Actual source code: bnls.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 line search approach for
      6:  solving bound constrained minimization problems.
      7:
      8:  ------------------------------------------------------------
      9:
     10:  x_0 = VecMedian(x_0)
     11:  f_0, g_0 = TaoComputeObjectiveAndGradient(x_0)
     12:  pg_0 = project(g_0)
     13:  check convergence at pg_0
     14:  needH = TaoBNKInitialize(default:BNK_INIT_DIRECTION)
     15:  niter = 0
     16:  step_accepted = true
     17:
     18:  while niter < max_it
     19:     niter += 1
     20:
     21:     if needH
     22:       If max_cg_steps > 0
     23:         x_k, g_k, pg_k = TaoSolve(BNCG)
     24:       end

     26:       H_k = TaoComputeHessian(x_k)
     27:       if pc_type == BNK_PC_BFGS
     28:         add correction to BFGS approx
     29:         if scale_type == BNK_SCALE_AHESS
     30:           D = VecMedian(1e-6, abs(diag(H_k)), 1e6)
     31:           scale BFGS with VecReciprocal(D)
     32:         end
     33:       end
     34:       needH = False
     35:     end
     36:
     37:     if pc_type = BNK_PC_BFGS
     38:       B_k = BFGS
     39:     else
     40:       B_k = VecMedian(1e-6, abs(diag(H_k)), 1e6)
     41:       B_k = VecReciprocal(B_k)
     42:     end
     43:     w = x_k - VecMedian(x_k - 0.001*B_k*g_k)
     44:     eps = min(eps, norm2(w))
     45:     determine the active and inactive index sets such that
     46:       L = {i : (x_k)_i <= l_i + eps && (g_k)_i > 0}
     47:       U = {i : (x_k)_i >= u_i - eps && (g_k)_i < 0}
     48:       F = {i : l_i = (x_k)_i = u_i}
     49:       A = {L + U + F}
     50:       IA = {i : i not in A}
     51:
     52:     generate the reduced system Hr_k dr_k = -gr_k for variables in IA
     53:     if p > 0
     54:       Hr_k += p*
     55:     end
     56:     if pc_type == BNK_PC_BFGS && scale_type == BNK_SCALE_PHESS
     57:       D = VecMedian(1e-6, abs(diag(Hr_k)), 1e6)
     58:       scale BFGS with VecReciprocal(D)
     59:     end
     60:     solve Hr_k dr_k = -gr_k
     61:     set d_k to (l - x) for variables in L, (u - x) for variables in U, and 0 for variables in F
     62:
     63:     if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     64:       dr_k = -BFGS*gr_k for variables in I
     65:       if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     66:         reset the BFGS preconditioner
     67:         calculate scale delta and apply it to BFGS
     68:         dr_k = -BFGS*gr_k for variables in I
     69:         if dot(d_k, pg_k)) >= 0 || norm(d_k) == NaN || norm(d_k) == Inf
     70:           dr_k = -gr_k for variables in I
     71:         end
     72:       end
     73:     end
     74:
     75:     x_{k+1}, f_{k+1}, g_{k+1}, ls_failed = TaoBNKPerformLineSearch()
     76:     if ls_failed
     77:       f_{k+1} = f_k
     78:       x_{k+1} = x_k
     79:       g_{k+1} = g_k
     80:       pg_{k+1} = pg_k
     81:       terminate
     82:     else
     83:       pg_{k+1} = project(g_{k+1})
     84:       count the accepted step type (Newton, BFGS, scaled grad or grad)
     85:     end
     86:
     87:     check convergence at pg_{k+1}
     88:  end
     89: */

     91: PetscErrorCode TaoSolve_BNLS(Tao tao)
     92: {
     93:   PetscErrorCode               ierr;
     94:   TAO_BNK                      *bnk = (TAO_BNK *)tao->data;
     95:   KSPConvergedReason           ksp_reason;
     96:   TaoLineSearchConvergedReason ls_reason;

     98:   PetscReal                    steplen = 1.0, resnorm;
     99:   PetscBool                    cgTerminate, needH = PETSC_TRUE, stepAccepted, shift = PETSC_TRUE;
    100:   PetscInt                     stepType;
    101:
    103:   /* Initialize the preconditioner, KSP solver and trust radius/line search */
    104:   tao->reason = TAO_CONTINUE_ITERATING;
    105:   TaoBNKInitialize(tao, bnk->init_type, &needH);
    106:   if (tao->reason != TAO_CONTINUE_ITERATING) return(0);

