Graph coloring algorithms for fast evaluation of Curtis decompositions
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
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Efficient function representation is very important for speed and memory requirements of multiple-valued decomposers. This paper presents a new representation of multiple-valued relations (functions in particular), called multiple-valued cube diagram bundles (MVCDB). MVCDBs improve on rough partition representation by labeling their blocks with variable values and by representing blocks efficiently. The MVCDB representation is especially efficient for very strongly unspecified multiple-valued input, multiple-valued output functions and relations, typical for machine learning applications.