK *)tao->data;

    236:   TaoSetFromOptions_BNK(PetscOptionsObject, tao);
    237:   if (bnk->update_type == BNK_UPDATE_STEP) bnk->update_type = BNK_UPDATE_REDUCTION;
    238:   if (!bnk->is_nash && !bnk->is_stcg && !bnk->is_gltr) SETERRQ(PETSC_COMM_SELF,1,"Must use a trust-region CG method for KSP (KSPNASH, KSPSTCG, KSPGLTR)");
    239:   return(0);
    240: }

    242: /*------------------------------------------------------------*/

    244: PETSC_EXTERN PetscErrorCode TaoCreate_BNTL(Tao tao)
    245: {
    246:   TAO_BNK        *bnk;
    248:
    250:   TaoCreate_BNK(tao);
    251:   tao->ops->solve=TaoSolve_BNTL;
    252:   tao->ops->setfromoptions=TaoSetFromOptions_BNTL;
    253:
    254:   bnk = (TAO_BNK *)tao->data;
    255:   bnk->update_type = BNK_UPDATE_REDUCTION; /* trust region updates based on predicted/actual reduction */
    256:   return(0);
    257: }