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% $Header: /u/gcmpack/manual/part3/case_studies/global_oce_estimation/global_oce_estimation.tex,v 1.13 2008/02/28 18:22:03 jmc Exp $ |
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% $Name: $ |
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|
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\section[Global Ocean State Estimation Example]{Global Ocean State Estimation at $4^\circ$ Resolution} |
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\label{www:tutorials} |
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\label{sect:eg-global_state_estimate} |
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\begin{rawhtml} |
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<!-- CMIREDIR:eg-global_state_estimate: --> |
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\end{rawhtml} |
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\begin{center} |
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(in directory: {\it verification/tutorial\_global\_oce\_optim/}) |
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\end{center} |
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|
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\subsection{Overview} |
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|
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This experiment illustrates the optimization capacity of the MITgcm: here, |
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a high level description. |
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|
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In this tutorial, a very simple case is used to illustrate the optimization |
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capacity of the MITgcm. Using an ocean configuration with realistic geography |
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and bathymetry on a $4\times4^\circ$ spherical polar grid, we estimate a |
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time-independent surface heat flux adjustment $Q_\mathrm{netm}$ that attempts |
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to bring the model climatology into consistency with observations (Levitus |
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dataset, \cite{lev:94a}). The files for this experiment can be found in the |
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verification directory under tutorial\_global\_oce\_optim. |
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|
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This adjustment $Q_\mathrm{netm}$ (a 2D field only function of longitude and |
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latitude) is the control variable of an optimization problem. It is inferred |
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by an iterative procedure using an `adjoint technique' and a least-squares |
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method (see, for example, \cite{stam-etal:02} and \cite{fer-eta:05}). |
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|
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The ocean model is run forward in time and the quality of the solution is |
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determined by a cost function, $J_1$, a measure of the departure of the model |
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climatology from observations: |
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\begin{equation}\label{cost_temp} |
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J_1=\frac{1}{N}\sum_{i=1}^N \left[ \frac{\overline{T}_i-\overline{T}_i^{lev}}{\sigma_i^T}\right]^2 |
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\end{equation} |
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where $\overline{T}_i$ and $\overline{T}_i^{lev}$ are, respectively, the model |
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and observed potential temperature at each |
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grid point $i$. The differences are weighted by an {\it a priori} uncertainty |
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$\sigma_i^T$ on observations (as provided by \cite{lev:94a}). The error |
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$\sigma_i^T$ is only a function of depth and varies from 0.5 at the surface to |
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0.05~K at the bottom of the ocean, mainly reflecting the decreasing |
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temperature variance with depth (Fig. \ref{Error}a). A value of $J_1$ of |
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order 1 means that the model is, on average, within observational |
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uncertainties. |
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|
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The cost function also places constraints on the adjustment to insure it is |
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"reasonable", i.e. of order of the uncertainties on the observed surface heat |
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flux: |
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\begin{equation} |
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J_2 = \frac{1}{N} \sum_{i=1}^N \left[\frac{Q_\mathrm{netm}}{\sigma^Q_i} \right]^2 |
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\end{equation} |
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where $\sigma^Q_i$ are the {\it a priori} errors on the observed heat flux as |
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estimated by Stammer et al. (2002) from 30\% of local root-mean-square |
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variability of the NCEP forcing field (Fig \ref{Error}b). |
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|
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The total cost function is defined as $J=\lambda_1 J_1+ \lambda_2 J_2$ where |
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$\lambda_1$ and $\lambda_2$ are weights controlling the relative contribution |
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of the two components. The adjoint model then yields the sensitivities |
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$\partial J/\partial Q_\mathrm{netm}$ of $J$ relative to the 2D fields |
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$Q_\mathrm{netm}$. Using a line-searching algorithm (\cite{gil-lem:89}), |
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$Q_\mathrm{netm}$ is adjusted then in the sense to |
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reduce $J$ --- the procedure is repeated until convergence. |
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|
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%The configuration is identical |
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%to the ``Global Ocean circulation'' tutorial where more details can be found. |
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|
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Fig. \ref{Results} shows the results of such an optimization. The |
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model is started from rest and from January-mean temperature and salinity |
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initial conditions taken from the Levitus dataset. The experiment is run a year |
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and the averaged temperature over the whole run (i.e. annual mean) is used |
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in the cost function (\ref{cost_temp}) to evaluate the model. Only the |
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top 2 levels are used. The first guess $Q_\mathrm{netm}$ is chosen to be |
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zero. The weights $\lambda_1$ and $\lambda_2$ are set to 1 and 2, respectively. |
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The total cost function converges after 15 iterations, decreasing from 6.1 to |
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2.7 (the temperature contribution decreases from 6.1 to 1.8 while the heat |
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flux one increases from 0 to 0.42). The right panels of Fig. (\ref{Results}) |
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illustrate the evolution of the temperature error at the surface from |
