Dynamic perfect hashing: upper and lower bounds
The dynamic dictionary problem is considered: provide an algorithm for storing a dynamic set, allowing the operations insert, delete, and lookup. A dynamic perfect hashing strategy is given: a randomized algorithm for the dynamic dictionary problem that takes $O(1)$ worst-case time for lookups and $...
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Veröffentlicht in: | SIAM journal on computing 1994-08, Vol.23 (4), p.738-761 |
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creator | DIETZFELBINGER, M KARLIN, A MEHLHORN, K MEYER AUF DER HEIDE, F ROHNERT, H TARJAN, R. E |
description | The dynamic dictionary problem is considered: provide an algorithm for storing a dynamic set, allowing the operations insert, delete, and lookup. A dynamic perfect hashing strategy is given: a randomized algorithm for the dynamic dictionary problem that takes $O(1)$ worst-case time for lookups and $O(1)$ amortized expected time for insertions and deletions; it uses space proportional to the size of the set stored. Furthermore, lower bounds for the time complexity of a class of deterministic algorithms for the dictionary problem are proved. This class encompasses realistic hashing-based schemes that use linear space. Such algorithms have amortized worst-case time complexity $\Omega (\log n)$ for a sequence of n insertions and lookups; if the worst-case lookup time is restricted to $k$, then the lower bound becomes $\Omega (k \cdot n^{{1 / k}} )$. |
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Such algorithms have amortized worst-case time complexity $\Omega (\log n)$ for a sequence of n insertions and lookups; if the worst-case lookup time is restricted to $k$, then the lower bound becomes $\Omega (k \cdot n^{{1 / k}} )$.</description><identifier>ISSN: 0097-5397</identifier><identifier>EISSN: 1095-7111</identifier><identifier>DOI: 10.1137/s0097539791194094</identifier><language>eng</language><publisher>Philadelphia, PA: Society for Industrial and Applied Mathematics</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Algorithms ; Applied sciences ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Dictionaries ; Exact sciences and technology ; Grants ; Memory organisation. 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Such algorithms have amortized worst-case time complexity $\Omega (\log n)$ for a sequence of n insertions and lookups; if the worst-case lookup time is restricted to $k$, then the lower bound becomes $\Omega (k \cdot n^{{1 / k}} )$.</abstract><cop>Philadelphia, PA</cop><pub>Society for Industrial and Applied Mathematics</pub><doi>10.1137/s0097539791194094</doi><tpages>24</tpages></addata></record> |
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subjects | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science control theory systems Data processing. List processing. Character string processing Dictionaries Exact sciences and technology Grants Memory organisation. Data processing Software Theoretical computing |
title | Dynamic perfect hashing: upper and lower bounds |
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