MoochoPack_CheckDecompositionFromPy_Step.cpp

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// ***********************************************************************
// 
// Moocho: Multi-functional Object-Oriented arCHitecture for Optimization
//                  Copyright (2003) Sandia Corporation
// 
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
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// This library is free software; you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as
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//  
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
// Lesser General Public License for more details.
//  
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
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// Questions? Contact Roscoe A. Bartlett (rabartl@sandia.gov) 
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#include <typeinfo>
#include <limits>

#include "MoochoPack_CheckDecompositionFromPy_Step.hpp"
#include "MoochoPack_moocho_algo_conversion.hpp"
#include "IterationPack_print_algorithm_step.hpp"
#include "AbstractLinAlgPack_Vector.hpp"
#include "AbstractLinAlgPack_LinAlgOpPack.hpp"

namespace MoochoPack {

CheckDecompositionFromPy_Step::CheckDecompositionFromPy_Step(
  const new_decomp_strategy_ptr_t   &new_decomp_strategy
  ,value_type                       max_decomposition_cond_change_frac
  )
  :new_decomp_strategy_(new_decomp_strategy)
  ,max_decomposition_cond_change_frac_( max_decomposition_cond_change_frac )
  ,max_cond_( 0.01 / std::numeric_limits<value_type>::epsilon() )
{
  reset();
}

void CheckDecompositionFromPy_Step::reset() {
  beta_min_ = std::numeric_limits<value_type>::max();
}

// Overridden

bool CheckDecompositionFromPy_Step::do_step(
  Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss
  )
{
  NLPAlgo                &algo       = rsqp_algo(_algo);
  NLPAlgoState               &s          = algo.rsqp_state();
  const Range1D           equ_decomp  = s.equ_decomp();
  EJournalOutputLevel     olevel      = algo.algo_cntr().journal_output_level();
  std::ostream            &out        = algo.track().journal_out();

  // print step header.
  if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
    using IterationPack::print_algorithm_step;
    print_algorithm_step( algo, step_poss, type, assoc_step_poss, out );
  }

  bool select_new_decomposition = false;

  const value_type
    small_num = std::numeric_limits<value_type>::min(),
    beta = s.py().get_k(0).norm_inf() / (s.c().get_k(0).sub_view(equ_decomp)->norm_inf()+small_num);

  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
    out << "\nbeta = ||py||/||c|| = " << beta << std::endl;
  }

  // Check to see if a new basis was selected or not
  IterQuantityAccess<index_type>
    &num_basis_iq = s.num_basis();
  if( num_basis_iq.updated_k(0) ) {
    if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS )
      out << "\nnum_basis_k was updated so the basis changed so we will skip this check\n"
        << "    reset min ||py||/||c|| to current value + 1\n";
    beta_min_ = beta + 1.0;
    return true;
  }

  if( beta + 1.0 < beta_min_ ) {
    beta_min_ = beta + 1.0;
  }
  else {
    if( (beta + 1.0)/ beta_min_ > max_decomposition_cond_change_frac() ) {
      if( (int)olevel >= (int)PRINT_BASIC_ALGORITHM_INFO ) {
        out << "Select a new decomposition"
            << " (k = " << algo.state().k() << ")\n"
          << "beta_change = ( ||py||/||c|| = " << beta
            << " ) / ( min ||py||/||c|| = " << beta_min_ << " )\n"
          << "beta_change = " << (beta/beta_min_) << " > max_decomposition_cond_change_frac = "
            << max_decomposition_cond_change_frac() << std::endl;
      }
      select_new_decomposition = true;
    }
  }
  if( !select_new_decomposition && beta > max_cond() ) {
    if( (int)olevel >= (int)PRINT_BASIC_ALGORITHM_INFO ) {
      out << "\nConditioning of decomposition matrix is > " << beta
        << " > max_cond = " << max_cond() << std::endl
        << "Selecting a new decomposition ... "
        << " (k = " << algo.state().k() << ")\n";
    }
    select_new_decomposition = true;
  }

  if(select_new_decomposition) {
    reset();
    return new_decomp_strategy().new_decomposition(algo,step_poss,type,assoc_step_poss);
  }

  return true;    
}

void CheckDecompositionFromPy_Step::print_step(
  const Algorithm& algo, poss_type step_poss
  ,IterationPack::EDoStepType type, poss_type assoc_step_poss
  ,std::ostream& out, const std::string& L ) const
{
  out
    << L << "default: beta_min = inf\n"
    << L << "         max_decomposition_cond_change_frac = " << max_decomposition_cond_change_frac() << std::endl
    << L << "         max_cond = 0.01 * mach_eps\n"
    << L << "beta = norm_inf(py_k) / (norm_inf(c_k(equ_decomp))+small_number)\n"
    << L << "select_new_decomposition = false\n"
    << L << "if num_basis_k is updated then\n"
    << L << "  beta_min = beta + 1\n"
    << L << "end\n"
    << L << "if beta + 1 < beta_min then\n"
    << L << "  beta_min = beta + 1\n"
    << L << "else\n"
    << L << "  if (beta + 1) / beta_min > max_decomposition_cond_change_frac then\n"
    << L << "    select_new_decomposition = true\n"
    << L << "  end\n"
    << L << "end\n"
    << L << "if beta > max_cond then\n"
    << L << "  select_new_decomposition = true\n"
    << L << "end\n"
    << L << "if select_new_decomposition == true then\n"
    << L << "  new decomposition selection : " << typeName(new_decomp_strategy()) << std::endl
    ;
  new_decomp_strategy().print_new_decomposition(
    rsqp_algo(algo),step_poss,type,assoc_step_poss,out, L + "    " );
  out
    << L << "  end new decomposition selection\n"
    << L << "end\n"
    ;
}

} // end namespace MoochoPack