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MoochoPack : Framework for Large-Scale Optimization Algorithms
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00001 #if 0 00002 00003 // @HEADER 00004 // *********************************************************************** 00005 // 00006 // Moocho: Multi-functional Object-Oriented arCHitecture for Optimization 00007 // Copyright (2003) Sandia Corporation 00008 // 00009 // Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive 00010 // license for use of this work by or on behalf of the U.S. Government. 00011 // 00012 // Redistribution and use in source and binary forms, with or without 00013 // modification, are permitted provided that the following conditions are 00014 // met: 00015 // 00016 // 1. Redistributions of source code must retain the above copyright 00017 // notice, this list of conditions and the following disclaimer. 00018 // 00019 // 2. Redistributions in binary form must reproduce the above copyright 00020 // notice, this list of conditions and the following disclaimer in the 00021 // documentation and/or other materials provided with the distribution. 00022 // 00023 // 3. Neither the name of the Corporation nor the names of the 00024 // contributors may be used to endorse or promote products derived from 00025 // this software without specific prior written permission. 00026 // 00027 // THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY 00028 // EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 00029 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 00030 // PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE 00031 // CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 00032 // EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 00033 // PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00034 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00035 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00036 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00037 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00038 // 00039 // Questions? Contact Roscoe A. Bartlett (rabartl@sandia.gov) 00040 // 00041 // *********************************************************************** 00042 // @HEADER 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
1.7.6.1