ipopt-src 0.2.3+3.14.16

// Copyright (C) 2004, 2009 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// Authors:  Carl Laird, Andreas Waechter            IBM    2004-11-12

#include "IpQualityFunctionMuOracle.hpp"

#include <cmath>
#include <cstdio>

namespace Ipopt
{
#if IPOPT_VERBOSITY > 0
static const Index dbg_verbosity = 0;
#endif

QualityFunctionMuOracle::QualityFunctionMuOracle(
   const SmartPtr<PDSystemSolver>& pd_solver
)
   : MuOracle(),
     pd_solver_(pd_solver),

     tmp_step_x_L_(NULL),
     tmp_step_x_U_(NULL),
     tmp_step_s_L_(NULL),
     tmp_step_s_U_(NULL),
     tmp_step_z_L_(NULL),
     tmp_step_z_U_(NULL),
     tmp_step_v_L_(NULL),
     tmp_step_v_U_(NULL),

     tmp_slack_x_L_(NULL),
     tmp_slack_x_U_(NULL),
     tmp_slack_s_L_(NULL),
     tmp_slack_s_U_(NULL),
     tmp_z_L_(NULL),
     tmp_z_U_(NULL),
     tmp_v_L_(NULL),
     tmp_v_U_(NULL),

     count_qf_evals_(0)
{
   DBG_ASSERT(IsValid(pd_solver_));
}

QualityFunctionMuOracle::~QualityFunctionMuOracle()
{ }

void QualityFunctionMuOracle::RegisterOptions(
   SmartPtr<RegisteredOptions> roptions
)
{
   roptions->AddLowerBoundedNumberOption(
      "sigma_max",
      "Maximum value of the centering parameter.",
      0., true,
      1e2,
      "This is the upper bound for the centering parameter chosen by the quality function based barrier parameter update. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddLowerBoundedNumberOption(
      "sigma_min",
      "Minimum value of the centering parameter.",
      0., false,
      1e-6,
      "This is the lower bound for the centering parameter chosen by the quality function based barrier parameter update. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddStringOption4(
      "quality_function_norm_type",
      "Norm used for components of the quality function.",
      "2-norm-squared",
      "1-norm", "use the 1-norm (abs sum)",
      "2-norm-squared", "use the 2-norm squared (sum of squares)",
      "max-norm", "use the infinity norm (max)",
      "2-norm", "use 2-norm",
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddStringOption4(
      "quality_function_centrality",
      "The penalty term for centrality that is included in quality function.",
      "none",
      "none", "no penalty term is added",
      "log", "complementarity * the log of the centrality measure",
      "reciprocal", "complementarity * the reciprocal of the centrality measure",
      "cubed-reciprocal", "complementarity * the reciprocal of the centrality measure cubed",
      "This determines whether a term is added to the quality function to penalize deviation from centrality with respect to complementarity. "
      "The complementarity measure here is the xi in the Loqo update rule. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddStringOption2(
      "quality_function_balancing_term",
      "The balancing term included in the quality function for centrality.",
      "none",
      "none", "no balancing term is added",
      "cubic", "Max(0,Max(dual_inf,primal_inf)-compl)^3",
      "This determines whether a term is added to the quality function that penalizes situations "
      "where the complementarity is much smaller than dual and primal infeasibilities. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddLowerBoundedIntegerOption(
      "quality_function_max_section_steps",
      "Maximum number of search steps during direct search procedure determining the optimal centering parameter.",
      0,
      8,
      "The golden section search is performed for the quality function based mu oracle. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".");
   roptions->AddBoundedNumberOption(
      "quality_function_section_sigma_tol",
      "Tolerance for the section search procedure determining the optimal centering parameter (in sigma space).",
      0., false,
      1., true,
      1e-2,
      "The golden section search is performed for the quality function based mu oracle. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
   roptions->AddBoundedNumberOption(
      "quality_function_section_qf_tol",
      "Tolerance for the golden section search procedure determining the optimal centering parameter (in the function value space).",
      0., false,
      1., true,
      0.,
      "The golden section search is performed for the quality function based mu oracle. "
      "Only used if option \"mu_oracle\" is set to \"quality-function\".",
      true);
}

bool QualityFunctionMuOracle::InitializeImpl(
   const OptionsList& options,
   const std::string& prefix
)
{
   Index enum_int;

   options.GetNumericValue("sigma_max", sigma_max_, prefix);
   options.GetNumericValue("sigma_min", sigma_min_, prefix);

   options.GetEnumValue("quality_function_norm_type", enum_int, prefix);
   quality_function_norm_ = NormEnum(enum_int);
   options.GetEnumValue("quality_function_centrality", enum_int, prefix);
   quality_function_centrality_ = CentralityEnum(enum_int);
   options.GetEnumValue("quality_function_balancing_term", enum_int, prefix);
   quality_function_balancing_term_ = BalancingTermEnum(enum_int);
   options.GetIntegerValue("quality_function_max_section_steps", quality_function_max_section_steps_, prefix);
   options.GetNumericValue("quality_function_section_sigma_tol", quality_function_section_sigma_tol_, prefix);
   options.GetNumericValue("quality_function_section_qf_tol", quality_function_section_qf_tol_, prefix);

   initialized_ = false;

   return true;
}

bool QualityFunctionMuOracle::CalculateMu(
   Number  mu_min,
   Number  mu_max,
   Number& new_mu
)
{
   DBG_START_METH("QualityFunctionMuOracle::CalculateMu",
                  dbg_verbosity);