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iteration 0 to iteration 15. Unsurprisingly, the largest errors at iteration 0 |
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(up to 6$^\circ$C, top left panels) are found in the Western boundary |
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currents. After optimization, the departure of the model temperature from |
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observations is reduced to 1$^\circ$C or less almost everywhere except in the |
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Pacific Equatorial Cold Tongue. Comparison of the initial temperature |
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error (top, right) and heat flux adjustment (bottom, left) shows that the |
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system basically increased the heat flux out of the ocean where temperatures |
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were too warm and vice-versa. Obviously, heat flux uncertainties are not the |
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sole responsible for temperature errors and the heat flux adjustment partly |
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compensates the poor representation of narrow currents (Western boundary |
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currents, Equatorial currents) at $4\times4^\circ$ resolution. This is |
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allowed by the large {\it a priori} error on the heat flux (Fig. \ref{Error}). |
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The Pacific Cold Tongue is a counter example: there, heat fluxes uncertainties |
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are fairly small (about 20~W.m$^2$), and a large temperature errors |
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remains after optimization. |
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|
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In the following, section 2 describes in details the implementation of the |
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control variable $Q_\mathrm{netm}$, the cost function $J$ and the I/O required |
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for the communication between the model and the line-search. Instructions to |
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compile the MITgcm and its adjoint and the line-search algorithm are given in |
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section 3. The method used to run the experiment is described in section 4. |
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|
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\begin{figure} [tpb] |
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\begin{center} |
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\includegraphics[width=\textwidth,height=.3\textheight]{part3/case_studies/global_oce_estimation/Error.eps} |
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\caption{{\it A priori} errors on potential temperature (left, in $^\circ$C) and |
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surface heat flux (right, in W~m$^{-2}$) used to compute the cost |
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terms $J_1$ and $J_2$, respectively.} |
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\label{Error} |
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\end{center} |
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\end{figure} |
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|
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\begin{figure} [tpb] |
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\begin{center} |
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\includegraphics[width=\textwidth,height=.3\textheight]{part3/case_studies/global_oce_estimation/Tutorial_fig.eps} |
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\caption{Initial annual mean surface heat flux (top right in W.m$^{-2}$) and |
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adjustment obtained at iteration 15 (bottom right). Averaged difference |
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between model and observed potential temperatures at the surface (in $^\circ$C) |
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before optimization (iteration 0, top right) and after optimization |
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(iteration 15, bottom right). Contour intervals for heat flux and temperature |
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are 25~W.m$^{-2}$ and 1$^\circ$C, respectively. A positive flux is out of the |
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ocean.} |
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\label{Results} |
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\end{center} |
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\end{figure} |
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|
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\subsection{Implementation of the control variable and the cost function} |
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|
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One of the goal of this tutorial is to illustrate how to implement a new |
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control variable. Most of this is fairly generic and is done in the ctrl |
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and cost packages found in the pkg/ directory. The modifications can be |
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tracked by the CPP option ALLOW\_HFLUXM\_CONTROL or the comment |
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cHFLUXM\_CONTROL. The more specific modifications required for the experiment |
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are found in verification/tutorial\_global\_oce\_optim/code\_ad. Here follows |
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a brief description of the implementation. |
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|
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\subsubsection{The control variable} |
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|
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The adjustment $Q_\mathrm{netm}$ is activated by setting |
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ALLOW\_HFLUXM\_CONTROL to "define" in ECCO\_OPTIONS.h. |
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|
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It is first implemented as a ``normal'' forcing variable. It is defined in |
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FFIELDS.h, initialized to zero in ini\_forcing.F, and then used in |
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external\_forcing\_surf.F. $Q_\mathrm{netm}$ is made a control variable in |
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the ctrl package by modifying the following subroutines: |
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|
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\begin{itemize} |
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\item ctrl\_init.F where $Q_\mathrm{netm}$ is defined as the control variable |
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number 24, |
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|
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\item ctrl\_pack.F which writes, at the end of each iteration, the sensitivity |
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of the cost function $\partial J/\partial Q_\mathrm{netm}$ in to a file to be |
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used by the line-search algorithm, |
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|
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\item ctrl\_unpack.F which reads, at the start of each iteration, the updated |
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adjustment as provided by the line-search algorithm, |
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|
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\item ctrl\_map\_forcing.F in which the updated adjustment is added to the |
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first guess $Q_\mathrm{netm}$. |
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\end{itemize} |
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|
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Note also some minor changes in ctrl.h, ctrl\_readparams.F, and ctrl\_dummy.h |
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(xx\_hfluxm\_file, fname\_hfluxm, xx\_hfluxm\_dummy). |
