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MoochoPack : Framework for Large-Scale Optimization Algorithms
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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 // Redistribution and use in source and binary forms, with or without 00011 // modification, are permitted provided that the following conditions are 00012 // met: 00013 // 00014 // 1. Redistributions of source code must retain the above copyright 00015 // notice, this list of conditions and the following disclaimer. 00016 // 00017 // 2. Redistributions in binary form must reproduce the above copyright 00018 // notice, this list of conditions and the following disclaimer in the 00019 // documentation and/or other materials provided with the distribution. 00020 // 00021 // 3. Neither the name of the Corporation nor the names of the 00022 // contributors may be used to endorse or promote products derived from 00023 // this software without specific prior written permission. 00024 // 00025 // THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY 00026 // EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 00027 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 00028 // PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE 00029 // CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 00030 // EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 00031 // PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00032 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00033 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00034 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00035 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00036 // 00037 // Questions? Contact Roscoe A. Bartlett (rabartl@sandia.gov) 00038 // 00039 // *********************************************************************** 00040 // @HEADER 00041 00042 #include <ostream> 00043 00044 #include "MoochoPack_ReducedGradientStd_Step.hpp" 00045 #include "MoochoPack_moocho_algo_conversion.hpp" 00046 #include "IterationPack_print_algorithm_step.hpp" 00047 #include "AbstractLinAlgPack_VectorMutable.hpp" 00048 #include "AbstractLinAlgPack_VectorOut.hpp" 00049 #include "AbstractLinAlgPack_MatrixOpNonsing.hpp" 00050 #include "AbstractLinAlgPack_LinAlgOpPack.hpp" 00051 00052 namespace MoochoPack { 00053 00054 bool ReducedGradientStd_Step::do_step( 00055 Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type 00056 , poss_type assoc_step_poss 00057 ) 00058 { 00059 using BLAS_Cpp::trans; 00060 using LinAlgOpPack::V_MtV; 00061 00062 NLPAlgo &algo = rsqp_algo(_algo); 00063 NLPAlgoState &s = algo.rsqp_state(); 00064 00065 EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level(); 00066 EJournalOutputLevel ns_olevel = algo.algo_cntr().null_space_journal_output_level(); 00067 std::ostream& out = algo.track().journal_out(); 00068 00069 // print step header. 00070 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00071 using IterationPack::print_algorithm_step; 00072 print_algorithm_step( _algo, step_poss, type, assoc_step_poss, out ); 00073 } 00074 00075 // Get iteration quantities 00076 IterQuantityAccess<VectorMutable> 00077 &Gf_iq = s.Gf(), 00078 &rGf_iq = s.rGf(); 00079 IterQuantityAccess<MatrixOp> 00080 &Z_iq = s.Z(); 00081 00082 // rGf = Z' * Gf 00083 V_MtV( &rGf_iq.set_k(0), Z_iq.get_k(0), trans, Gf_iq.get_k(0) ); 00084 00085 if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00086 out << "\n||rGf||inf = " << rGf_iq.get_k(0).norm_inf() << std::endl; 00087 } 00088 00089 if( static_cast<int>(ns_olevel) >= static_cast<int>(PRINT_VECTORS) ) { 00090 out << "\nrGf_k =\n" << rGf_iq.get_k(0); 00091 } 00092 00093 return true; 00094 } 00095 00096 void ReducedGradientStd_Step::print_step( 00097 const Algorithm& algo, poss_type step_poss, IterationPack::EDoStepType type 00098 ,poss_type assoc_step_poss, std::ostream& out, const std::string& L 00099 ) const 00100 { 00101 out 00102 << L << "*** Evaluate the reduced gradient of the objective funciton\n" 00103 << L << "rGf_k = Z_k' * Gf_k\n"; 00104 } 00105 00106 } // end namespace MoochoPack
1.7.6.1