The Complexity of Implicit and Space Efficient Priority Queues

Mortensen, Christian Worm; Pettie, Seth

doi:10.1007/11534273_6

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…It turns out to be cheaper to maintain a single partitioning element than to maintain many twin relationships as done in the correspondence-based approaches. When developing this transformation we were inspired by the priority queue described in [22], where a related partitioning scheme is used. The way we implement partitioning allows efficient deamortization; in accordance our bounds are worst-case rather than amortized in contrast to the bounds derived in [22].…”

Section: Our Resultsmentioning

confidence: 99%

“…When developing this transformation we were inspired by the priority queue described in [22], where a related partitioning scheme is used. The way we implement partitioning allows efficient deamortization; in accordance our bounds are worst-case rather than amortized in contrast to the bounds derived in [22]. Our second transformation combines the total correspondence approach with an efficient priority queue supporting decrease.…”

Section: Our Resultsmentioning

confidence: 99%

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Two new methods for constructing double-ended priority queues from priority queues

2008

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We introduce two data-structural transformations to construct doubleended priority queues from priority queues. To apply our transformations the priority queues exploited must support the extraction of an unspecified element, in addition to the standard priority-queue operations. With the first transformation we obtain a double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, insert, extract; and the worst-case cost of O(lg n) with at most lg n + O(1) element comparisons for delete. With the second transformation we get a meldable double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, insert, extract; the worst-case cost of O(lg n) with at most lg n + O(lg lg n) element comparisons for delete; and the worst-case cost of O(min {lg m, lg n}) for meld. Here, m and n denote the number of elements stored in the data structures prior to the operation in question.

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Section: Our Resultsmentioning

confidence: 99%

Section: Our Resultsmentioning

confidence: 99%

Two new methods for constructing double-ended priority queues from priority queues

2008

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“…Implicit dictionaries have been the topic of several papers culminating in a dictionary supporting all operations in O(log n) time [5]. For a more extensive overview see [8].…”

Section: Introductionmentioning

confidence: 99%

A Cache-Oblivious Implicit Dictionary with the Working Set Property

Brodal

Kejlberg-Rasmussen

Truelsen

2010

Algorithms and Computation

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Abstract. In this paper we present an implicit dictionary with the working set property i.e. a dictionary supporting insert(e), delete(x) and predecessor(x) in O(log n) time and search(x) in O(log ) time, where n is the number of elements stored in the dictionary and is the number of distinct elements searched for since the element with key x was last searched for. The dictionary stores the elements in an array of size n using no additional space. In the cache-oblivious model the operations insert(e), delete(x) and predecessor(x) cause O(log B n) cache-misses and search(x) causes O(log B ) cache-misses.

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