Abstract
In this paper, we analyze hashing from a worst-case perspective. To this end, we study a new property of hash families that is strongly related to d-perfect hashing, namely c-ideality. On the one hand, this notion generalizes the definition of perfect hashing, which has been studied extensively; on the other hand, it provides a direct link to the notion of c-approximativity. We focus on the usually neglected case where the average load \(\alpha \) is at least 1 and prove upper and lower parametrized bounds on the minimal size of c-ideal hash families.
As an aside, we show how c-ideality helps to analyze the advice complexity of hashing. The concept of advice, introduced a decade ago, lets us measure the information content of an online problem. We prove hashing’s advice complexity to be linear in the hash table size.
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Notes
- 1.
Note that the number of hash functions, \(|\mathcal {H}_\text {all}|=m^u\), is huge.
- 2.
This number can be expressed as \(\sum _{k=1}^m\left( {\begin{array}{c}\alpha _k\\ 2\end{array}}\right) \in \varTheta \left( \alpha _1^2+\ldots +\alpha _m^2\right) \subseteq \mathcal {O}\left( m\cdot \alpha _\text {max}^2\right) \).
- 3.
Note that, for any \(h_\textrm{eq}\), there are exactly (\(u\!\!\mod m\)) cells of size \(\lceil {u/m} \rceil \) since \(\sum _{k=1}^m |h_\textrm{eq}^{-1}(k)| = u\).
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Frei, F., Wehner, D. (2023). Bounds for c-Ideal Hashing. In: Fernau, H., Jansen, K. (eds) Fundamentals of Computation Theory. FCT 2023. Lecture Notes in Computer Science, vol 14292. Springer, Cham. https://doi.org/10.1007/978-3-031-43587-4_15
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