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heimbach |
1.1 |
\section{The line search optimisation algorithm |
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\label{sectionoptim}} |
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\subsection{General features} |
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The line search algorithm is based on a quasi-Newton |
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variable storage method which was implemented by |
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\cite{gil-mar:89}. |
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TO BE CONTINUED... |
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\subsection{The online vs. offline version} |
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\begin{itemize} |
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\item {\bf Online version} \\ |
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Every call to {\it simul} refers to an execution of the |
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forward and adjoint model. |
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Several iterations of optimization may thus be performed within |
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a single run of the main program (lsopt\_top). |
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The following cases may occur: |
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\begin{itemize} |
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\item |
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cold start only (no optimization) |
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\item |
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cold start, followed by one or several iterations of optimization |
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\item |
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warm start from previous cold start with one or several iterations |
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\item |
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warm start from previous warm start with one or several iterations |
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\end{itemize} |
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% |
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\item {\bf Offline version} \\ |
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Every call to simul refers to a read procedure which |
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reads the result of a forward and adjoint run |
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Therefore, only one call to simul is allowed, |
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{\tt itmax = 0}, for cold start |
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{\tt itmax = 1}, for warm start |
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Also, at the end, {\bf x(i+1)} needs to be computed and saved |
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to be available for the offline model and adjoint run |
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\end{itemize} |
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In order to achieve minimum difference between the online and offline code |
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{\bf xdiff(i+1)} is stored to file at the end of an (offline) iteration, |
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but recomputed identically at the beginning of the next iteration. |
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\subsection{Number of iterations vs. number of simulations} |
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{\tt - itmax:} controls the max. number of iterations \\ |
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{\tt - nfunc:} controls the max. number of simulations |
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within one iteration |
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\paragraph{Summary} ~ \\ |
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From one iteration to the next the descent direction changes. |
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Within one iteration more than one forward and adjoint |
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run may be performed. |
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The updated control used as input for these simulations uses the same |
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descent direction, but different step sizes. |
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\paragraph{Description} ~ \\ |
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From one iteration to the next the descent direction dd changes using |
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the result for the adjoint vector gg of the previous iteration. |
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In lsline the updated control |
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\[ |
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\tt |
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xdiff(i,1) = xx(i-1) + tact(i-1,1)*dd(i-1) |
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\] |
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serves as input for |
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a forward and adjoint model run yielding a new {\tt gg(i,1)}. |
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In general, the new solution passes the 1st and 2nd Wolfe tests |
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so {\tt xdiff(i,1)} represents the solution sought: |
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\[ |
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{\tt xx(i) = xdiff(i,1)} |
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\] |
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If one of the two tests fails, |
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an inter- or extrapolation is invoked to determine |
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a new step size {\tt tact(i-1,2)}. |
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If more than one function call is permitted, |
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the new step size is used together |
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with the "old" descent direction {\tt dd(i-1)} |
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(i.e. dd is not updated using the new {\tt gg(i)}), |
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to compute a new |
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\[ |
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{\tt xdiff(i,2) = xx(i-1) + tact(i-1,2)*dd(i-1)} |
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\] |
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that serves as input |
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in a new forward and adjoint run, yielding {\tt gg(i,2)}. |
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cnh |
1.2 |
If now, both Wolfe tests are successful, |
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heimbach |
1.1 |
the updated solution is given by |
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\[ |
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\tt |
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xx(i) = xdiff(i,2) = xx(i-1) + tact(i-1,2)*dd(i-1) |
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\] |
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In order to save memory both the fields dd and xdiff |
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have a double usage. |
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\begin{itemize} |
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\item [{\tt xdiff}] ~\\ |
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- in {\it lsopt\_top}: used as {\tt x(i) - x(i-1)} for Hessian update \\ |
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- in {\it lsline}: intermediate result for control update |
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{\tt x = x + tact*dd} |
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\item [{\tt dd}] ~\\ |
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- in {\it lsopt\_top, lsline}: descent vector, {\tt dd = -gg} |
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and {\tt hessupd} \\ |
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- in {\it dgscale}: intermediate result to compute new preconditioner |
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\end{itemize} |
