PlayaOptState.hpp
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00002 // ************************************************************************
00003 // 
00004 //                 Playa: Programmable Linear Algebra
00005 //                 Copyright 2012 Sandia Corporation
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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 

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