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: }