   ///////////////////////////////////////////////////////////////////////////
   // Reserve memory for temporary vectors used in CalculateQualityFunction //
   ///////////////////////////////////////////////////////////////////////////

   tmp_step_x_L_ = IpNLP().x_L()->MakeNew();
   tmp_step_x_U_ = IpNLP().x_U()->MakeNew();
   tmp_step_s_L_ = IpNLP().d_L()->MakeNew();
   tmp_step_s_U_ = IpNLP().d_U()->MakeNew();
   tmp_step_z_L_ = IpNLP().x_L()->MakeNew();
   tmp_step_z_U_ = IpNLP().x_U()->MakeNew();
   tmp_step_v_L_ = IpNLP().d_L()->MakeNew();
   tmp_step_v_U_ = IpNLP().d_U()->MakeNew();

   tmp_slack_x_L_ = IpNLP().x_L()->MakeNew();
   tmp_slack_x_U_ = IpNLP().x_U()->MakeNew();
   tmp_slack_s_L_ = IpNLP().d_L()->MakeNew();
   tmp_slack_s_U_ = IpNLP().d_U()->MakeNew();
   tmp_z_L_ = IpNLP().x_L()->MakeNew();
   tmp_z_U_ = IpNLP().x_U()->MakeNew();
   tmp_v_L_ = IpNLP().d_L()->MakeNew();
   tmp_v_U_ = IpNLP().d_U()->MakeNew();

   /////////////////////////////////////
   // Compute the affine scaling step //
   /////////////////////////////////////

   Jnlst().Printf(J_DETAILED, J_BARRIER_UPDATE,
                  "Solving the Primal Dual System for the affine step\n");
   // First get the right hand side
   SmartPtr<IteratesVector> rhs_aff = IpData().curr()->MakeNewIteratesVector(false);
   rhs_aff->Set_x(*IpCq().curr_grad_lag_x());
   rhs_aff->Set_s(*IpCq().curr_grad_lag_s());
   rhs_aff->Set_y_c(*IpCq().curr_c());
   rhs_aff->Set_y_d(*IpCq().curr_d_minus_s());
   rhs_aff->Set_z_L(*IpCq().curr_compl_x_L());
   rhs_aff->Set_z_U(*IpCq().curr_compl_x_U());
   rhs_aff->Set_v_L(*IpCq().curr_compl_s_L());
   rhs_aff->Set_v_U(*IpCq().curr_compl_s_U());

   // Get space for the affine scaling step
   SmartPtr<IteratesVector> step_aff = IpData().curr()->MakeNewIteratesVector(true);

   // Now solve the primal-dual system to get the step.  We allow a
   // somewhat inexact solution, iterative refinement will be done
   // after mu is known
   bool allow_inexact = true;
   bool retval = pd_solver_->Solve(-1.0, 0.0, *rhs_aff, *step_aff, allow_inexact);
   if( !retval )
   {
      Jnlst().Printf(J_DETAILED, J_BARRIER_UPDATE,
                     "The linear system could not be solved for the affine step!\n");
      return false;
   }

   DBG_PRINT_VECTOR(2, "step_aff", *step_aff);

   /////////////////////////////////////
   // Compute the pure centering step //
   /////////////////////////////////////

   Number avrg_compl = IpCq().curr_avrg_compl();

   Jnlst().Printf(J_DETAILED, J_BARRIER_UPDATE,
                  "Solving the Primal Dual System for the centering step\n");
   // First get the right hand side
   SmartPtr<IteratesVector> rhs_cen = IpData().curr()->MakeNewIteratesVector(true);
   rhs_cen->x_NonConst()->AddOneVector(-avrg_compl, *IpCq().grad_kappa_times_damping_x(), 0.);
   rhs_cen->s_NonConst()->AddOneVector(-avrg_compl, *IpCq().grad_kappa_times_damping_s(), 0.);

   rhs_cen->y_c_NonConst()->Set(0.);
   rhs_cen->y_d_NonConst()->Set(0.);
   rhs_cen->z_L_NonConst()->Set(avrg_compl);
   rhs_cen->z_U_NonConst()->Set(avrg_compl);
   rhs_cen->v_L_NonConst()->Set(avrg_compl);
   rhs_cen->v_U_NonConst()->Set(avrg_compl);

   // Get space for the centering step
   SmartPtr<IteratesVector> step_cen = IpData().curr()->MakeNewIteratesVector(true);

   // Now solve the primal-dual system to get the step
   allow_inexact = true;
   retval = pd_solver_->Solve(1.0, 0.0, *rhs_cen, *step_cen, allow_inexact);
   if( !retval )
   {
      Jnlst().Printf(J_DETAILED, J_BARRIER_UPDATE,
                     "The linear system could not be solved for the centering step!\n");
      return false;
   }

   DBG_PRINT_VECTOR(2, "step_cen", *step_cen);

   // Start the timing for the quality function search here
   IpData().TimingStats().QualityFunctionSearch().Start();

   // Some initializations
   if( !initialized_ )
   {
      n_dual_ = IpData().curr()->x()->Dim() + IpData().curr()->s()->Dim();
      n_pri_ = IpData().curr()->y_c()->Dim() + IpData().curr()->y_d()->Dim();
      n_comp_ = IpData().curr()->z_L()->Dim() + IpData().curr()->z_U()->Dim() + IpData().curr()->v_L()->Dim()
                + IpData().curr()->v_U()->Dim();
      initialized_ = true;
   }

   count_qf_evals_ = 0;

