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Optimisation Techniques for Combining Constraint Solvers

Authors: Kepser, Stephan; Richts, Jörn;

Optimisation Techniques for Combining Constraint Solvers

Abstract

In recent years, techniques that had been developed for the combination of unification algorithms for equational theories were extended to combining constraint solvers. These techniques inherited an old deficit that was already present in the ombination of equational theories which makes them rather unsuitable for pratical use: The underlying combination algorithms are highly non-deterministic. This paper is concerned with the pratical problem of how to optimise the combination method of Baader and Schulz. We present two optimisation methods,called the iterative and the deductive method. The iterative method reorders and localises the non-deterministic decisions. The deductive method uses specific algorithms for the components to reach certain decisions deterministically. Run time tests of our implementation indicate that the optimised combination method yields combined decision procedures that are efficient enough to be used in practice.

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Germany
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Keywords

ddc:004, Optimisation Techniques, combination method, iterative method, Optimierungstechniken, Kombinationsverfahren, iterative Verfahren, info:eu-repo/classification/ddc/004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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