MoochoPack_LineSearchNLE_Step.cpp

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// Moocho: Multi-functional Object-Oriented arCHitecture for Optimization
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#include <ostream>
#include <typeinfo>

#include "MoochoPack_LineSearchNLE_Step.hpp"
#include "MoochoPack_Exceptions.hpp"
#include "MoochoPack_moocho_algo_conversion.hpp"
#include "IterationPack_print_algorithm_step.hpp"
#include "ConstrainedOptPack_MeritFuncNLESqrResid.hpp"
#include "ConstrainedOptPack_MeritFuncCalcNLE.hpp"
#include "ConstrainedOptPack_MeritFuncCalc1DQuadratic.hpp"
#include "AbstractLinAlgPack_VectorMutable.hpp"
#include "AbstractLinAlgPack_VectorStdOps.hpp"
#include "AbstractLinAlgPack_VectorOut.hpp"
#include "AbstractLinAlgPack_assert_print_nan_inf.hpp"
#include "AbstractLinAlgPack_LinAlgOpPack.hpp"
#include "Teuchos_Assert.hpp"

namespace MoochoPack {

LineSearchNLE_Step::LineSearchNLE_Step(
  const direct_line_search_ptr_t& direct_line_search
  )
  :direct_line_search_(direct_line_search)
{}

bool LineSearchNLE_Step::do_step(
  Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type
  ,poss_type assoc_step_poss
  )
{
  using AbstractLinAlgPack::Vp_StV;
  using LinAlgOpPack::V_VpV;

  NLPAlgo         &algo   = rsqp_algo(_algo);
  NLPAlgoState    &s      = algo.rsqp_state();
  NLP             &nlp    = algo.nlp();

  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 );
  }

  const size_type
    //n  = nlp.n(),
    m  = nlp.m();

  TEUCHOS_TEST_FOR_EXCEPTION( m == 0, std::logic_error, "LineSearchNLE_Step::do_step(...) : Error!" );

  // /////////////////////////////////////////
  // Set references to iteration quantities
  //
  // Set k+1 first then go back to get k+0 to ensure
  // we have backward storage!

  IterQuantityAccess<value_type>
    &alpha_iq = s.alpha();
  IterQuantityAccess<VectorMutable>
    &x_iq    = s.x(),
    &d_iq    = s.d(),
    &c_iq    = s.c();
  
  VectorMutable        &x_kp1   = x_iq.get_k(+1);
  const Vector         &x_k     = x_iq.get_k(0);
  VectorMutable        &c_kp1   = c_iq.get_k(+1);
  const Vector         &c_k     = c_iq.get_k(0);
  const Vector         &d_k     = d_iq.get_k(0);
  value_type           &alpha_k = alpha_iq.get_k(0);

  // //////////////////////////////////////
  // Build the merit function
  
  ConstrainedOptPack::MeritFuncNLESqrResid  phi_c;

  // /////////////////////////////////////
  // Compute Dphi_k, phi_kp1 and phi_k

  // phi_k, phi_kp1
  const value_type  phi_k   = phi_c.value(c_k);
  value_type        phi_kp1 = phi_c.value(c_kp1);
  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
    out << "\nBegin definition of NLP merit function phi_c.value(c(x)):\n";
    phi_c.print_merit_func( out, "    " );
    out << "end definition of the NLP merit funciton\n";
  }
  // Dphi_k
  phi_c.calc_deriv(c_k);
  const value_type
    Dphi_k  = phi_c.deriv();
  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
    out << "\nDphi_k = "  << Dphi_k << std::endl;
  }
  TEUCHOS_TEST_FOR_EXCEPTION(
    Dphi_k >= 0, LineSearchFailure
    ,"LineSearchNLE_Step::do_step(...) : " 
    "Error, d_k is not a descent direction for the merit function "
    "since Dphi_k = " << Dphi_k << " >= 0" );

  // //////////////////////
  // Do the line search
  
  nlp.unset_quantities();
  nlp.set_c( &c_kp1 );
  ConstrainedOptPack::MeritFuncCalcNLE    phi_c_calc( &phi_c, &nlp );
  const Vector*                           xd[2] = { &x_k, &d_k };
  MeritFuncCalc1DQuadratic                phi_calc_1d( phi_c_calc, 1, xd, &x_kp1 );
  
  if( !direct_line_search().do_line_search(
      phi_calc_1d, phi_k, &alpha_k, &phi_kp1
      ,( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS)
         ? &out : static_cast<std::ostream*>(0) )
      )
    )
  {
    // The line search failed!
    if( static_cast<int>(olevel) >= static_cast<int>(PRINT_BASIC_ALGORITHM_INFO) )
      out
        << "\nThe maximum number of linesearch iterations has been exceeded "
        << "(k = " << algo.state().k() << ")\n"
        << "(phi_k - phi_kp1)/phi_k = " << ((phi_k - phi_kp1)/phi_k)
        << "\nso we will reject the step and declare a line search failure.\n";
    TEUCHOS_TEST_FOR_EXCEPTION(
      true, LineSearchFailure
      ,"LineSearchNLE_Step::do_step(): Line search failure" );
  }

  nlp.unset_quantities();
  
  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
    out << "\nalpha_k                = " << alpha_k;
    out << "\n||x_kp1||inf           = " << x_kp1.norm_inf();
    out << "\n||c_kp1||inf           = " << c_kp1.norm_inf();
    out << "\nphi_kp1                = " << phi_kp1;
    out << std::endl;
  }
  
  if( (int)olevel >= (int)PRINT_VECTORS ) {
    out << "\nx_kp1 =\n" << x_kp1;
    out << "\nc_kp1 =\n" << c_kp1;
  }

  return true;
}

void LineSearchNLE_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 << "*** Preform a line search for c(x_k + alpha_k*d_k) along the full space search direction d_k.\n"
    << L << "ToDo: Fill this in!\n";
}

} // end namespace MoochoPack