   // Compute some quantities used for the quality function evaluations
   // (This way we try to avoid retrieving numbers from cache...

   curr_slack_x_L_ = IpCq().curr_slack_x_L();
   curr_slack_x_U_ = IpCq().curr_slack_x_U();
   curr_slack_s_L_ = IpCq().curr_slack_s_L();
   curr_slack_s_U_ = IpCq().curr_slack_s_U();

   curr_z_L_ = IpData().curr()->z_L();
   curr_z_U_ = IpData().curr()->z_U();
   curr_v_L_ = IpData().curr()->v_L();
   curr_v_U_ = IpData().curr()->v_U();

   IpData().TimingStats().Task5().Start();
   switch( quality_function_norm_ )
   {
      case NM_NORM_1:
         curr_grad_lag_x_asum_ = IpCq().curr_grad_lag_x()->Asum();
         curr_grad_lag_s_asum_ = IpCq().curr_grad_lag_s()->Asum();
         curr_c_asum_ = IpCq().curr_c()->Asum();
         curr_d_minus_s_asum_ = IpCq().curr_d_minus_s()->Asum();
         break;
      case NM_NORM_2_SQUARED:
      case NM_NORM_2:
         curr_grad_lag_x_nrm2_ = IpCq().curr_grad_lag_x()->Nrm2();
         curr_grad_lag_s_nrm2_ = IpCq().curr_grad_lag_s()->Nrm2();
         curr_c_nrm2_ = IpCq().curr_c()->Nrm2();
         curr_d_minus_s_nrm2_ = IpCq().curr_d_minus_s()->Nrm2();
         break;
      case NM_NORM_MAX:
         curr_grad_lag_x_amax_ = IpCq().curr_grad_lag_x()->Amax();
         curr_grad_lag_s_amax_ = IpCq().curr_grad_lag_s()->Amax();
         curr_c_amax_ = IpCq().curr_c()->Amax();
         curr_d_minus_s_amax_ = IpCq().curr_d_minus_s()->Amax();
         break;
      default:
         DBG_ASSERT(false && "Unknown value for quality_function_norm_");
   }
   IpData().TimingStats().Task5().End();

   // We now compute the step for the slack variables.  This safes
   // time, because we then don't have to do this any more for each
   // evaluation of the quality function
   SmartPtr<Vector> step_aff_x_L = step_aff->z_L()->MakeNew();
   SmartPtr<Vector> step_aff_x_U = step_aff->z_U()->MakeNew();
   SmartPtr<Vector> step_aff_s_L = step_aff->v_L()->MakeNew();
   SmartPtr<Vector> step_aff_s_U = step_aff->v_U()->MakeNew();
   IpNLP().Px_L()->TransMultVector(1., *step_aff->x(), 0., *step_aff_x_L);
   IpNLP().Px_U()->TransMultVector(-1., *step_aff->x(), 0., *step_aff_x_U);
   IpNLP().Pd_L()->TransMultVector(1., *step_aff->s(), 0., *step_aff_s_L);
   IpNLP().Pd_U()->TransMultVector(-1., *step_aff->s(), 0., *step_aff_s_U);
   SmartPtr<Vector> step_cen_x_L = step_cen->z_L()->MakeNew();
   SmartPtr<Vector> step_cen_x_U = step_cen->z_U()->MakeNew();
   SmartPtr<Vector> step_cen_s_L = step_cen->v_L()->MakeNew();
   SmartPtr<Vector> step_cen_s_U = step_cen->v_U()->MakeNew();
   IpNLP().Px_L()->TransMultVector(1., *step_cen->x(), 0., *step_cen_x_L);
   IpNLP().Px_U()->TransMultVector(-1., *step_cen->x(), 0., *step_cen_x_U);
   IpNLP().Pd_L()->TransMultVector(1., *step_cen->s(), 0., *step_cen_s_L);
   IpNLP().Pd_U()->TransMultVector(-1., *step_cen->s(), 0., *step_cen_s_U);

   Number sigma;

   // First we determine whether we want to search for a value of
   // sigma larger or smaller than 1.  For this, we estimate the
   // slope of the quality function at sigma=1.
   Number qf_1 = CalculateQualityFunction(1., *step_aff_x_L, *step_aff_x_U, *step_aff_s_L, *step_aff_s_U,
                                          *step_aff->y_c(), *step_aff->y_d(), *step_aff->z_L(), *step_aff->z_U(), *step_aff->v_L(), *step_aff->v_U(),
                                          *step_cen_x_L, *step_cen_x_U, *step_cen_s_L, *step_cen_s_U, *step_cen->y_c(), *step_cen->y_d(), *step_cen->z_L(),
                                          *step_cen->z_U(), *step_cen->v_L(), *step_cen->v_U());

