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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_CheckDescentQuasiNormalStep_Step.hpp" 00045 #include "MoochoPack_Exceptions.hpp" 00046 #include "MoochoPack_moocho_algo_conversion.hpp" 00047 #include "IterationPack_print_algorithm_step.hpp" 00048 #include "AbstractLinAlgPack_VectorSpace.hpp" 00049 #include "AbstractLinAlgPack_VectorMutable.hpp" 00050 #include "AbstractLinAlgPack_VectorOut.hpp" 00051 #include "AbstractLinAlgPack_VectorStdOps.hpp" 00052 #include "AbstractLinAlgPack_MatrixOp.hpp" 00053 #include "AbstractLinAlgPack_LinAlgOpPack.hpp" 00054 #include "Teuchos_Assert.hpp" 00055 00056 namespace MoochoPack { 00057 00058 CheckDescentQuasiNormalStep_Step::CheckDescentQuasiNormalStep_Step( 00059 const calc_fd_prod_ptr_t& calc_fd_prod 00060 ) 00061 :calc_fd_prod_(calc_fd_prod) 00062 {} 00063 00064 bool CheckDescentQuasiNormalStep_Step::do_step( 00065 Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type 00066 ,poss_type assoc_step_poss 00067 ) 00068 { 00069 using BLAS_Cpp::no_trans; 00070 using AbstractLinAlgPack::dot; 00071 using LinAlgOpPack::V_MtV; 00072 00073 NLPAlgo &algo = rsqp_algo(_algo); 00074 NLPAlgoState &s = algo.rsqp_state(); 00075 NLP &nlp = algo.nlp(); 00076 const Range1D equ_decomp = s.equ_decomp(); 00077 00078 EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level(); 00079 std::ostream& out = algo.track().journal_out(); 00080 00081 // print step header. 00082 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00083 using IterationPack::print_algorithm_step; 00084 print_algorithm_step( algo, step_poss, type, assoc_step_poss, out ); 00085 } 00086 00087 const size_type 00088 nb = nlp.num_bounded_x(); 00089 00090 // Get iteration quantities 00091 IterQuantityAccess<VectorMutable> 00092 &c_iq = s.c(), 00093 &Ypy_iq = s.Ypy(); 00094 const Vector::vec_ptr_t 00095 cd_k = c_iq.get_k(0).sub_view(equ_decomp); 00096 const Vector 00097 &Ypy_k = Ypy_iq.get_k(0); 00098 00099 value_type descent_c = -1.0; 00100 if( s.get_iter_quant_id( Gc_name ) != AlgorithmState::DOES_NOT_EXIST ) { 00101 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00102 out << "\nGc_k exists; compute descent_c = c_k(equ_decomp)'*Gc_k(:,equ_decomp)'*Ypy_k ...\n"; 00103 } 00104 const MatrixOp::mat_ptr_t 00105 Gcd_k = s.Gc().get_k(0).sub_view(Range1D(),equ_decomp); 00106 VectorSpace::vec_mut_ptr_t 00107 t = cd_k->space().create_member(); 00108 V_MtV( t.get(), *Gcd_k, BLAS_Cpp::trans, Ypy_k ); 00109 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) { 00110 out << "\nGc_k(:,equ_decomp)'*Ypy_k =\n" << *t; 00111 } 00112 descent_c = dot( *cd_k, *t ); 00113 } 00114 else { 00115 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00116 out << "\nGc_k does not exist; compute descent_c = c_k(equ_decomp)'*FDGc_k(:,equ_decomp)'*Ypy_k " 00117 << "using finite differences ...\n"; 00118 } 00119 VectorSpace::vec_mut_ptr_t 00120 t = nlp.space_c()->create_member(); 00121 calc_fd_prod().calc_deriv_product( 00122 s.x().get_k(0),nb?&nlp.xl():NULL,nb?&nlp.xu():NULL 00123 ,Ypy_k,NULL,&c_iq.get_k(0),true,&nlp 00124 ,NULL,t.get() 00125 ,static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ? &out : NULL 00126 ); 00127 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_VECTORS) ) { 00128 out << "\nFDGc_k(:,equ_decomp)'*Ypy_k =\n" << *t->sub_view(equ_decomp); 00129 } 00130 descent_c = dot( *cd_k, *t->sub_view(equ_decomp) ); 00131 } 00132 00133 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00134 out << "\ndescent_c = " << descent_c << std::endl; 00135 } 00136 00137 if( descent_c > 0.0 ) { // ToDo: add some allowance for > 0.0 for finite difference errors! 00138 if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) { 00139 out << "\nError, descent_c > 0.0; this is not a descent direction\n" 00140 << "Throw TestFailed and terminate the algorithm ...\n"; 00141 } 00142 TEUCHOS_TEST_FOR_EXCEPTION( 00143 true, TestFailed 00144 ,"CheckDescentQuasiNormalStep_Step::do_step(...) : Error, descent for the decomposed constraints " 00145 "with respect to the quasi-normal step c_k(equ_decomp)'*FDGc_k(:,equ_decomp)'*Ypy_k = " 00146 << descent_c << " > 0.0; This is not a descent direction!\n" ); 00147 } 00148 00149 return true; 00150 } 00151 00152 void CheckDescentQuasiNormalStep_Step::print_step( 00153 const Algorithm& algo, poss_type step_poss, IterationPack::EDoStepType type 00154 ,poss_type assoc_step_poss, std::ostream& out, const std::string& L 00155 ) const 00156 { 00157 out 00158 << L << "*** Check for descent in the decomposed equality constraints for the quasi-normal step\n" 00159 << L << "if Gc_k exists then\n" 00160 << L << " descent_c = c_k(equ_decomp)'*Gc_k(:,equ_decomp)'*Ypy_k\n" 00161 << L << "else\n" 00162 << L << " descent_c = c_k(equ_decomp)'*FDGc(:,equ_decomp)'*Ypy_k (finite diff.)\n" 00163 << L << "end\n" 00164 << L << "if descent > 0.0 then\n" 00165 << L << " throw TestFailed exception!\n" 00166 << L << "end\n" 00167 ; 00168 } 00169 00170 } // end namespace MoochoPack
1.7.6.1