/[MITgcm]/manual/s_autodiff/text/doc_ad_2.tex
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revision 1.7 by cnh, Thu Oct 25 18:36:55 2001 UTC revision 1.10 by cnh, Thu Nov 15 15:05:45 2001 UTC
# Line 607  at every timestep Line 607  at every timestep
607  [i.e. every hour $ i=0,...,5$ corresponding to  [i.e. every hour $ i=0,...,5$ corresponding to
608  $ k_{i}^{lev1} = 66, 67, \ldots, 71 $].  $ k_{i}^{lev1} = 66, 67, \ldots, 71 $].
609  Thus, the  final state $ v_n = v_{k_{n}^{lev1}} $ is reached  Thus, the  final state $ v_n = v_{k_{n}^{lev1}} $ is reached
610  and the model state of all  proceeding timesteps along the last  and the model state of all proceeding timesteps along the last
611  sub-subsections are available, enabling integration backwards  sub-subsections are available, enabling integration backwards
612  in time along the last sub-subsection.  in time along the last sub-subsection.
613  Thus, the adjoint can be computed along this last  Thus, the adjoint can be computed along this last
# Line 1258  In this section we describe in a general Line 1258  In this section we describe in a general
1258  the parts of the code that are relevant for automatic  the parts of the code that are relevant for automatic
1259  differentiation using the software tool TAMC.  differentiation using the software tool TAMC.
1260    
 \begin{figure}[b!]  
1261  \input{part5/doc_ad_the_model}  \input{part5/doc_ad_the_model}
 \caption{~}  
 \label{fig:adthemodel}  
 \end{figure}  
1262    
1263  The basic flow is depicted in \ref{fig:adthemodel}.  The basic flow is depicted in \ref{fig:adthemodel}.
1264  If the option {\tt ALLOW\_AUTODIFF\_TAMC} is defined, the driver routine  If the option {\tt ALLOW\_AUTODIFF\_TAMC} is defined, the driver routine
# Line 1782  The gradient $ \nabla _{u}{\cal J} |_{u_ Line 1778  The gradient $ \nabla _{u}{\cal J} |_{u_
1778  with the value of the cost function itself $ {\cal J}(u_{[k]}) $  with the value of the cost function itself $ {\cal J}(u_{[k]}) $
1779  at iteration step $ k $ serve  at iteration step $ k $ serve
1780  as input to a minimization routine (e.g. quasi-Newton method,  as input to a minimization routine (e.g. quasi-Newton method,
1781  conjugate gradient, ... \cite{gil_lem:89})  conjugate gradient, ... \cite{gil-lem:89})
1782  to compute an update in the  to compute an update in the
1783  control variable for iteration step $k+1$  control variable for iteration step $k+1$
1784  \[  \[
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