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|
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\subsubsection{Cost functions} |
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|
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The cost functions are implemented using the {\it cost} package. |
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|
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\begin{itemize} |
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|
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\item The temperature cost function $J_1$ which measures the drift of the mean |
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model temperature from the Levitus climatology is implemented in cost\_temp.F. |
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It is activated by ALLOW\_COST\_TEMP in ECCO\_OPTIONS.h. It requires the mean |
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temperature of the model which is obtained by accumulating the temperature in |
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cost\_tile.F (called at each time step). |
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The value of the cost function is stored in {\it objf\_temp} and its weight |
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$\lambda_1$ in {\it mult\_temp}. |
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|
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\item The heat flux cost function, penalizing the departure of the surface |
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heat flux from observations is implemented in cost\_hflux.F, and activated by |
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the key ALLOW\_COST\_HFLUXM in ECCO\_OPTIONS.h. The value of the cost |
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function is stored in {\it objf\_hfluxm} and its weight $\lambda_2$ in |
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{\it mult\_hfluxm}. |
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|
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\item The subroutine cost\_final.F calls the cost\_functions subroutines |
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and make the (weighted) sum of the various contributions. |
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|
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\item The various weights used in the cost functions are read in |
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cost\_weights.F. The weight of the cost functions are read in |
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cost\_readparams.F from the input file data.cost. |
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|
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\end{itemize} |
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|
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|
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\subsection{Code Configuration} |
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\label{www:tutorials} |
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\label{SEC:eg_globest_code_config} |
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|
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The model configuration for this experiment resides under the directory |
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{\it verification/tutorial\_global\_oce\_optim/}. The experiment files in |
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code\_ad/ and input\_ad/ contain the code customizations and parameter |
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settings. Most of them are identical to those used in the Global Ocean |
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( experiment {\it tutorial\_global\_oce\_latlon}). Below, we describe some of |
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the customizations required for this experiment. |
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|
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\subsubsection{Compilation-time customizations in {\it code\_ad/}} |
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|
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In ECCO\_CPPOPTIONS.h: |
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|
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\begin{itemize} |
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\item define ALLOW\_ECCO\_OPTIMIZATION |
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|
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\item define ALLOW\_COST, ALLOW\_COST\_TEMP, and ALLOW\_COST\_HFLUXM |
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|
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\item define ALLOW\_HFLUXM\_CONTROL |
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\end{itemize} |
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|
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\subsubsection{Running-time customizations in {\it input\_ad/}} |
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|
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\begin{itemize} |
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|
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\item {\it data}: note the smaller {\it cg2dTargetResidual} than in the |
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forward-only experiment, |
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|
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\item {\it data.optim} specifies the iteration number, |
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|
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\item {\it data.ctrl} is used, in particular, to specify the |
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name of the sensitivity and adjustment files associated to a control |
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variable, |
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|
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\item {\it data.cost}: parameters of the cost functions, in particular |
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{\it lastinterval} specifies the length of time-averaging for the model |
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temperature to be used in the cost function (\ref{cost_temp}), |
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|
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\item {\it data.pkg}: note that the Gradient Check package is turned on by |
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default (useGrdchk=.TRUE.), |
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|
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\item {\it Err\_hflux.bin} and {\it Err\_levitus\_15layer.bin} are the |
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files containing the heat flux and potential temperature uncertainties, |
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respectively. |
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|
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\end{itemize} |
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|
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\subsection{Compiling} |
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|
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The optimization experiment requires two executables: 1) the |
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MITgcm and its adjoint ({\it mitgcmuv\_ad}) and 2) the line-search |
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algorithm ({\it optim.x}). |
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|
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\subsubsection{Compilation of MITgcm and its adjoint: {\it mitcgmuv\_ad}} |
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|
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Before compiling, first note that, in the directory code\_ad/, two files |
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must be updated: |
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\begin{itemize} |
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\item code\_ad\_diff.list which lists new subroutines to be compiled |
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by the TAF software (cost\_temp.F and cost\_hflux.F here), |
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|
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\item the adjoint\_hfluxm files which provides a list of the control variables |