   Number sigma_1minus = 1. - Max(Number(1e-4), quality_function_section_sigma_tol_);
   Number qf_1minus = CalculateQualityFunction(sigma_1minus, *step_aff_x_L, *step_aff_x_U, *step_aff_s_L, *step_aff_s_U,
                      *step_aff->y_c(), *step_aff->y_d(), *step_aff->z_L(), *step_aff->z_U(), *step_aff->v_L(), *step_aff->v_U(),
                      *step_cen_x_L, *step_cen_x_U, *step_cen_s_L, *step_cen_s_U, *step_cen->y_c(), *step_cen->y_d(), *step_cen->z_L(),
                      *step_cen->z_U(), *step_cen->v_L(), *step_cen->v_U());

   if( qf_1minus > qf_1 )
   {
      // It seems that the quality function decreases for values
      // larger than sigma, so perform golden section search for sigma
      // > 1.
      Number sigma_up = Min(sigma_max_, mu_max / avrg_compl);
      Number sigma_lo = 1.;
      if( sigma_lo >= sigma_up )
      {
         sigma = sigma_up;
      }
      else
      {
         // ToDo maybe we should use different tolerances for sigma>1
         sigma = PerformGoldenSection(sigma_up, -100., sigma_lo, qf_1, quality_function_section_sigma_tol_,
                                      quality_function_section_qf_tol_, *step_aff_x_L, *step_aff_x_U, *step_aff_s_L, *step_aff_s_U,
                                      *step_aff->y_c(), *step_aff->y_d(), *step_aff->z_L(), *step_aff->z_U(), *step_aff->v_L(), *step_aff->v_U(),
                                      *step_cen_x_L, *step_cen_x_U, *step_cen_s_L, *step_cen_s_U, *step_cen->y_c(), *step_cen->y_d(),
                                      *step_cen->z_L(), *step_cen->z_U(), *step_cen->v_L(), *step_cen->v_U());
      }
   }
   else
   {
      // Search for sigma less than 1

      Number sigma_lo = Max(sigma_min_, mu_min / avrg_compl);
      Number sigma_up = Min(Max(sigma_lo, sigma_1minus), mu_max / avrg_compl);
      if( sigma_lo >= sigma_up )
      {
         // Skip the search, we are already at the minimum
         sigma = sigma_lo;
      }
      else
      {
         sigma = PerformGoldenSection(sigma_up, qf_1minus, sigma_lo, -100., quality_function_section_sigma_tol_,
                                      quality_function_section_qf_tol_, *step_aff_x_L, *step_aff_x_U, *step_aff_s_L, *step_aff_s_U,
                                      *step_aff->y_c(), *step_aff->y_d(), *step_aff->z_L(), *step_aff->z_U(), *step_aff->v_L(), *step_aff->v_U(),
                                      *step_cen_x_L, *step_cen_x_U, *step_cen_s_L, *step_cen_s_U, *step_cen->y_c(), *step_cen->y_d(),
                                      *step_cen->z_L(), *step_cen->z_U(), *step_cen->v_L(), *step_cen->v_U());
      }
   }

#ifdef TRACEQUALITYFUNCTION
   char fname[100];
   Snprintf(fname, 100, "qf_values_%" IPOPT_INDEX_FORMAT ".dat", IpData().iter_count());
   FILE* fid = fopen(fname, "w");

   Number sigma_1 = sigma_max_;
   Number sigma_2 = 1e-9 / avrg_compl;
   Number sigma_trace = sigma_1;
   while (sigma_trace > sigma_2)
   {
      Number qf = CalculateQualityFunction(sigma_trace,
                                           *step_aff_x_L,
                                           *step_aff_x_U,
                                           *step_aff_s_L,
                                           *step_aff_s_U,
                                           *step_aff->y_c(),
                                           *step_aff->y_d(),
                                           *step_aff->z_L(),
                                           *step_aff->z_U(),
                                           *step_aff->v_L(),
                                           *step_aff->v_U(),
                                           *step_cen_x_L,
                                           *step_cen_x_U,
                                           *step_cen_s_L,
                                           *step_cen_s_U,
                                           *step_cen->y_c(),
                                           *step_cen->y_d(),
                                           *step_cen->z_L(),
                                           *step_cen->z_U(),
                                           *step_cen->v_L(),
                                           *step_cen->v_U());
      fprintf(fid, "%9.2e %25.16e\n", sigma_trace, qf);
      sigma_trace /= 1.1;
   }
   fclose(fid);
#endif

   // End timing of quality function search
   IpData().TimingStats().QualityFunctionSearch().End();

   Jnlst().Printf(J_DETAILED, J_BARRIER_UPDATE,
                  "Sigma = %e\n", sigma);
   Number mu = sigma * avrg_compl;

   // Store the affine search direction (in case it is needed in the
   // line search for a corrector step)
   IpData().set_delta_aff(step_aff);
   IpData().SetHaveAffineDeltas(true);

   // Now construct the overall search direction here
   SmartPtr<IteratesVector> step = IpData().curr()->MakeNewIteratesVector(true);
   step->AddTwoVectors(sigma, *step_cen, 1.0, *IpData().delta_aff(), 0.0);

   DBG_PRINT_VECTOR(2, "step", *step);
   IpData().set_delta(step);
   IpData().SetHaveDeltas(true);

   ///////////////////////////////////////////////////////////////////////////
   // Release memory for temporary vectors used in CalculateQualityFunction //
   ///////////////////////////////////////////////////////////////////////////

   tmp_step_x_L_ = NULL;
   tmp_step_x_U_ = NULL;
   tmp_step_s_L_ = NULL;
   tmp_step_s_U_ = NULL;
   tmp_step_z_L_ = NULL;
   tmp_step_z_U_ = NULL;
   tmp_step_v_L_ = NULL;
   tmp_step_v_U_ = NULL;

   tmp_slack_x_L_ = NULL;
   tmp_slack_x_U_ = NULL;
   tmp_slack_s_L_ = NULL;
   tmp_slack_s_U_ = NULL;
   tmp_z_L_ = NULL;
   tmp_z_U_ = NULL;
   tmp_v_L_ = NULL;
   tmp_v_U_ = NULL;

   curr_slack_x_L_ = NULL;
   curr_slack_x_U_ = NULL;
   curr_slack_s_L_ = NULL;
   curr_slack_s_U_ = NULL;

