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Add new figures for the incremental techniques

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Jip J. Dekker 2021-07-15 15:29:13 +10:00
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chapters/5_incremental.tex
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@ -566,15 +566,6 @@ We implemented both approaches using the \nanozinc\ prototype, with the results
The incremental \nanozinc\ translation shows a 12x speedup compared to re-compiling the model from scratch in each iteration.
For this particular problem, incrementally instructing the target solver (\gls{gecode}) does not lead to a significant reduction in runtime.
\begin{figure}
\centering
\includegraphics[width=0.5\columnwidth]{assets/img/6_gbac}
\caption{\label{fig:6-gbac}A run-time performance comparison between incremental processing (Incr.)
and re-evaluation (Redo) of 5 GBAC \minizinc\ instances in the application of \gls{lns} on a 3.4 GHz Quad-Core Intel Core i5 using the Gecode 6.1.2 solver.
Each run consisted of 2500 iterations of applying neighbourhood predicates.
Reported times are averages of 10 runs.}
\end{figure}
\paragraph{Radiation} Our second experiment is based on a problem of planning cancer radiation therapy treatment using multi-leaf collimators \autocite{baatar-2011-radiation}.
Two characteristics mark the quality of a solution: the amount of time the patient is exposed to radiation, and the number of ``shots'' or different angles the treatment requires.
However, the first characteristic is considered more important than the second.
@ -592,13 +583,15 @@ Given two variables \mzninline{exposure} and \mzninline{shots}, once we have fou
Since each added lexicographic constraint is strictly stronger than the previous one, we never have to retract previous constraints.
\begin{figure}
\centering
\includegraphics[width=0.5\columnwidth]{assets/img/6_radiation}
\caption{\label{fig:6-radiation}A run-time performance comparison between
incremental processing (Incr.) and re-evaluation (Redo) of 9 Radiation
\minizinc\ instances in the application of Lexicographic objectives on a 3.4
GHz Quad-Core Intel Core i5 using the \gls{gecode} 6.1.2 solver. Each test was run
to optimality and was conducted 20 times to provide an average.}
\begin{subfigure}[b]{0.5\linewidth}
\includegraphics[width=\columnwidth]{assets/img/inc_cmp_lex.pdf}
\caption{\label{subfig:inc-cmp-lex}Radiation}
\end{subfigure}
\begin{subfigure}[b]{0.5\linewidth}
\includegraphics[width=\columnwidth]{assets/img/inc_cmp_lns.pdf}
\caption{\label{subfig:inc-cmp-lns}GBAC}
\end{subfigure}
\caption{\label{fig:inc-cmp} A comparison of the two new incremental techniques and a recompilation strategy.}
\end{figure}
As shown in \cref{fig:6-radiation}, the incremental processing of the added \mzninline{lex_less} calls is a clear improvement over the re-evaluation of the whole model.