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Finding minimal perfect hash functions

Authors: Gary Haggard; Kevin Karplus;

Finding minimal perfect hash functions

Abstract

A heuristic is given for finding minimal perfect hash functions without extensive searching. The procedure is to construct a set of graph (or hypergraph) models for the dictionary, then choose one of the models for use in constructing the minimal perfect hashing function. The construction of this function relies on a backtracking algorithm for numbering the vertices of the graph. Careful selection of the graph model limits the time spent searching. Good results have been obtained for dictionaries of up to 181 words. Using the same techniques, non-minimal perfect has functions have been found for sets of up to 667 words.

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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!
7
Average
Top 10%
Average
bronze