   /*
   char ssigma[40];
   Snprintf(ssigma, 39, " sigma=%8.2e", sigma);
   IpData().Append_info_string(ssigma);
   Snprintf(ssigma, 39, " qf=%" IPOPT_INDEX_FORMAT "", count_qf_evals_);
   IpData().Append_info_string(ssigma);
   Snprintf(ssigma, 39, " xi=%8.2e ", IpCq().curr_centrality_measure());
   IpData().Append_info_string(ssigma);
   if( sigma > 1. )
      IpData().Append_info_string("LARGESIGMA");
   */

   new_mu = mu;
   return true;
}

Number QualityFunctionMuOracle::CalculateQualityFunction(
   Number        sigma,
   const Vector& step_aff_x_L,
   const Vector& step_aff_x_U,
   const Vector& step_aff_s_L,
   const Vector& step_aff_s_U,
   const Vector& /*step_aff_y_c*/,
   const Vector& /*step_aff_y_d*/,
   const Vector& step_aff_z_L,
   const Vector& step_aff_z_U,
   const Vector& step_aff_v_L,
   const Vector& step_aff_v_U,
   const Vector& step_cen_x_L,
   const Vector& step_cen_x_U,
   const Vector& step_cen_s_L,
   const Vector& step_cen_s_U,
   const Vector& /*step_cen_y_c*/,
   const Vector& /*step_cen_y_d*/,
   const Vector& step_cen_z_L,
   const Vector& step_cen_z_U,
   const Vector& step_cen_v_L,
   const Vector& step_cen_v_U
)
{
   DBG_START_METH("QualityFunctionMuOracle::CalculateQualityFunction",
                  dbg_verbosity);
   count_qf_evals_++;

   IpData().TimingStats().Task1().Start();
   tmp_step_x_L_->AddTwoVectors(1., step_aff_x_L, sigma, step_cen_x_L, 0.);
   tmp_step_x_U_->AddTwoVectors(1., step_aff_x_U, sigma, step_cen_x_U, 0.);
   tmp_step_s_L_->AddTwoVectors(1., step_aff_s_L, sigma, step_cen_s_L, 0.);
   tmp_step_s_U_->AddTwoVectors(1., step_aff_s_U, sigma, step_cen_s_U, 0.);
   tmp_step_z_L_->AddTwoVectors(1., step_aff_z_L, sigma, step_cen_z_L, 0.);
   tmp_step_z_U_->AddTwoVectors(1., step_aff_z_U, sigma, step_cen_z_U, 0.);
   tmp_step_v_L_->AddTwoVectors(1., step_aff_v_L, sigma, step_cen_v_L, 0.);
   tmp_step_v_U_->AddTwoVectors(1., step_aff_v_U, sigma, step_cen_v_U, 0.);
   IpData().TimingStats().Task1().End();

   // Compute the fraction-to-the-boundary step sizes
   IpData().TimingStats().Task2().Start();
   Number tau = IpData().curr_tau();
   Number alpha_primal = IpCq().uncached_slack_frac_to_the_bound(tau, *tmp_step_x_L_, *tmp_step_x_U_, *tmp_step_s_L_,
                         *tmp_step_s_U_);

   Number alpha_dual = IpCq().uncached_dual_frac_to_the_bound(tau, *tmp_step_z_L_, *tmp_step_z_U_, *tmp_step_v_L_,
                       *tmp_step_v_U_);
   IpData().TimingStats().Task2().End();

   Number xi = 0.; // centrality measure

   IpData().TimingStats().Task1().Start();
   tmp_slack_x_L_->AddTwoVectors(1., *curr_slack_x_L_, alpha_primal, *tmp_step_x_L_, 0.);
   tmp_slack_x_U_->AddTwoVectors(1., *curr_slack_x_U_, alpha_primal, *tmp_step_x_U_, 0.);
   tmp_slack_s_L_->AddTwoVectors(1., *curr_slack_s_L_, alpha_primal, *tmp_step_s_L_, 0.);
   tmp_slack_s_U_->AddTwoVectors(1., *curr_slack_s_U_, alpha_primal, *tmp_step_s_U_, 0.);

   tmp_z_L_->AddTwoVectors(1., *curr_z_L_, alpha_dual, *tmp_step_z_L_, 0.);
   tmp_z_U_->AddTwoVectors(1., *curr_z_U_, alpha_dual, *tmp_step_z_U_, 0.);
   tmp_v_L_->AddTwoVectors(1., *curr_v_L_, alpha_dual, *tmp_step_v_L_, 0.);
   tmp_v_U_->AddTwoVectors(1., *curr_v_U_, alpha_dual, *tmp_step_v_U_, 0.);
   IpData().TimingStats().Task1().End();

   IpData().TimingStats().Task3().Start();
   tmp_slack_x_L_->ElementWiseMultiply(*tmp_z_L_);
   tmp_slack_x_U_->ElementWiseMultiply(*tmp_z_U_);
   tmp_slack_s_L_->ElementWiseMultiply(*tmp_v_L_);
   tmp_slack_s_U_->ElementWiseMultiply(*tmp_v_U_);
   IpData().TimingStats().Task3().End();