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\paragraph{The parameter file lsopt.par} |
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\begin{itemize} |
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\item {\bf NUPDATE} |
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max. no. of update pairs {\tt (gg(i)-gg(i-1), xx(i)-xx(i-1))} |
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to be stored in {\tt OPWARMD} to estimate Hessian |
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[pair of current iter. is stored in |
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{\tt (2*jmax+2, 2*jmax+3)} |
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jmax must be > 0 to access these entries] |
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Presently {\tt NUPDATE} must be > 0 |
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(i.e. iteration without reference to previous |
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iterations through {\tt OPWARMD} has not been tested) |
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\item {\bf EPSX} |
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relative precision on xx bellow which xx should not be improved |
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\item {\bf EPSG} |
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relative precision on gg below which optimization is |
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considered successful |
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\item {\bf IPRINT} |
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controls verbose (>=1) or non-verbose output |
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\item {\bf NUMITER} |
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max. number of iterations of optimisation; |
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NUMTER = 0: cold start only, no optimization |
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\item {\bf ITER\_NUM} |
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index of new restart file to be created (not necessarily = NUMITER!) |
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\item {\bf NFUNC} |
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max. no. of simulations per iteration |
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(must be > 0); |
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is used if step size {\tt tact} is inter-/extrapolated; |
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in this case, if NFUNC > 1, a new simulation is performed with |
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same gradient but "improved" step size |
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\item {\bf FMIN} |
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first guess cost function value |
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(only used as long as first iteration not completed, |
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i.e. for jmax <= 0) |
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\end{itemize} |
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\paragraph{OPWARMI, OPWARMD files} |
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Two files retain values of previous iterations which are |
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used in latest iteration to update Hessian: |
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\begin{itemize} |
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\item {\bf OPWARMI}: contains index settings and scalar variables |
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{\footnotesize |
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\begin{tabular}{ll} |
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{\tt n = nn} & no. of control variables \\ |
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{\tt fc = ff} & cost value of last iteration \\ |
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{\tt isize} & no. of bytes per record in OPWARMD \\ |
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{\tt m = nupdate} & max. no. of updates for Hessian \\ |
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{\tt jmin, jmax} & pointer indices for OPWARMD file (cf. below) \\ |
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{\tt gnorm0} & norm of first (cold start) gradient gg \\ |
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{\tt iabsiter} & total number of iterations with respect to cold start |
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\end{tabular} |
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} |
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\item {\bf OPWARMD}: contains vectors (control and gradient) |
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{\scriptsize |
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\begin{tabular}{cll} |
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entry & name & description \\ |
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\hline |
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1 & {\tt xx(i)} & control vector of latest iteration \\ |
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2 & {\tt gg(i)} & gradient of latest iteration \\ |
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3 & {\tt xdiff(i),diag} & preconditioning vector; (1,...,1) |
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for cold start \\ |
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2*jmax+2 & {\tt gold=g(i)-g(i-1)} & for last update (jmax) \\ |
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2*jmax+3 & {\tt xdiff=tact*d=xx(i)-xx(i-1)} & for last update (jmax) |
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\end{tabular} |
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} |
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% |
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\end{itemize} |
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\begin{figure}[b!] |
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{\footnotesize |
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\begin{verbatim} |
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Example 1: jmin = 1, jmax = 3, mupd = 5 |
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1 2 3 | 4 5 6 7 8 9 empty empty |
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|___|___|___| | |___|___| |___|___| |___|___| |___|___| |___|___| |
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0 | 1 2 3 |
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Example 2: jmin = 3, jmax = 7, mupd = 5 ---> jmax = 2 |
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1 2 3 | |
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|___|___|___| | |___|___| |___|___| |___|___| |___|___| |___|___| |
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| 6 7 3 4 5 |
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\end{verbatim} |
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} |
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\caption{Examples of OPWARM file handling} |
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\label{fig:opwarm} |
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\end{figure} |
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\paragraph{Error handling} |
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\newpage |
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\begin{figure}[b!] |
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\input{part8/lsopt_flow_1} |
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\caption{Flow chart (part 1 of 3)} |
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\label{fig:lsoptflow1} |
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\end{figure} |
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\begin{figure}[b!] |
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\input{part8/lsopt_flow_2} |
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\caption{Flow chart (part 2 of 3)} |
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\label{fig:lsoptflow2} |
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\end{figure} |
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\begin{figure}[b!] |
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\input{part8/lsopt_flow_3} |
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\caption{Flow chart (part 3 of 3)} |
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\label{fig:lsoptflow3} |
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\end{figure} |
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