MoochoPack_QuasiRangeSpaceStepTailoredApproach_Strategy.cpp

#if 0

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// Moocho: Multi-functional Object-Oriented arCHitecture for Optimization
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#include "MoochoPack_QuasiRangeSpaceStepTailoredApproach_Strategy.hpp"
#include "MoochoPack_MoochoAlgorithmStepNames.hpp"
#include "MoochoPack_NLPAlgo.hpp"
#include "MoochoPack_NLPAlgoState.hpp"
#include "MoochoPack/src/NLPrSQPTailoredApproach.h"
#include "MoochoPack_EvalNewPointTailoredApproach_Step.hpp"
#include "ConstrainedOptPack/src/DenseIdentVertConcatMatrixSubclass.h"
#include "AbstractLinAlgPack/src/AbstractLinAlgPack_MatrixOp.hpp"
#include "DenseLinAlgPack_LinAlgOpPack.hpp"
#include "MiWorkspacePack.h"
#include "Midynamic_cast_verbose.h"

namespace LinAlgOpPack {
  using AbstractLinAlgPack::Vp_StMtV;
}

namespace MoochoPack {

bool QuasiRangeSpaceStepTailoredApproach_Strategy::solve_quasi_range_space_step(
  std::ostream& out, EJournalOutputLevel olevel, NLPAlgo *algo, NLPAlgoState *s
  ,const DVectorSlice& xo, const DVectorSlice& c_xo, DVectorSlice* v
    )
{
  using Teuchos::dyn_cast;
  using Teuchos::Workspace;
  Teuchos::WorkspaceStore* wss = Teuchos::get_default_workspace_store().get();

  // Get NLP reference
#ifdef _WINDOWS
  NLPrSQPTailoredApproach
    &nlp  = dynamic_cast<NLPrSQPTailoredApproach&>(algo->nlp());
#else
  NLPrSQPTailoredApproach
    &nlp  = dyn_cast<NLPrSQPTailoredApproach>(algo->nlp());
#endif

  // Get D for Z_k = [ D; I ]
  const MatrixOp
    &Z_k = s->Z().get_k(0);
#ifdef _WINDOWS
  const DenseIdentVertConcatMatrixSubclass
    &cZ_k = dynamic_cast<const DenseIdentVertConcatMatrixSubclass&>(Z_k);
#else
  const DenseIdentVertConcatMatrixSubclass
    &cZ_k = dyn_cast<const DenseIdentVertConcatMatrixSubclass>(Z_k);
#endif
  const DMatrixSlice
    D = cZ_k.m().D();

  // Get reference to EvalNewPoint step
#ifdef _WINDOWS
  EvalNewPointTailoredApproach_Step
    &eval_tailored = dynamic_cast<EvalNewPointTailoredApproach_Step&>(
      *algo->get_step(algo->get_step_poss(EvalNewPoint_name)));
#else
  EvalNewPointTailoredApproach_Step
    &eval_tailored = dyn_cast<EvalNewPointTailoredApproach_Step>(
      *algo->get_step(algo->get_step_poss(EvalNewPoint_name)));
#endif

  // Compute an approximate newton step for constriants wy
  DVector c_xo_tmp = c_xo, vy_tmp;  // This is hacked.  This sucks!
  nlp.calc_semi_newton_step(xo,&c_xo_tmp,false,&vy_tmp);
    
  // Compute wy, Ywy
  eval_tailored.recalc_py_Ypy(D,&vy_tmp(),v,olevel,out);

  return true;
}

void QuasiRangeSpaceStepTailoredApproach_Strategy::print_step( std::ostream& out, const std::string& L ) const
{
  out << L << "*** Compute the approximate range space step by calling on the \"Tailored Approach\" NLP interface:\n"
    << L << "Compute vy s.t. ||Gc_k'*Y_k*vy + c_xo|| << ||c_xo|| (nlp.calc_semi_newton_step(...))\n"
    << L << "update vy and compute v = Yvy from EvalNewPointTailoredApproach_Step::recalc_py_Ypy(...)\n";
    ;
}

} // end namespace MoochoPack

#endif // 0