00001 /* @HEADER@ */ 00002 // ************************************************************************ 00003 // 00004 // Playa: Programmable Linear Algebra 00005 // Copyright 2012 Sandia Corporation 00006 // 00007 // Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, 00008 // the U.S. Government retains certain rights in this software. 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 Kevin Long (kevin.long@ttu.edu) 00038 // 00039 00040 /* @HEADER@ */ 00041 00042 00043 #ifndef PLAYA_OPT_STATE_H 00044 #define PLAYA_OPT_STATE_H 00045 00046 00047 #include "PlayaVectorDecl.hpp" 00048 00049 namespace Playa 00050 { 00051 00052 /** 00053 * OptStatus provides diagnostic information on the current state 00054 * of an optimization run. 00055 */ 00056 enum OptStatus 00057 { 00058 Opt_Continue, 00059 Opt_Converged, 00060 Opt_DirectionFailure, 00061 Opt_ExceededMaxiters, 00062 Opt_LineSearchFailed, 00063 Opt_Crashed 00064 }; 00065 00066 /** \relates OptStatus */ 00067 inline std::ostream& operator<<(std::ostream& os, const OptStatus& s) 00068 { 00069 switch (s) 00070 { 00071 case Opt_Continue: 00072 os << "Opt_Continue"; break; 00073 case Opt_Converged: 00074 os << "Opt_Converged"; break; 00075 case Opt_DirectionFailure: 00076 os << "Opt_DirectionFailure"; break; 00077 case Opt_ExceededMaxiters: 00078 os << "Opt_ExceededMaxiters"; break; 00079 case Opt_LineSearchFailed: 00080 os << "Opt_LineSearchFailed"; break; 00081 default: 00082 os << "Opt_Crashed"; 00083 } 00084 return os; 00085 } 00086 00087 00088 /** 00089 * OptState encapsulates the current state of an optimization run, for 00090 * use in convergence testing. 00091 */ 00092 class OptState 00093 { 00094 public: 00095 /** */ 00096 OptState(const Vector<double>& xCur, 00097 const double& fCur, 00098 const Vector<double>& gradCur); 00099 00100 /** */ 00101 OptStatus status() const {return status_;} 00102 00103 /** */ 00104 void setStatus(const OptStatus status) {status_ = status;} 00105 00106 /** Return the current iteration count */ 00107 int iter() const {return iter_;} 00108 00109 /** Return the current objective function value */ 00110 double fCur() const {return fCur_;} 00111 00112 /** Return the previous objective function value */ 00113 double fPrev() const {return fPrev_;} 00114 00115 /** Return the current evaluation point */ 00116 Vector<double> xCur() const {return xCur_;} 00117 00118 /** Return the previous evaluation point */ 00119 Vector<double> xPrev() const {return xPrev_;} 00120 00121 /** Return the current gradient */ 00122 Vector<double> gradCur() const {return gradCur_;} 00123 00124 /** Return the previous gradientx */ 00125 Vector<double> gradPrev() const {return gradPrev_;} 00126 00127 /** */ 00128 void update(const Vector<double>& xNew, const Vector<double>& gradNew, 00129 const double& fNew); 00130 00131 private: 00132 OptStatus status_; 00133 int iter_; 00134 00135 Vector<double> xCur_; 00136 Vector<double> xPrev_; 00137 00138 Vector<double> gradCur_; 00139 Vector<double> gradPrev_; 00140 00141 double fCur_; 00142 double fPrev_; 00143 00144 }; 00145 00146 } 00147 00148 #endif 00149