   DBG_PRINT_VECTOR(2, "compl_x_L", *tmp_slack_x_L_);
   DBG_PRINT_VECTOR(2, "compl_x_U", *tmp_slack_x_U_);
   DBG_PRINT_VECTOR(2, "compl_s_L", *tmp_slack_s_L_);
   DBG_PRINT_VECTOR(2, "compl_s_U", *tmp_slack_s_U_);

   Number dual_inf = -1.;
   Number primal_inf = -1.;
   Number compl_inf = -1.;

   IpData().TimingStats().Task5().Start();
   switch( quality_function_norm_ )
   {
      case NM_NORM_1:
         dual_inf = (1. - alpha_dual) * (curr_grad_lag_x_asum_ + curr_grad_lag_s_asum_);

         primal_inf = (1. - alpha_primal) * (curr_c_asum_ + curr_d_minus_s_asum_);

         compl_inf = tmp_slack_x_L_->Asum() + tmp_slack_x_U_->Asum() + tmp_slack_s_L_->Asum() + tmp_slack_s_U_->Asum();

         dual_inf /= n_dual_;
         if( n_pri_ > 0 )
         {
            primal_inf /= n_pri_;
         }
         DBG_ASSERT(n_comp_ > 0);
         compl_inf /= n_comp_;
         break;
      case NM_NORM_2_SQUARED:
         dual_inf = std::pow(1. - alpha_dual, 2) * (std::pow(curr_grad_lag_x_nrm2_, 2) + std::pow(curr_grad_lag_s_nrm2_, 2));
         primal_inf = std::pow(1. - alpha_primal, 2) * (std::pow(curr_c_nrm2_, 2) + std::pow(curr_d_minus_s_nrm2_, 2));
         compl_inf = std::pow(tmp_slack_x_L_->Nrm2(), 2) + std::pow(tmp_slack_x_U_->Nrm2(), 2) + std::pow(tmp_slack_s_L_->Nrm2(), 2)
                     + std::pow(tmp_slack_s_U_->Nrm2(), 2);
         dual_inf /= n_dual_;
         if( n_pri_ > 0 )
         {
            primal_inf /= n_pri_;
         }
         DBG_ASSERT(n_comp_ > 0);
         compl_inf /= n_comp_;
         break;
      case NM_NORM_MAX:
         dual_inf = (1. - alpha_dual) * Max(curr_grad_lag_x_amax_, curr_grad_lag_s_amax_);
         primal_inf = (1. - alpha_primal) * Max(curr_c_amax_, curr_d_minus_s_amax_);
         compl_inf = Max(tmp_slack_x_L_->Amax(), tmp_slack_x_U_->Amax(), tmp_slack_s_L_->Amax(),
                         tmp_slack_s_U_->Amax());
         break;
      case NM_NORM_2:
         dual_inf = (1. - alpha_dual) * std::sqrt(std::pow(curr_grad_lag_x_nrm2_, 2) + std::pow(curr_grad_lag_s_nrm2_, 2));
         primal_inf = (1. - alpha_primal) * std::sqrt(std::pow(curr_c_nrm2_, 2) + std::pow(curr_d_minus_s_nrm2_, 2));
         compl_inf = std::sqrt(
                        std::pow(tmp_slack_x_L_->Nrm2(), 2) + std::pow(tmp_slack_x_U_->Nrm2(), 2) + std::pow(tmp_slack_s_L_->Nrm2(), 2)
                        + std::pow(tmp_slack_s_U_->Nrm2(), 2));
         dual_inf /= std::sqrt((Number) n_dual_);
         if( n_pri_ > 0 )
         {
            primal_inf /= std::sqrt((Number) n_pri_);
         }
         DBG_ASSERT(n_comp_ > 0);
         compl_inf /= std::sqrt((Number) n_comp_);
         break;
      default:
         DBG_ASSERT(false && "Unknown value for quality_function_norm_");
   }
   IpData().TimingStats().Task5().End();

   Number quality_function = dual_inf + primal_inf + compl_inf;

   if( quality_function_centrality_ != CEN_NONE )
   {
      IpData().TimingStats().Task4().Start();
      xi = IpCq().CalcCentralityMeasure(*tmp_slack_x_L_, *tmp_slack_x_U_, *tmp_slack_s_L_, *tmp_slack_s_U_);
      IpData().TimingStats().Task4().End();
   }
   switch( quality_function_centrality_ )
   {
      case CEN_NONE:
         //Nothing
         break;
      case CEN_LOG:
         quality_function -= compl_inf * std::log(xi);
         break;
      case CEN_RECIPROCAL:
         quality_function += compl_inf / xi;
         break;
      case CEN_CUBED_RECIPROCAL:
         quality_function += compl_inf / std::pow(xi, 3);
         break;
      default:
         DBG_ASSERT(false && "Unknown value for quality_function_centrality_");
   }

   switch( quality_function_balancing_term_ )
   {
      case BT_NONE:
         //Nothing
         break;
      case BT_CUBIC:
         quality_function += std::pow(Max(Number(0.), Max(dual_inf, primal_inf) - compl_inf), 3);
         break;
      default:
         DBG_ASSERT(false && "Unknown value for quality_function_balancing term_");
   }

