MoochoPack : Framework for Large-Scale Optimization Algorithms Version of the Day
MoochoPack_MeritFunc_PenaltyParamUpdateWithMult_AddedStep.cpp
00001 #if 0
00002 
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00006 // Moocho: Multi-functional Object-Oriented arCHitecture for Optimization
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00043 
00044 #include <ostream>
00045 #include <typeinfo>
00046 
00047 #include "MoochoPack_MeritFunc_PenaltyParamUpdateWithMult_AddedStep.hpp"
00048 #include "MoochoPack_NLPAlgoState.hpp"
00049 #include "ConstrainedOptPack/src/VectorWithNorms.h"
00050 
00051 namespace MoochoPack {
00052 
00053 MeritFunc_PenaltyParamUpdateWithMult_AddedStep::MeritFunc_PenaltyParamUpdateWithMult_AddedStep(
00054       const merit_func_ptr_t& merit_func, value_type small_mu
00055     , value_type mult_factor, value_type kkt_near_sol )
00056   : MeritFunc_PenaltyParamUpdateGuts_AddedStep(merit_func,small_mu,mult_factor,kkt_near_sol)
00057 {}
00058 
00059 // Overridden from MeritFunc_PenaltyParamUpdateGuts_AddedStep
00060 
00061 bool MeritFunc_PenaltyParamUpdateWithMult_AddedStep::min_mu(
00062   NLPAlgoState& s, value_type* min_mu ) const
00063 {
00064   if ( s.lambda().updated_k(0) ) {
00065     *min_mu = s.lambda().get_k(0).norm_inf();
00066     return true;
00067   }
00068   return false;
00069 }
00070 
00071 void MeritFunc_PenaltyParamUpdateWithMult_AddedStep::print_min_mu_step(
00072   std::ostream& out, const std::string& L ) const
00073 {
00074   out
00075     << L << "if lambda_k is updated then\n"
00076     << L << "    min_mu = norm( lambda_k, inf )\n"
00077     << L << "    update_mu = true\n"
00078     << L << "else\n"
00079     << L << "    update_mu = false\n"
00080     << L << "endif\n"
00081     ;
00082 }
00083 
00084 } // end namespace MoochoPack
00085 
00086 #endif // 0
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