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and the name of cost function to the TAF software. |
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|
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\end{itemize} |
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|
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Then, in the directory build\_ad/, type: |
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\begin{verbatim} |
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% ../../../tools/genmake2 -mods=../code\_ad -adof=../code\_ad/adjoint\_hfluxm |
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% make depend |
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% make adall |
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\end{verbatim} |
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to generate the MITgcm executable {\it mitgcmuv\_ad}. |
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|
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\subsubsection{Compilation of the line-search algorithm: {\it optim.x}} |
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|
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This is done from the directories lsopt/ and optim/ (under MITgcm/). In lsopt/, |
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unzip the blash1 library adapted to your platform, and change the Makefile |
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accordingly. Compile with: |
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\begin{verbatim} |
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% make all |
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\end{verbatim} |
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(more details in lsopt\_doc.txt) |
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|
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In optim/, the path of the directory where {\it mitgcm\_ad} was compiled |
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must be specified in the Makefile in the variable INCLUDEDIRS. The file name |
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of the control variable (xx\_hfluxm\_file here) must be added to the name list |
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read by optim\_num.F. Then use |
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\begin{verbatim} |
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% make depend |
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\end{verbatim} |
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and |
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\begin{verbatim} |
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% make |
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\end{verbatim} |
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to generate the line-search executable {\it optim.x}. |
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|
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\subsection{Running the estimation} |
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|
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Copy the {\it mitgcmuv\_ad} executable to input\_ad/ and {\it optim.x} to the |
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subdirectory input\_ad/OPTIM/. Move into input\_ad/. The first iteration is |
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somewhat particular and is best done "by hand" while the following iterations |
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can be run automatically (see below). Check that the iteration number is set |
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to 0 in data.optim and run the MITgcm: |
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\begin{verbatim} |
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% ./mitgcmuv_ad |
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\end{verbatim} |
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|
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The output files adxx\_hfluxm.0000000000.* and xx\_hfluxm.0000000000.* contain |
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the sensitivity of the cost function to $Q_\mathrm{netm}$ and the adjustment |
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to $Q_\mathrm{netm}$ (zero at the first iteration), respectively. Two other |
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files called costhflux\_tut\_MITgcm.opt0000 and ctrlhflux\_tut\_MITgcm.opt0000 |
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are also generated. They essentially contain the same information as the |
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adxx\_.hfluxm* and xx\_hfluxm* files, but in a compressed format. These two files |
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are the only ones involved in the communication between the adjoint model |
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{\it mitgcmuv\_ad} and the line-search algorithm {\it optim.x}. Only at the first |
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iteration, are they both generated by {\it mitgcmuv\_ad}. Subsenquently, |
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costhflux\_tut\_MITgcm.opt$n$ is an output of the adjoint model at |
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iteration $n$ and an input of the line-search. The latter returns an updated |
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adjustment in ctrlhflux\_tut\_MITgcm.opt$n+1$ to be used as an input of the |
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adjoint model at iteration n+1. |
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|
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At the first iteration, move costhflux\_tut\_MITgcm.opt0000 and |
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ctrlhflux\_tut\_MITgcm.opt0000 to OPTIM/, move into this directory and link data.optim |
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and data.ctrl locally: |
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\begin{verbatim} |
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% cd OPTIM/ |
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% ln -s ../data.optim . |
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% ln -s ../data.ctrl . |
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\end{verbatim} |
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The target cost function {\it fmin} needs to be specified too: as a rule of thumb, |
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it should be about 0.95-0.90 times the value of the cost function at |
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the first iteration. This value is only used at the first iteration and does |
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not need to be updated afterward. However, it implicitly specifies the |
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``pace'' at which the cost function is going down (if you are lucky and it does |
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indeed diminish!). More in the ECCO section maybe ? |
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|
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Once this is done, run the line-search algorithm: |
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\begin{verbatim} |
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% ./optim.x |
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\end{verbatim} |
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which computes the updated adjustment for iteration 1, ctrlhflux\_tut\_MITgcm.opt0001. |
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|
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The following iterations can be executed automatically using the shell |
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script {\it cycsh} found in input\_ad/. This script will take care of changing |
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the iteration numbers in the data.optim, launch the adjoint model, clean and |
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store the outputs, move the costhflux* and ctrlhflux* files, and run the |
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line-search algorithm. Edit {\it cycsh} to specify the prefix of the |
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directories used to store the outputs and the maximum number of iteration. |
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|