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MoochoPack : Framework for Large-Scale Optimization Algorithms Version of the Day
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00001 // @HEADER 00002 // *********************************************************************** 00003 // 00004 // Moocho: Multi-functional Object-Oriented arCHitecture for Optimization 00005 // Copyright (2003) Sandia Corporation 00006 // 00007 // Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive 00008 // license for use of this work by or on behalf of the U.S. Government. 00009 // 00010 // This library is free software; you can redistribute it and/or modify 00011 // it under the terms of the GNU Lesser General Public License as 00012 // published by the Free Software Foundation; either version 2.1 of the 00013 // License, or (at your option) any later version. 00014 // 00015 // This library is distributed in the hope that it will be useful, but 00016 // WITHOUT ANY WARRANTY; without even the implied warranty of 00017 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 00018 // Lesser General Public License for more details. 00019 // 00020 // You should have received a copy of the GNU Lesser General Public 00021 // License along with this library; if not, write to the Free Software 00022 // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 00023 // USA 00024 // Questions? Contact Roscoe A. Bartlett (rabartl@sandia.gov) 00025 // 00026 // *********************************************************************** 00027 // @HEADER 00028 00029 #include <limits> 00030 #include <ostream> 00031 #include <iostream> 00032 00033 #include "MoochoPack_CalcD_vStep_Step.hpp" 00034 #include "MoochoPack_IpState.hpp" 00035 #include "MoochoPack_moocho_algo_conversion.hpp" 00036 #include "IterationPack_print_algorithm_step.hpp" 00037 //#include "ConstrainedOptPack_print_vector_change_stats.hpp" 00038 #include "AbstractLinAlgPack_MatrixSymDiagStd.hpp" 00039 #include "AbstractLinAlgPack_VectorMutable.hpp" 00040 #include "AbstractLinAlgPack_VectorStdOps.hpp" 00041 #include "AbstractLinAlgPack_VectorAuxiliaryOps.hpp" 00042 #include "AbstractLinAlgPack_VectorOut.hpp" 00043 #include "AbstractLinAlgPack_LinAlgOpPack.hpp" 00044 #include "Teuchos_dyn_cast.hpp" 00045 00046 00047 bool MoochoPack::CalcD_vStep_Step::do_step(Algorithm& _algo 00048 , poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss) 00049 { 00050 using Teuchos::dyn_cast; 00051 using IterationPack::print_algorithm_step; 00052 using AbstractLinAlgPack::ele_wise_prod; 00053 using AbstractLinAlgPack::lowerbound_multipliers_step; 00054 using AbstractLinAlgPack::upperbound_multipliers_step; 00055 00056 NLPAlgo &algo = rsqp_algo(_algo); 00057 IpState &s = dyn_cast<IpState>(_algo.state()); 00058 NLP &nlp = algo.nlp(); 00059 00060 EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level(); 00061 std::ostream& out = algo.track().journal_out(); 00062 00063 // print step header. 00064 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) 00065 { 00066 using IterationPack::print_algorithm_step; 00067 print_algorithm_step( algo, step_poss, type, assoc_step_poss, out ); 00068 } 00069 00070 // Get iteration quantities 00071 const value_type& mu = s.barrier_parameter().get_k(0); 00072 const Vector &d_k = s.d().get_k(0); 00073 const MatrixSymDiagStd& invXl = s.invXl().get_k(0); 00074 const MatrixSymDiagStd& invXu = s.invXu().get_k(0); 00075 const MatrixSymDiagStd& Vl = s.Vl().get_k(0); 00076 const MatrixSymDiagStd& Vu = s.Vu().get_k(0); 00077 00078 VectorMutable& dvl_k = s.dvl().set_k(0); 00079 VectorMutable& dvu_k = s.dvu().set_k(0); 00080 00081 lowerbound_multipliers_step(mu, invXl.diag(), Vl.diag(), d_k, &dvl_k); 00082 upperbound_multipliers_step(mu, invXu.diag(), Vu.diag(), d_k, &dvu_k); 00083 00084 /* 00085 // dvl = mu*invXl*e - vl - invXl*Vl*d_k 00086 dvl_k = 0; 00087 ele_wise_prod(-1.0, invXl.diag(), Vl.diag(), &dvl_k); 00088 ele_wise_prod(1.0, dvl_k, d_k, &dvl_k); 00089 00090 std::cout << "d_k =\n" << d_k; 00091 std::cout << "-invXl*Vl*d_k = \n" << dvl_k; 00092 00093 dvl_k.axpy(-1.0, Vl.diag()); 00094 00095 std::cout << "-vl-invXl*Vl*d_k = \n" << dvl_k; 00096 00097 dvl_k.axpy(mu, invXl.diag()); 00098 00099 std::cout << "dvl_k = \n" << dvl_k; 00100 00101 // dvu = mu*invXu*e - vu + invXu*Vu*d_k 00102 dvu_k = 0; 00103 ele_wise_prod(1.0, invXu.diag(), Vu.diag(), &dvu_k); 00104 ele_wise_prod(1.0, dvu_k, d_k, &dvu_k); 00105 00106 dvu_k.axpy(-1.0, Vu.diag()); 00107 00108 dvu_k.axpy(mu, invXu.diag()); 00109 */ 00110 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) 00111 { 00112 out << "\nx_k = \n" << s.x().get_k(0) 00113 << "\nxl = \n" << nlp.xl() 00114 << "\nxu = \n" << nlp.xu() 00115 << "\ndvl_k = \n" << dvl_k 00116 << "\ndvu_k = \n" << dvu_k; 00117 } 00118 00119 return true; 00120 } 00121 00122 void MoochoPack::CalcD_vStep_Step::print_step( const Algorithm& algo 00123 , poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss 00124 , std::ostream& out, const std::string& L ) const 00125 { 00126 out 00127 << L << "*** Calculates the search direction for the dual variables\n" 00128 << L << "dvl_k = mu*invXl_k*e - vl_k - invXl_k*Vl_k*d_k\n" 00129 << L << "dvu_k = mu*invXu_k*e - vu_k + invXu_k*Vu_k*d_k\n"; 00130 }
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