Date: 2022-03-04T17:39:53+00:00

Actual source code: bqnls.c

      1: #include <../src/tao/bound/impls/bqnk/bqnk.h>

      3: static const char *BNK_AS[64] = {"none", "bertsekas"};

      5: static PetscErrorCode TaoBQNLSComputeHessian(Tao tao)
      6: {
      7:   TAO_BNK        *bnk = (TAO_BNK *)tao->data;
      8:   TAO_BQNK       *bqnk = (TAO_BQNK*)bnk->ctx;
     10:   PetscReal      gnorm2, delta;

     13:   /* Compute the initial scaling and update the approximation */
     14:   gnorm2 = bnk->gnorm*bnk->gnorm;
     15:   if (gnorm2 == 0.0) gnorm2 = PETSC_MACHINE_EPSILON;
     16:   if (bnk->f == 0.0) {
     17:     delta = 2.0 / gnorm2;
     18:   } else {
     19:     delta = 2.0 * PetscAbsScalar(bnk->f) / gnorm2;
     20:   }
     21:   MatLMVMSymBroydenSetDelta(bqnk->B, delta);
     22:   MatLMVMUpdate(bqnk->B, tao->solution, bnk->unprojected_gradient);
     23:   return(0);
     24: }

     26: static PetscErrorCode TaoBQNLSComputeStep(Tao tao, PetscBool shift, KSPConvergedReason *ksp_reason, PetscInt *step_type)
     27: {
     28:   TAO_BNK        *bnk = (TAO_BNK *)tao->data;
     29:   TAO_BQNK       *bqnk = (TAO_BQNK*)bnk->ctx;
     31:   PetscInt       nupdates;

     34:   MatSolve(bqnk->B, tao->gradient, tao->stepdirection);
     35:   VecScale(tao->stepdirection, -1.0);
     36:   TaoBNKBoundStep(tao, bnk->as_type, tao->stepdirection);
     37:   *ksp_reason = KSP_CONVERGED_ATOL;
     38:   MatLMVMGetUpdateCount(bqnk->B, &nupdates);
     39:   if (nupdates == 0) {
     40:     *step_type = BNK_SCALED_GRADIENT;
     41:   } else {
     42:     *step_type = BNK_BFGS;
     43:   }
     44:   return(0);
     45: }

     47: static PetscErrorCode TaoSetFromOptions_BQNLS(PetscOptionItems *PetscOptionsObject,Tao tao)
     48: {
     49:   TAO_BNK        *bnk = (TAO_BNK *)tao->data;
     50:   TAO_BQNK       *bqnk = (TAO_BQNK*)bnk->ctx;
     52:   KSPType        ksp_type;
     53:   PetscBool      is_spd;

     56:   PetscOptionsHead(PetscOptionsObject,"Quasi-Newton-Krylov method for bound constrained optimization");
     57:   PetscOptionsEList("-tao_bqnls_as_type", "active set estimation method", "", BNK_AS, BNK_AS_TYPES, BNK_AS[bnk->as_type], &bnk->as_type, NULL);
     58:   PetscOptionsReal("-tao_bqnls_epsilon", "(developer) tolerance used when computing actual and predicted reduction", "", bnk->epsilon, &bnk->epsilon,NULL);
     59:   PetscOptionsReal("-tao_bqnls_as_tol", "(developer) initial tolerance used when estimating actively bounded variables", "", bnk->as_tol, &bnk->as_tol,NULL);
     60:   PetscOptionsReal("-tao_bqnls_as_step", "(developer) step length used when estimating actively bounded variables", "", bnk->as_step, &bnk->as_step,NULL);
     61:   PetscOptionsInt("-tao_bqnls_max_cg_its", "number of BNCG iterations to take for each Newton step", "", bnk->max_cg_its, &bnk->max_cg_its,NULL);
     62:   PetscOptionsTail();
     63:   TaoSetFromOptions(bnk->bncg);
     64:   TaoLineSearchSetFromOptions(tao->linesearch);
     65:   KSPSetFromOptions(tao->ksp);
     66:   KSPGetType(tao->ksp,&ksp_type);
     67:   bnk->is_nash = bnk->is_gltr = bnk->is_stcg = PETSC_FALSE;
     68:   MatSetFromOptions(bqnk->B);
     69:   MatGetOption(bqnk->B, MAT_SPD, &is_spd);
     70:   if (!is_spd) SETERRQ(PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_INCOMP, "LMVM matrix must be symmetric positive-definite");
     71:   return(0);
     72: }

     74: /*MC
     75:   TAOBQNLS - Bounded Quasi-Newton Line Search method for nonlinear minimization with bound
     76:              constraints. This method approximates the action of the inverse-Hessian with a
     77:              limited memory quasi-Newton formula. The quasi-Newton matrix and its options are
     78:              accessible via the prefix `-tao_bqnls_`

     80:   Options Database Keys:
     81: + -tao_bqnls_max_cg_its - maximum number of bounded conjugate-gradient iterations taken in each Newton loop
     82: - -tao_bqnls_as_type - active-set estimation method ("none", "bertsekas")

     84:   Level: beginner
     85: M*/
     86: PETSC_EXTERN PetscErrorCode TaoCreate_BQNLS(Tao tao)
     87: {
     88:   TAO_BNK        *bnk;
     89:   TAO_BQNK       *bqnk;

     93:   TaoCreate_BQNK(tao);
     94:   KSPSetOptionsPrefix(tao->ksp, "unused");
     95:   tao->ops->setfromoptions = TaoSetFromOptions_BQNLS;

     97:   bnk = (TAO_BNK*)tao->data;
     98:   bnk->update_type = BNK_UPDATE_STEP;
     99:   bnk->computehessian = TaoBQNLSComputeHessian;
    100:   bnk->computestep = TaoBQNLSComputeStep;

    102:   bqnk = (TAO_BQNK*)bnk->ctx;
    103:   bqnk->solve = TaoSolve_BNLS;
    104:   MatSetOptionsPrefix(bqnk->B, "tao_bqnls_");
    105:   MatSetType(bqnk->B, MATLMVMBFGS);
    106:   return(0);
    107: }