   Jnlst().Printf(J_MOREDETAILED, J_BARRIER_UPDATE,
                  "sigma = %8.2e d_inf = %18.12e p_inf = %18.12e cmpl = %18.12e q = %18.12e a_pri = %8.2e a_dual = %8.2e xi = %8.2e\n",
                  sigma, dual_inf, primal_inf, compl_inf, quality_function, alpha_primal, alpha_dual, xi);

   return quality_function;
   //return compl_inf;
}

Number QualityFunctionMuOracle::PerformGoldenSection(
   Number        sigma_up_in,
   Number        q_up,
   Number        sigma_lo_in,
   Number        q_lo,
   Number        sigma_tol,
   Number        qf_tol,
   const Vector& step_aff_x_L,
   const Vector& step_aff_x_U,
   const Vector& step_aff_s_L,
   const Vector& step_aff_s_U,
   const Vector& step_aff_y_c,
   const Vector& step_aff_y_d,
   const Vector& step_aff_z_L,
   const Vector& step_aff_z_U,
   const Vector& step_aff_v_L,
   const Vector& step_aff_v_U,
   const Vector& step_cen_x_L,
   const Vector& step_cen_x_U,
   const Vector& step_cen_s_L,
   const Vector& step_cen_s_U,
   const Vector& step_cen_y_c,
   const Vector& step_cen_y_d,
   const Vector& step_cen_z_L,
   const Vector& step_cen_z_U,
   const Vector& step_cen_v_L,
   const Vector& step_cen_v_U
)
{
   Number sigma_up = ScaleSigma(sigma_up_in);
   Number sigma_lo = ScaleSigma(sigma_lo_in);

   Number sigma;
   Number gfac = (3. - std::sqrt(5.)) / 2.;
   Number sigma_mid1 = sigma_lo + gfac * (sigma_up - sigma_lo);
   Number sigma_mid2 = sigma_lo + (1. - gfac) * (sigma_up - sigma_lo);

   Number qmid1 = CalculateQualityFunction(UnscaleSigma(sigma_mid1), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                           step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U, step_cen_x_L,
                                           step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L, step_cen_z_U, step_cen_v_L,
                                           step_cen_v_U);
   Number qmid2 = CalculateQualityFunction(UnscaleSigma(sigma_mid2), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                           step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U, step_cen_x_L,
                                           step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L, step_cen_z_U, step_cen_v_L,
                                           step_cen_v_U);

   Index nsections = 0;
   while( (sigma_up - sigma_lo) >= sigma_tol * sigma_up
          &&
          //while ((sigma_up-sigma_lo)>=sigma_tol &&  // Note we are using the non-relative criterion here for sigma
          (1. - Min(q_lo, q_up, qmid1, qmid2) / Max(q_lo, q_up, qmid1, qmid2)) >= qf_tol
          && nsections < quality_function_max_section_steps_ )
   {
      nsections++;
      //printf("sigma_lo=%e sigma_mid1=%e sigma_mid2=%e sigma_up=%e\n",sigma_lo, sigma_mid1, sigma_mid2, sigma_up);
      if( qmid1 > qmid2 )
      {
         sigma_lo = sigma_mid1;
         q_lo = qmid1;
         sigma_mid1 = sigma_mid2;
         qmid1 = qmid2;
         sigma_mid2 = sigma_lo + (1. - gfac) * (sigma_up - sigma_lo);
         qmid2 = CalculateQualityFunction(UnscaleSigma(sigma_mid2), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                          step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U,
                                          step_cen_x_L, step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L,
                                          step_cen_z_U, step_cen_v_L, step_cen_v_U);
      }
      else
      {
         sigma_up = sigma_mid2;
         q_up = qmid2;
         sigma_mid2 = sigma_mid1;
         qmid2 = qmid1;
         sigma_mid1 = sigma_lo + gfac * (sigma_up - sigma_lo);
         qmid1 = CalculateQualityFunction(UnscaleSigma(sigma_mid1), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                          step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U,
                                          step_cen_x_L, step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L,
                                          step_cen_z_U, step_cen_v_L, step_cen_v_U);
      }
   }

   if( (sigma_up - sigma_lo) >= sigma_tol * sigma_up
       && (1. - Min(q_lo, q_up, qmid1, qmid2) / Max(q_lo, q_up, qmid1, qmid2)) < qf_tol )
   {
      // The qf tolerance make it stop
      IpData().Append_info_string("qf_tol ");
      Number qf_min = Min(q_lo, q_up, qmid1, qmid2);
      DBG_ASSERT(qf_min > -100.);
      if( qf_min == q_lo )
      {
         sigma = sigma_lo;
      }
      else if( qf_min == qmid1 )
      {
         sigma = sigma_mid1;
      }
      else if( qf_min == qmid2 )
      {
         sigma = sigma_mid2;
      }
      else
      {
         sigma = sigma_up;
      }
   }
   else
   {
      Number q;
      if( qmid1 < qmid2 )
      {
         sigma = sigma_mid1;
         q = qmid1;
      }
      else
      {
         sigma = sigma_mid2;
         q = qmid2;
      }
      if( sigma_up == ScaleSigma(sigma_up_in) )
      {
         Number qtmp;
         if( q_up < 0. )
         {
            qtmp = CalculateQualityFunction(UnscaleSigma(sigma_up), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                            step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U,
                                            step_cen_x_L, step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L,
                                            step_cen_z_U, step_cen_v_L, step_cen_v_U);
         }
         else
         {
            qtmp = q_up;
         }
         if( qtmp < q )
         {
            sigma = sigma_up;
            // never used: q = qtmp;
         }
      }
      else if( sigma_lo == ScaleSigma(sigma_lo_in) )
      {
         Number qtmp;
         if( q_lo < 0. )
         {
            qtmp = CalculateQualityFunction(UnscaleSigma(sigma_lo), step_aff_x_L, step_aff_x_U, step_aff_s_L,
                                            step_aff_s_U, step_aff_y_c, step_aff_y_d, step_aff_z_L, step_aff_z_U, step_aff_v_L, step_aff_v_U,
                                            step_cen_x_L, step_cen_x_U, step_cen_s_L, step_cen_s_U, step_cen_y_c, step_cen_y_d, step_cen_z_L,
                                            step_cen_z_U, step_cen_v_L, step_cen_v_U);
         }
         else
         {
            qtmp = q_lo;
         }
         if( qtmp < q )
         {
            sigma = sigma_lo;
            // never used: q = qtmp;
         }
      }
   }

