Not so mighty now
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@ -223,7 +223,7 @@
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\newglossaryentry{del-rew}{
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name={delayed rewriting},
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description={A technique using during \gls{rewriting}. It delays the \gls{rewriting} of \glspl{constraint} whose \gls{decomp} might change based on information that can become available later during the process.},
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description={A technique using during \gls{rewriting}. It delays the \gls{rewriting} of \glspl{constraint} whose \gls{decomp} can change based on information that can become available later during the process.},
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}
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\newglossaryentry{domain}{
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@ -511,7 +511,7 @@
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description={
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\Glspl{solver} generally make search decisions when they can not directly infer a \gls{sol}.
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The \gls{solver} then assumes a value for a \gls{variable} or adds a \gls{constraint}.
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The decision might have to be revised later.
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The decision can be to be revised later.
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}
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}
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@ -319,7 +319,7 @@ The full set of rules appears in \cref{fig:rew-rewrite-to-nzn-calls,fig:rew-rewr
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To simplify presentation, we assume that all rules that introduce new identifiers into the \nanozinc{} program do so in a way that ensures those identifiers are fresh (i.e., not used in the rest of the \nanozinc{} program), by suitable alpha renaming.
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We also present our rules using a \emph{call by name} evaluation strategy, i.e., the arguments to function calls are substituted into function body.
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As such, they are only evaluated when required for the result of the function.
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Although pure \microzinc{} behaves the same under any call evaluation strategy, in implementation a \emph{call by value} strategy might provide more predictable behaviour with debugging functions in the \minizinc{} language, such as \mzninline{trace} and \mzninline{assert}.
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Although pure \microzinc{} behaves the same under any call evaluation strategy, in implementation a \emph{call by value} strategy can provide more predictable behaviour with debugging functions in the \minizinc{} language, such as \mzninline{trace} and \mzninline{assert}.
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\begin{figure*}[p]
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\centering
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@ -141,7 +141,7 @@ It is, however, still a significant improvement over repeatedly \gls{rewriting}
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The improvements offered by these new methods may spark future research.
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Primarily, they will allow and promote using \gls{meta-optimization} algorithms in \cmls{} for new problems.
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It could even be worthwhile to revisit existing applications of incremental constraint modelling.
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The utilization of the presented methods might offer a significant improvement in performance, allowing the solving of more complex problems.
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The utilization of the presented methods can offer a significant improvement in performance, allowing the solving of more complex problems.
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Finally, new \gls{meta-optimization} techniques could require extensions of the methods presented.
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\section*{Summary}
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