    108:   /* Have not converged; continue with Newton method */
    109:   while (tao->reason == TAO_CONTINUE_ITERATING) {
    110:     ++tao->niter;
    111:
    112:     if (needH && bnk->inactive_idx) {
    113:       /* Take BNCG steps (if enabled) to trade-off Hessian evaluations for more gradient evaluations */
    114:       TaoBNKTakeCGSteps(tao, &cgTerminate);
    115:       if (cgTerminate) {
    116:         tao->reason = bnk->bncg->reason;
    117:         return(0);
    118:       }
    119:       /* Compute the hessian and update the BFGS preconditioner at the new iterate */
    120:       (*bnk->computehessian)(tao);
    121:       needH = PETSC_FALSE;
    122:     }
    123:
    124:     /* Use the common BNK kernel to compute the safeguarded Newton step (for inactive variables only) */
    125:     (*bnk->computestep)(tao, shift, &ksp_reason, &stepType);
    126:     TaoBNKSafeguardStep(tao, ksp_reason, &stepType);

    128:     /* Store current solution before it changes */
    129:     bnk->fold = bnk->f;
    130:     VecCopy(tao->solution, bnk->Xold);
    131:     VecCopy(tao->gradient, bnk->Gold);
    132:     VecCopy(bnk->unprojected_gradient, bnk->unprojected_gradient_old);
    133:
    134:     /* Trigger the line search */
    135:     TaoBNKPerformLineSearch(tao, &stepType, &steplen, &ls_reason);

    137:     if (ls_reason != TAOLINESEARCH_SUCCESS && ls_reason != TAOLINESEARCH_SUCCESS_USER) {
    138:       /* Failed to find an improving point */
    139:       needH = PETSC_FALSE;
    140:       bnk->f = bnk->fold;
    141:       VecCopy(bnk->Xold, tao->solution);
    142:       VecCopy(bnk->Gold, tao->gradient);
    143:       VecCopy(bnk->unprojected_gradient_old, bnk->unprojected_gradient);
    144:       steplen = 0.0;
    145:       tao->reason = TAO_DIVERGED_LS_FAILURE;
    146:     } else {
    147:       /* new iterate so we need to recompute the Hessian */
    148:       needH = PETSC_TRUE;
    149:       /* compute the projected gradient */
    150:       TaoBNKEstimateActiveSet(tao, bnk->as_type);
    151:       VecCopy(bnk->unprojected_gradient, tao->gradient);
    152:       VecISSet(tao->gradient, bnk->active_idx, 0.0);
    153:       TaoGradientNorm(tao, tao->gradient, NORM_2, &bnk->gnorm);
    154:       /* update the trust radius based on the step length */
    155:       TaoBNKUpdateTrustRadius(tao, 0.0, 0.0, BNK_UPDATE_STEP, stepType, &stepAccepted);
    156:       /* count the accepted step type */
    157:       TaoBNKAddStepCounts(tao, stepType);
    158:       /* active BNCG recycling for next iteration */
    159:       TaoBNCGSetRecycleFlag(bnk->bncg, PETSC_TRUE);
    160:     }

    162:     /*  Check for termination */
    163:     VecFischer(tao->solution, bnk->unprojected_gradient, tao->XL, tao->XU, bnk->W);
    164:     VecNorm(bnk->W, NORM_2, &resnorm);
    165:     if (PetscIsInfOrNanReal(resnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");
    166:     TaoLogConvergenceHistory(tao, bnk->f, resnorm, 0.0, tao->ksp_its);
    167:     TaoMonitor(tao, tao->niter, bnk->f, resnorm, 0.0, steplen);
    168:     (*tao->ops->convergencetest)(tao, tao->cnvP);
    169:   }
    170:   return(0);
    171: }

    173: /*------------------------------------------------------------*/

    175: PETSC_EXTERN PetscErrorCode TaoCreate_BNLS(Tao tao)
    176: {
    177:   TAO_BNK        *bnk;
    179:
    181:   TaoCreate_BNK(tao);
    182:   tao->ops->solve = TaoSolve_BNLS;
    183:
    184:   bnk = (TAO_BNK *)tao->data;
    185:   bnk->init_type = BNK_INIT_DIRECTION;
    186:   bnk->update_type = BNK_UPDATE_STEP; /* trust region updates based on line search step length */
    187:   return(0);
    188: }