   return UnscaleSigma(sigma);
}

/*
 Number QualityFunctionMuOracle::ScaleSigma(Number sigma) {return std::log(sigma);}
 Number QualityFunctionMuOracle::UnscaleSigma(Number scaled_sigma) {return std::exp(scaled_sigma);}
 */

Number QualityFunctionMuOracle::ScaleSigma(
   Number sigma
)
{
   return sigma;
}

Number QualityFunctionMuOracle::UnscaleSigma(
   Number scaled_sigma
)
{
   return scaled_sigma;
}

/* AW: Tried search in the log space, but that was even worse than
 search in unscaled space */
/*
 Number
 QualityFunctionMuOracle::PerformGoldenSectionLog
 (Number sigma_up,
 Number sigma_lo,
 Number tol,
 const Vector& step_aff_x_L,
 const Vector& step_aff_x_U,
 const Vector& step_aff_s_L,
 const Vector& step_aff_s_U,
 const Vector& step_aff_y_c,
 const Vector& step_aff_y_d,
 const Vector& step_aff_z_L,
 const Vector& step_aff_z_U,
 const Vector& step_aff_v_L,
 const Vector& step_aff_v_U,
 const Vector& step_cen_x_L,
 const Vector& step_cen_x_U,
 const Vector& step_cen_s_L,
 const Vector& step_cen_s_U,
 const Vector& step_cen_y_c,
 const Vector& step_cen_y_d,
 const Vector& step_cen_z_L,
 const Vector& step_cen_z_U,
 const Vector& step_cen_v_L,
 const Vector& step_cen_v_U
 )
 {
 Number log_sigma;
 Number log_sigma_up = std::log(sigma_up);
 Number log_sigma_lo = std::log(sigma_lo);

 Number log_sigma_up_in = log_sigma_up;
 Number log_sigma_lo_in = log_sigma_lo;
 Number gfac = (3.-std::sqrt(5.))/2.;
 Number log_sigma_mid1 = log_sigma_lo + gfac*(log_sigma_up-log_sigma_lo);
 Number log_sigma_mid2 = log_sigma_lo + (1.-gfac)*(log_sigma_up-log_sigma_lo);

 Number qmid1 = CalculateQualityFunction(std::exp(log_sigma_mid1),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);
 Number qmid2 = CalculateQualityFunction(std::exp(log_sigma_mid2),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);

 Index nsections = 0;
 while ((log_sigma_up-log_sigma_lo)>=tol*log_sigma_up && nsections<quality_function_max_section_steps_) {
 nsections++;
 if (qmid1 > qmid2) {
 log_sigma_lo = log_sigma_mid1;
 log_sigma_mid1 = log_sigma_mid2;
 qmid1 = qmid2;
 log_sigma_mid2 = log_sigma_lo + (1.-gfac)*(log_sigma_up-log_sigma_lo);
 qmid2 = CalculateQualityFunction(std::exp(log_sigma_mid2),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);
 }
 else {
 log_sigma_up = log_sigma_mid2;
 log_sigma_mid2 = log_sigma_mid1;
 qmid2 = qmid1;
 log_sigma_mid1 = log_sigma_lo + gfac*(log_sigma_up-log_sigma_lo);
 qmid1 = CalculateQualityFunction(std::exp(log_sigma_mid1),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);
 }
 }

 Number q;
 if (qmid1 < qmid2) {
 log_sigma = log_sigma_mid1;
 q = qmid1;
 }
 else {
 log_sigma = log_sigma_mid2;
 q = qmid2;
 }
 if (log_sigma_up == log_sigma_up_in) {
 Number qtmp = CalculateQualityFunction(std::exp(log_sigma_up),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);
 if (qtmp < q) {
 log_sigma = log_sigma_up;
 q = qtmp;
 }
 }
 else if (log_sigma_lo == log_sigma_lo_in) {
 Number qtmp = CalculateQualityFunction(std::exp(log_sigma_lo),
 step_aff_x_L,
 step_aff_x_U,
 step_aff_s_L,
 step_aff_s_U,
 step_aff_y_c,
 step_aff_y_d,
 step_aff_z_L,
 step_aff_z_U,
 step_aff_v_L,
 step_aff_v_U,
 step_cen_x_L,
 step_cen_x_U,
 step_cen_s_L,
 step_cen_s_U,
 step_cen_y_c,
 step_cen_y_d,
 step_cen_z_L,
 step_cen_z_U,
 step_cen_v_L,
 step_cen_v_U);
 if (qtmp < q) {
 log_sigma = log_sigma_lo;
 q = qtmp;
 }
 }

 return std::exp(log_sigma);
 }
 */

} // namespace Ipopt