Actual source code: bqnktr.c

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

      4: static PetscErrorCode TaoSetUp_BQNKTR(Tao tao)
      5: {
      6:   TAO_BNK         *bnk = (TAO_BNK*)tao->data;

     10:   TaoSetUp_BQNK(tao);
     11:   if (!bnk->is_nash && !bnk->is_stcg && !bnk->is_gltr) SETERRQ(PetscObjectComm((PetscObject)tao),PETSC_ERR_SUP,"Must use a trust-region CG method for KSP (KSPNASH, KSPSTCG, KSPGLTR)");
     12:   return(0);
     13: }

     15: /*MC
     16:   TAOBQNKTR - Bounded Quasi-Newton-Krylov Trust Region method for nonlinear minimization with
     17:               bound constraints. This method approximates the Hessian-vector product using a
     18:               limited-memory quasi-Newton formula, and iteratively inverts the Hessian with a
     19:               Krylov solver. The quasi-Newton matrix and its settings can be accessed via the
     20:               prefix `-tao_bqnk_`

     22:   Options Database Keys:
     23: + -tao_bqnk_max_cg_its - maximum number of bounded conjugate-gradient iterations taken in each Newton loop
     24: . -tao_bqnk_init_type - trust radius initialization method ("constant", "direction", "interpolation")
     25: . -tao_bqnk_update_type - trust radius update method ("step", "direction", "interpolation")
     26: - -tao_bqnk_as_type - active-set estimation method ("none", "bertsekas")

     28:   Level: beginner
     29: M*/
     30: PETSC_EXTERN PetscErrorCode TaoCreate_BQNKTR(Tao tao)
     31: {
     32:   TAO_BNK        *bnk;
     33:   TAO_BQNK       *bqnk;

     37:   TaoCreate_BQNK(tao);
     38:   tao->ops->setup = TaoSetUp_BQNKTR;
     39:   bnk = (TAO_BNK*)tao->data;
     40:   bqnk = (TAO_BQNK*)bnk->ctx;
     41:   bqnk->solve = TaoSolve_BNTR;
     42:   return(0);
     43: }