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Fix small spelling inconsistencies

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Jip J. Dekker 2021-07-25 15:32:42 +10:00
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4
chapters/3_rewriting.tex
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@ -805,8 +805,8 @@ Times are averages of 10 runs.
Points below the line indicate that the new system is faster. Points below the line indicate that the new system is faster.
On average, the new system achieves a speed-up of 5.5, with every \instance{} achieving at least 2.5 speed-up and multiple \instances{} with a speed-up of over 100. On average, the new system achieves a speed-up of 5.5, with every \instance{} achieving at least 2.5 speed-up and multiple \instances{} with a speed-up of over 100.
In terms of memory performance (\cref{sfig:rew-comparemem}), version 2.5.5 can sometimes still outperform the new prototype. In terms of memory performance (\cref{sfig:rew-comparemem}), version 2.5.5 can sometimes still outperform the new prototype.
We have identified that the main memory bottlenecks are our currently unoptimised implementations of \gls{cse} lookup tables and argument vectors. We have identified that the main memory bottlenecks are our currently unoptimized implementations of \gls{cse} lookup tables and argument vectors.
These are very encouraging results, given that we are comparing a largely unoptimised prototype to a mature piece of software. These are very encouraging results, given that we are comparing a largely unoptimized prototype to a mature piece of software.
\begin{figure} \begin{figure}
\centering \centering

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chapters/5_incremental.tex
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@ -560,7 +560,7 @@ For one of the \solvers{}, we also compare with an oracle approach that can dire
As such, we show that the use of \gls{rbmo} introduces an insignificant computational overhead. As such, we show that the use of \gls{rbmo} introduces an insignificant computational overhead.
Our second experiment compares the performance of using \minizinc{} incrementally. Our second experiment compares the performance of using \minizinc{} incrementally.
We compare our two methods, \gls{rbmo} and the incremental constraint modelling interface, against the baseline of continuously \gls{rewriting} and reinitialising the \solver{}. We compare our two methods, \gls{rbmo} and the incremental constraint modelling interface, against the baseline of continuously \gls{rewriting} and reinitializing the \solver{}.
For this comparison compare the time that is required to (repeatedly) rewrite an \instance{} and the time required by the \solver{}. For this comparison compare the time that is required to (repeatedly) rewrite an \instance{} and the time required by the \solver{}.
The first model contains a lexicographic objective. The first model contains a lexicographic objective.
The second model is shared between the two experiments and uses a round-robin \gls{lns} approach. The second model is shared between the two experiments and uses a round-robin \gls{lns} approach.

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chapters/6_conclusions.tex
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@ -128,7 +128,7 @@ Since multiple iterations of \gls{meta-optimization} typically share large parts
As an additional improvement, the changes observed in the \gls{slv-mod} can be incrementally applied within the \solver{}. As an additional improvement, the changes observed in the \gls{slv-mod} can be incrementally applied within the \solver{}.
Ideally, the \solver{} can fully support the incremental changes made to the \gls{slv-mod}. Ideally, the \solver{} can fully support the incremental changes made to the \gls{slv-mod}.
This avoids the overhead of re-initialisation and allows the solver to retain all search information. This avoids the overhead of reinitialization and allows the solver to retain all search information.
Otherwise, the \solver{} can still be warm-started. Otherwise, the \solver{} can still be warm-started.
Instead of starting the search without any information, the \solver{} is given information about the previous \gls{sol} to speed up its search. Instead of starting the search without any information, the \solver{} is given information about the previous \gls{sol} to speed up its search.