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Grammar check of the Abstract

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Jip J. Dekker 2021-07-24 13:50:08 +10:00
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chapters/0_abstract.tex
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@ -12,7 +12,7 @@ However, \cmls{} have evolved to include functionality that is no longer directl
As such, the \gls{rewriting} process has become more important and complex.
\minizinc{}, one such language, was originally designed for constraint programming \solvers{}, whose \glspl{slv-mod} contain small number of highly complex \constraints{}.
The same \minizinc{} models can now target mixed integer programming and Boolean satisfiability \solvers{}, resulting is a large number of very simple \constraints{}.
The same \minizinc{} models can now target mixed integer programming and Boolean satisfiability \solvers{}, resulting in numerous very simple \constraints{}.
Distinctively, the \minizinc{}'s \gls{rewriting} process is founded on its functional language.
It generates \glspl{slv-mod} through the application of increasingly complex \minizinc{} functions from \solver{}-specific libraries.
Consequently, the efficiency of the functional evaluation of the language can be a limiting factor.
@ -29,6 +29,6 @@ In addition, we incorporate new analysis techniques to avoid the use of \glspl{r
Crucially, the architecture is designed to incorporate incremental \constraint{} modelling in two ways.
Primarily, the \gls{rewriting} process is fully incremental: changes made to the \instance{} through a provided interface require minimal addition \gls{rewriting} effort.
Moreover, we introduce \gls{rbmo}, a way to specify \gls{meta-optimization} algorithms directly in \minizinc{}.
These specification are executed by a normal \minizinc{} \solver{}, requiring only a slight extension of its capabilities.
These specifications are executed by a normal \minizinc{} \solver{}, requiring only a slight extension of its capabilities.
Together, the functionality of this architecture helps make \cmls{} a more powerful and attractive approach to solve real world problems.